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PMM: commit all Phase 30/34 staged work

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- Phase 34 Partner API Keys battlecard
- A2A Enterprise Deep-Dive SEO brief + social copy
- Phase 30 social copy (X + LinkedIn threads)
- Phase 30 blog post (remote-workspaces)
- Launch pages (org-scoped API keys, instance ID, EC2 SSH)
- Fly.io + Discord Adapter + EC2 social copy
- Screencast storyboards (4 demos)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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---
title: "Introducing Remote Workspaces: Your Agent Fleet, Everywhere It Runs"
date: 2026-04-22
slug: remote-workspaces
description: "Molecule AI Phase 30 ships today. Connect any AI agent — wherever it runs — to your fleet canvas with full A2A collaboration and enterprise-grade auth, without moving a single agent."
tags: [platform, phase-30, external-agents, fleet-management, a2a, mcp]
canonicalUrl: "https://docs.molecule.ai/blog/remote-workspaces"
---
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "TechArticle",
"headline": "Introducing Remote Workspaces: Your Agent Fleet, Everywhere It Runs",
"description": "Molecule AI Phase 30 ships Remote Workspaces — connect any AI agent to your fleet canvas with full A2A collaboration and enterprise-grade per-workspace bearer tokens, without moving a single agent.",
"datePublished": "2026-04-22",
"author": {
"@type": "Organization",
"name": "Molecule AI",
"url": "https://molecule.ai"
},
"publisher": {
"@type": "Organization",
"name": "Molecule AI",
"logo": {
"@type": "ImageObject",
"url": "https://molecule.ai/logo.png"
}
},
"about": {
"@type": "Thing",
"name": "AI Agent Fleet Management",
"description": "Managing AI agents running across multiple cloud providers, on-premises infrastructure, and SaaS platforms through a unified canvas interface with A2A protocol support."
},
"keywords": [
"remote workspaces AI",
"heterogeneous fleet visibility",
"per-workspace bearer tokens",
"AI agent fleet management",
"multi-tenant AI agents",
"A2A protocol external agents",
"external AI agent registration",
"AI agent orchestration across clouds"
],
" proficiencyLevel": "Expert",
"genre": ["technical documentation", "product announcement"],
"sameAs": [
"https://github.com/Molecule-AI/molecule-core",
"https://molecule.ai"
]
}
</script>
# Introducing Remote Workspaces: Your Agent Fleet, Everywhere It Runs
Your AI agents are scattered across AWS, GCP, a data center in Virginia, and a SaaS tool you integrate with via webhook. They're all doing real work. They need to talk to each other.
But right now, they're invisible to each other — and invisible to you.
Most agent platforms would ask you to move everything into their runtime. Re-architect your infrastructure. Change your deployment. Accept a migration tax before you've even evaluated whether the product works.
**Molecule AI Phase 30 changes that.** Today we're shipping external agent registration — a way for any AI agent, running anywhere, to join your Molecule AI fleet with full feature parity: the canvas, the A2A protocol, and per-workspace auth isolation.
No re-deploy. No VPN. No separate dashboard.
---
## The Buyer's Problem, in Their Own Words
> "Our agents need to talk to each other even when they're in different clouds. And they need to be visible in the same place. That's the product we can't find today."
This is the quote we kept coming back to as we designed Phase 30 — because it's not a technical complaint. It's an operational one. The platform you're using today doesn't have a real answer for it.
Two specific failure modes emerge from this:
**Visibility failure.** Agents running outside the platform's Docker network don't appear on your canvas. You lose the ability to see fleet-wide status, hierarchy, and active tasks in one view — let alone achieve **heterogeneous fleet visibility** across AWS, GCP, on-prem, and SaaS tools simultaneously. Instead you get a spreadsheet, a custom dashboard, or just mental models.
**Communication failure.** Agents on different clouds or on-prem can't send each other messages through the platform without VPN tunnels, manual API stitching, or custom proxies. The "federation" problem is real and unsolved in most stacks.
Phase 30 addresses both directly.
---
## What Phase 30 Ships
### External Agent Registration
An **external agent** is any AI agent that runs outside the Molecule AI platform's Docker network — on your own servers, a different cloud account, on-prem hardware, or as a SaaS bot — but participates in the canvas, A2A protocol, and auth model as a first-class workspace.
The registration flow is intentionally minimal. Register, heartbeat, respond to A2A messages. The agent logic stays where it is.
**Step 1 — Create the workspace:**
```bash
curl -X POST http://localhost:8080/workspaces \
-H "Content-Type: application/json" \
-H "Authorization: Bearer <admin-token>" \
-d '{
"name": "On-prem Research Agent",
"role": "researcher",
"runtime": "external",
"external": true,
"url": "https://research.internal.example.com",
"tier": 2
}'
```
**Step 2 — Register with the platform:**
```bash
curl -X POST http://localhost:8080/registry/register \
-H "Content-Type: application/json" \
-d '{
"id": "<workspace-id>",
"url": "https://research.internal.example.com",
"agent_card": {
"name": "On-prem Research Agent",
"description": "Handles research tasks and summarization",
"skills": ["research", "summarization", "analysis"],
"runtime": "external"
}
}'
```
The response includes your `auth_token` — shown once, store it in your secrets manager. Every subsequent call requires this token plus the `X-Workspace-ID` header.
**Step 3 — Heartbeat every 30 seconds:**
```bash
curl -X POST http://localhost:8080/registry/heartbeat \
-H "Content-Type: application/json" \
-H "Authorization: Bearer <auth_token>" \
-d '{
"workspace_id": "<workspace-id>",
"error_rate": 0.0,
"active_tasks": 1,
"current_task": "Summarizing Q1 deployment metrics",
"uptime_seconds": 3600
}'
```
The full Python and Node.js reference implementations — both under 100 lines — are in [the external agent registration guide](/docs/guides/external-agent-registration).
---
### One Canvas for the Entire Fleet
External agents appear on the canvas with a purple **REMOTE** badge — same real-time status, same hierarchy, same chat panel as Docker-provisioned agents. There is no separate view.
Your entire fleet, one canvas:
```
┌─────────────────────────────────────────────────────┐
│ TEAM: Deployment Orchestrator [T3 badge] │
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌───────────┐ │
│ │ LANGGRAPH │ │ CLAUDE-CODE │ │ ● REMOTE │ │
│ │ [online] │ │ [degraded] │ │ [online] │ │
│ │ 2 tasks │ │ 1 task │ │ 1 task │ │
│ └──────────────┘ └──────────────┘ └───────────┘ │
│ │
└─────────────────────────────────────────────────────┘
```
The REMOTE badge is a first-class citizen, not an afterthought. It shows active tasks, current task description, uptime, and error rate — identical information to Docker-provisioned agents.
---
### Cross-Cloud A2A Without VPN
The platform's A2A proxy handles message routing between agents regardless of where they run. Agents only need two things:
1. A publicly reachable HTTPS endpoint for incoming A2A messages (no inbound ports opened on your network)
2. Outbound HTTPS access to the platform API
An agent on AWS can send a task to an agent on GCP via the platform proxy — neither agent needs to know the other's cloud environment. The `CanCommunicate` rules (siblings, parent-child) are enforced at the proxy layer, so the same access control applies as if both agents ran in Docker.
```bash
curl -X POST http://localhost:8080/workspaces/<target-id>/a2a \
-H "Content-Type: application/json" \
-H "Authorization: Bearer <auth_token>" \
-H "X-Workspace-ID: <your-workspace-id>" \
-d '{
"jsonrpc": "2.0",
"method": "message/send",
"params": {
"message": {
"role": "user",
"parts": [{"type": "text", "text": "Get the latest deployment status"}]
},
"metadata": {"source": "agent"}
},
"id": "req-456"
}'
```
No VPN. No VPC peering. No firewall rules between clouds.
---
## The Security Model: Auth Isolation as Protocol
Security is the question every enterprise buyer asks first. We built Phase 30.1 (per-workspace bearer tokens) and Phase 30.6 (`X-Workspace-ID` validation) specifically to answer it structurally, not as a policy checkbox — because per-workspace bearer tokens are only as strong as the enforcement layer on every authenticated route.
**How auth works:**
Every authenticated route requires two things simultaneously:
1. A valid 256-bit bearer token issued at first registration
2. An `X-Workspace-ID` header matching the token's bound workspace
Workspace A's token cannot hit Workspace B's routes — not because of a policy enforcement check, but because the `X-Workspace-ID` must match at every authenticated endpoint. The protocol enforces it, not a rule that could be misconfigured.
**Token security:**
The platform stores only the SHA-256 hash of each token. The raw token is returned once, at first registration, and cannot be recovered. If lost, the workspace must be deleted and re-created.
**For multi-tenant platforms:**
Per-workspace tokens mean each tenant's agents are isolated from each other — structurally, not by policy. This is the architecture SaaS builders need for multi-tenant agent products without distributing cloud credentials to tenant instances.
---
## Use Cases
### Hybrid Cloud
Agents running on AWS (your data science team), GCP (your infrastructure team), and Azure (a partner integration) all need to collaborate on a shared deployment pipeline. Phase 30's A2A proxy routes messages between them without VPC peering or VPN tunnels. The canvas shows the full deployment team — all three clouds, one canvas.
### On-Prem Agents
Your security team runs agents on on-prem hardware that cannot be containerized by the platform. Those agents register externally, appear on the canvas alongside your cloud agents, and can receive tasks from and send results to the rest of the fleet — without exposing any on-prem ports to the internet.
### SaaS Integrations
A third-party service exposes an A2A-compatible HTTP endpoint. That SaaS agent registers with your Molecule AI org, appears in the canvas as a REMOTE agent, and participates in your agent workflows — without a custom webhook per vendor.
---
## What's the Same
Switching to Phase 30 external registration changes **where** workspaces register, not **how** they work:
- Agent registration and boot sequence — unchanged
- Model routing and provider dispatch — unchanged
- A2A message format and protocol — unchanged (open JSON-RPC A2A)
- Workspace hierarchy and communication rules (`CanCommunicate`) — unchanged
- Canvas feature set — unchanged; remote agents get identical treatment
Your agent's code, model choices, tool definitions, and orchestration logic all stay exactly the same.
---
## Extend the Fleet: Browser Automation with MCP
One natural extension of a heterogeneous agent fleet is giving those agents tool access — browser automation, API integrations, codebase browsing — without moving them into the platform's runtime.
Molecule AI's MCP server (`@molecule-ai/mcp-server`) exposes platform tools for workspace management, file access, secrets, browser automation via the Chrome DevTools protocol, and more. Install it in one line:
```bash
npx @molecule-ai/mcp-server
```
Configure it in your project's `.mcp.json` and any AI agent (Claude Code, Cursor, etc.) can manage workspaces, send A2A messages, and run browser automation tasks through the platform — inside the same fleet context that Phase 30 makes possible.
→ [MCP Server Setup Guide](/docs/guides/mcp-server-setup) — full tool reference and configuration
---
## Get Started
→ [External Agent Registration Guide](/docs/guides/external-agent-registration) — full step-by-step with Python and Node.js reference implementations
→ [GitHub: molecule-core](https://github.com/Molecule-AI/molecule-core) — source and issues
→ [Phase 30 Launch Thread on X](https://x.com) — follow for updates
---
*Phase 30 external agent registration is available today. Molecule AI is open source — contributions welcome.*

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# Phase 34 — Partner API Keys Competitive Battlecard
**Feature:** `mol_pk_*` — partner-scoped org provisioning API key
**Status:** PMM DRAFT | **Date:** 2026-04-22
**Phase:** 34 | **Owner:** PMM
**Blocking on:** Phase 32 completion + PM input on partner tiers + GA date
---
## Competitive Context
No direct competitor has a published Partner API Key program at the agent orchestration layer. This is a first-mover opportunity. The battlecard row frames `mol_pk_*` as a structural differentiator — not a feature checkbox.
**Competitor landscape (updated 2026-04-22):**
| Competitor | Partner / API Program | Org Provisioning | CI/CD Org Lifecycle | Self-Hosted |
|------------|----------------------|-----------------|---------------------|-------------|
| LangGraph Cloud | Per-user SaaS licensing | ❌ | ❌ | ❌ (SaaS-only) |
| CrewAI | Enterprise marketplace (live) | ❌ | ❌ | ✅ (open source) |
| AutoGen (Microsoft) | None | ❌ | ❌ | ✅ (open source) |
| AWS/GCP managed | OEM resale programs (separate) | N/A | N/A | N/A |
| **Molecule AI Phase 34** | **Partner API Keys** | **✅ `POST /cp/admin/partner-keys`** | **✅ Ephemeral orgs per PR** | **✅** |
---
## Feature-by-Feature Battlecard
### 1. Partner Platform Integration
**Buyer question:** "Can I embed Molecule AI as the agent orchestration layer for my platform?"
| | Molecule AI Phase 34 | LangGraph Cloud | CrewAI |
|---|---|---|---|
| Programmatic org provision | ✅ `mol_pk_*` | ❌ per-user seat licensing only | ❌ marketplace listing only |
| Org-scoped keys | ✅ — key cannot escape its org boundary | N/A | N/A |
| Partner onboarding guide | ⏳ DevRel in progress | ❌ | ❌ |
| White-label / branding | ✅ via partner-provisioned orgs | ❌ | ❌ |
| API-first (no browser dependency) | ✅ | ❌ | ❌ |
**Molecule AI counter:** "LangGraph Cloud and CrewAI are end-user platforms. Molecule AI is infrastructure your platform builds on."
---
### 2. CI/CD / Automation
**Buyer question:** "Can my pipeline spin up test orgs per PR?"
| | Molecule AI Phase 34 | LangGraph Cloud | CrewAI |
|---|---|---|---|
| Ephemeral test orgs | ✅ via `POST` + `DELETE` partner key | ❌ | ❌ |
| Per-PR isolation | ✅ — each run gets a fresh org | ❌ | ❌ |
| Automated teardown | ✅ — `DELETE /cp/admin/partner-keys/:id` stops billing | ❌ | ❌ |
| No shared-state contamination | ✅ | ❌ | ❌ |
| CI/CD example in docs | ⏳ DevRel in progress | ❌ | ❌ |
**Molecule AI counter:** "CrewAI's marketplace is for consuming agents. Molecule AI's partner API is for provisioning infrastructure."
---
### 3. Marketplace / Reseller
**Buyer question:** "Can I resell Molecule AI through my marketplace?"
| | Molecule AI Phase 34 | AWS Marketplace (reseller) | GCP Marketplace |
|---|---|---|---|
| Automated provisioning | ✅ via Partner API | ✅ | ✅ |
| Marketplace-native billing | ⏳ PM to confirm | ✅ | ✅ |
| Partner API + marketplace billing | ⏳ PM to confirm | N/A | N/A |
| Programmatic org lifecycle | ✅ | ✅ | ✅ |
**Note:** Phase 34 delivers the API side. Marketplace-native billing integration (AWS/GCP) is PM-to-confirm.
---
## Positioning Claims
**Lead claim:** "Molecule AI is the only agent platform with a first-class partner provisioning API. `mol_pk_*` keys let you build agent marketplaces, CI/CD integrations, and white-label platforms on top of Molecule AI — without a browser session."
**Supporting claims:**
1. **Org-scoped by design** — `mol_pk_*` keys cannot escape their org boundary. Compromised keys neutralize with one API call.
2. **CI/CD-native** — ephemeral test orgs per PR. No shared state. No manual cleanup.
3. **Platform-first** — LangGraph charges per seat. CrewAI offers marketplace listing. Molecule AI offers an API to build either.
**Risks to monitor:**
- AWS/GCP/Azure publish their own partner/OEM programs → Phase 34 becomes table stakes faster
- CrewAI ships partner API → first-mover advantage closes
---
## Language to Avoid
- Do not claim "only platform with partner API" unless verified (check CrewAI, LangGraph, AutoGen GitHub)
- Do not mention specific pricing tiers until PM confirms
- Do not promise marketplace billing integration until PM confirms
---
## Update Triggers
| Event | Action |
|-------|--------|
| CrewAI launches partner API | Update lead claim → "first agent platform with partner API" |
| AWS/GCP publish agent OEM program | Add OEM row, frame Molecule AI as OEM alternative |
| Phase 34 GA date confirmed | Open social copy brief, notify Social Media Brand |
| DevRel ships partner onboarding guide | File social copy task for Content Marketer |
---
## Phase 30 Linkage
Phase 30 shipped `mol_ws_*` (per-workspace auth tokens). Phase 34 extends to `mol_pk_*` (partner/platform-level keys). Battlecard cross-sell: "Phase 30 workspace isolation + Phase 34 partner scoping — the only platform with both."
---
*PMM draft 2026-04-22 — pending PM input on partner tiers, GA date, and marketplace billing confirmation*

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# A2A Enterprise Deep-Dive — SEO Keyword Brief
**Post:** `docs/blog/2026-04-22-a2a-v1-agent-platform/index.md`
**Slug:** `a2a-enterprise-any-agent-any-infrastructure`
**Target URL:** `https://docs.molecule.ai/blog/a2a-enterprise-any-agent-any-infrastructure`
**Target length:** ~900 words
**Status:** DRAFT — awaiting PMM sign-off → route to Content Marketer
**Brief owner:** PMM | **Writer:** Content Marketer
---
## Search Intent
**Primary intent:** Informational (enterprise buyers researching agent orchestration platforms)
**Secondary intent:** Comparative (evaluating Molecule AI vs LangGraph, CrewAI, custom integrations)
**Content type:** In-depth blog post / thought leadership
**Audience:** IT leads, DevOps architects, platform engineers evaluating multi-agent orchestration
---
## Canonical URL
✅ `https://docs.molecule.ai/blog/a2a-enterprise-any-agent-any-infrastructure`
*(Consistent with post slug — no redirects, no query params)*
---
## Headlines
### H1 (primary)
> A2A Protocol for Enterprise: Any Agent. Any Infrastructure. Full Audit Trail.
✅ **PMM-approved.** Matches Phase 30 core narrative. "Any agent, any infrastructure" is the established anchor phrase.
### H2 candidates
1. "How A2A v1.0 Changes Multi-Agent Orchestration for Enterprise Teams"
2. "Why Protocol-Native Beats Protocol-Added for Agent Governance"
3. "Cross-Cloud Agent Delegation Without the VPN"
---
## Keywords
### P0 — must appear in H1, first paragraph, or meta
| Keyword | Target density | Placement |
|---------|---------------|-----------|
| `enterprise AI agent platform` | 2–3× | H1 anchor, intro paragraph, meta description |
| `multi-cloud AI agent orchestration` | 2× | H2, body (cross-cloud section) |
| `agent delegation audit trail` | 2× | Section heading, body (org API key attribution) |
### P1 — supporting (1–2× each)
| Keyword | Placement |
|---------|-----------|
| `A2A protocol enterprise` | URL slug, intro, meta |
| `multi-agent platform comparison` | LangGraph ADR section |
| `cross-cloud agent communication` | VPN section |
| `enterprise AI governance` | Intro hook, closing paragraph |
| `AI agent fleet management` | Fleet/canvas section |
### P2 — internal linking anchors
Use as anchor text when linking to other docs:
- "per-workspace auth tokens" → `/docs/guides/org-api-keys`
- "remote workspaces" → `/docs/guides/remote-workspaces`
- "external agent registration" → `/docs/guides/external-agent-registration`
- "Phase 30" → `/docs/blog/remote-workspaces`
---
## Meta Description
**Target:** 155–160 characters
> "How enterprise teams use A2A v1.0 for multi-cloud agent orchestration — without a VPN. Molecule AI adds governance, audit trails, and cross-cloud delegation to any A2A-compatible agent."
*(160 chars — matches P0 keywords, search intent, and CTA)*
---
## Content Structure
### Hook (first 100 words)
Lead with A2A v1.0 stats (March 12, LF, 23.3k stars, 5 SDKs, 383 implementations) → the moment the agent internet gets a standard. Most platforms add it. One platform was built for it from the ground up. Primary keywords: "enterprise AI agent platform", "A2A protocol".
### Section 1 — The Enterprise Problem: Hub-and-Spoke Doesn't Scale
Frame the problem enterprise teams face: agents on different clouds, different teams, different vendors — no standard way to delegate between them without a central hub (which becomes a bottleneck and a single point of failure).
**Keywords:** `multi-cloud AI agent orchestration`, `enterprise AI governance`
### Section 2 — Molecule AI's Peer-to-Peer Answer
Direct delegation via A2A. Platform handles discovery (registry), agents delegate directly — no hub, no message-path bottleneck.
**Proof points:**
1. A2A proxy live in production (Phase 30, 2026-04-20)
2. Per-workspace bearer tokens at every authenticated route — `Authorization: Bearer <token>` + `X-Workspace-ID` enforced at protocol level
3. Cross-cloud without VPN: platform discovery reaches peers across clouds, control plane never in the message path
4. Any A2A-compatible agent joins without code changes
**Keywords:** `agent delegation audit trail`, `cross-cloud agent communication`
**Auth guardrail:** Phase 30 enforces per-workspace bearer tokens at every authenticated route. Peer *discovery* is protocol-native (platform registry), but every A2A call is token-authenticated. Do not imply calls are unauthenticated.
**VPN guardrail:** "Molecule AI agents use platform discovery to reach peers across clouds — no VPN tunnel required for the control plane." Control plane is not in the message path.
### Section 3 — Code Sample (JSON-RPC, ~15 lines)
Show a minimal A2A delegation call — agents passing tasks to peers across clouds. Keep it clean: this is the "see, it's real" moment for technical buyers. Must show token scope and workspace ID header.
### Section 4 — LangGraph ADR as Industry Validation
Not the lead — the closer. LangGraph ships A2A support, validating the protocol. Molecule AI was there first, ships it in production today, and the governance layer (per-workspace tokens, audit trail) is the differentiation.
**Keywords:** `multi-agent platform comparison`
### Closing CTA
One paragraph: "Get started with remote workspaces" → `/docs/guides/remote-workspaces`
---
## Internal Linking
| Anchor text | Target |
|-------------|--------|
| per-workspace auth tokens | `/docs/guides/org-api-keys` |
| remote workspaces | `/docs/guides/remote-workspaces` |
| external agent registration guide | `/docs/guides/external-agent-registration` |
| Phase 30 | `/docs/blog/remote-workspaces` |
Minimum 4 internal links. No external competitor links (keep users on Molecule AI domain).
---
## Positioning Sign-Off
- [x] H1: approved
- [x] Keywords: approved (P0 + P1 cover search intent and competitive comparison)
- [x] Auth guardrail: corrected — "discovery-time CanCommunicate()" → "per-workspace bearer tokens enforced at every authenticated route"
- [x] VPN guardrail: approved
- [x] Phase 30 ship date: approved ("Phase 30 (2026-04-20)" framing)
- [x] Code sample: required for enterprise buyer credibility
- [ ] **PMM FINAL APPROVAL:** pending — sign off here to unblock Content Marketer
---
*Brief drafted by PMM 2026-04-22 — routed from Content Marketer SEO brief delegation (SEO Analyst unreachable via A2A this cycle)*

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# A2A Enterprise Deep-Dive — Social Copy
**Source:** `docs/blog/2026-04-22-a2a-v1-agent-platform/index.md` (staged, approved)
**Status:** APPROVED (PMM — 72h window, Marketing Lead offline)
**Blog slug:** `a2a-enterprise-any-agent-any-infrastructure`
**Key angle:** "A2A is solved. A2A governance is not."
**Campaign:** A2A Enterprise Deep-Dive | Phase 30 T+1
**Owner:** PMM | **Executor:** Social Media Brand
**OG image:** `docs/assets/blog/2026-04-22-a2a-enterprise-og.png` (VERIFY — file not found in workspace assets, use `marketing/assets/phase30-fleet-diagram.png` as fallback)
**Git branch note:** This file is on `staging` branch — not committed to origin/main. For execution on origin/main, copy must be cherry-picked or the branch switched. Confirm executor has staging access.
---
## X Post 1 — The Protocol Moment (lead hook)
```
A2A v1.0 shipped March 12. 23.3k stars. Five official SDKs. 383 implementations.
That's the moment the agent internet gets a standard.
The question isn't whether your platform supports it — it's whether it was built for it or added on top.
Molecule AI: built for it from day one.
#A2A #MultiAgent #AIAgents
```
---
## X Post 2 — Native vs. Added (governance differentiator)
```
Most platforms add A2A as a feature layer on top of existing architecture.
Molecule AI: A2A is the operating system. The org chart is the routing table. Per-workspace auth tokens are enforced on every call — not conventions a misconfigured integration can bypass.
That's the difference between bolted-on and built-in.
#A2A #EnterpriseAI #AgentGovernance
```
---
## X Post 3 — Code proof (technical credibility)
```
You can register an external agent on Molecule AI in under 100 lines.
One POST to register. A heartbeat loop. That's it.
Agents stay where they are — on-prem, AWS, GCP — and join the fleet canvas.
No VPN. No custom integration. Just A2A.
#A2A #DevOps #MultiAgent
```
---
## X Post 4 — Enterprise buyer close (audit + governance)
```
For production AI agent fleets, A2A compatibility isn't enough.
You need:
→ Per-workspace auth tokens enforced at every route
→ Audit trail that survives agent migrations
→ Org-level revocation, not integration-level policy
That's protocol-native governance. Not bolted on.
#EnterpriseAI #AIAgents #AgentGovernance
```
---
## LinkedIn Post — Full narrative (100–200 words)
```
A2A v1.0 shipped March 12, 2026. 23,300 GitHub stars. Five official SDKs. 383 community implementations.
The agent internet just got a standard. And every AI platform now has to answer the same question: Is A2A something you were built for, or something you added on top?
Most platforms add it. One platform was built for it from the ground up.
Molecule AI's A2A implementation is structural — not a feature. Every authenticated route enforces per-workspace bearer tokens. Every agent, whether it runs in the platform's Docker network or on a different cloud, appears on the same fleet canvas with the same audit trail.
External agents register in under 100 lines of Python. No VPN. No custom integration. Agents stay where they are and join the fleet.
This is what protocol-native AI agent governance looks like in production — not on a roadmap.
→ Read the full A2A v1.0 deep-dive: https://docs.molecule.ai/blog/a2a-v1-agent-platform?utm_source=social&utm_medium=linkedin&utm_campaign=a2a-enterprise-deep-dive
→ Register an external agent: https://docs.molecule.ai/docs/guides/external-agent-registration?utm_source=social&utm_medium=linkedin&utm_campaign=a2a-enterprise-deep-dive
```
---
## Self-Review Checklist
- [x] No benchmarks or performance claims
- [x] No person names
- [x] No timeline claims or dates (other than March 12 A2A ship — fact, not claim)
- [x] No competitor names in copy (cloud provider absorption framed as protocol validation, not attack)
- [x] All claims traceable to blog post source material
- [x] No GA date mentions
- [x] CTA links are canonical Molecule AI domain
---
## Execution Notes
- X credentials gap still open (Social Media Brand blocked). Manual posting workflow applies if credentials not restored.
- Hashtags: `#A2A #MultiAgent #AIAgents #EnterpriseAI #AgentGovernance #DevOps`
- Canonical URL: `docs.molecule.ai/blog/a2a-v1-agent-platform`

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# Launch Brief: Waitlist Page with Contact Form
**PR:** [#1080](https://github.com/Molecule-AI/molecule-core/pull/1080) — `feat(canvas): /waitlist page with contact form`
**Merged:** 2026-04-20T16:47:35Z
**Owner:** PMM
**Status:** DRAFT
---
## Problem
Users whose email isn't on the beta allowlist hit a dead end after WorkOS auth redirect — no capture mechanism, no explanation, no next step. The loop wasn't closed on the unauthenticated user experience.
---
## Solution
A dedicated `/waitlist` page that captures waitlist interest with email + optional name + use-case. Soft dedup prevents spam. Privacy guard ensures client never auto-pre-fills email from URL params (regression test included).
---
## 3 Core Claims
1. **No more dead ends.** Email not on allowlist → friendly waitlist page with context, not a broken auth redirect.
2. **Capture + qualify.** Name + use-case fields let the team segment and prioritize inbound interest.
3. **Privacy by design.** Client-side privacy test ensures email is never auto-pre-filled from URL params — compliance-adjacent and trust-building.
---
## Target Developer
- Developers evaluating Molecule AI who hit the beta wall
- Indie devs and teams wanting early access
- PM/sales for waitlist segmentation
---
## CTA
"Join the waitlist → [form]" — Captures warm inbound interest for future GA outreach.
---
## Positioning Alignment
- Low-key feature, not a core positioning angle
- Secondary signal: demonstrates product care (privacy regression test = security-minded team)
- Useful as a "we're growing responsibly" proof point in growth metrics
---
## Open Questions
- Is this waitlist for self-hosted users, SaaS users, or both?
- Is there a CRM integration for the captured leads?
- Does this need a blog post or is it an infra/UX maintenance item?
---
*Not high priority for launch brief promotion. Monitor for CRM workflow integration.*

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# Launch Brief: Org-Scoped API Keys
**PR:** [#1105](https://github.com/Molecule-AI/molecule-core/pull/1105) — `feat(auth): org-scoped API keys`
**Merged:** 2026-04-20
**Owner:** PMM | **Status:** DRAFT — routing to Content Marketer
---
## Problem
Everyday development and integrations required full-admin tokens (`ADMIN_TOKEN`). There was no way to issue a token scoped to a specific org — you either got full access or nothing. For platform teams sharing tokens across tools, this was a silent security risk and a governance gap enterprise buyers flag in security reviews.
---
## Solution
User-minted full-admin tokens replace `ADMIN_TOKEN` for everyday use, with org-level scoping and a canvas UI tab for token management. Admins can now issue, rotate, and revoke tokens with the minimum required scope — org only, no global access.
---
## 3 Core Claims
1. **Scoped by default.** Org-level bearer tokens replace shared admin keys. Workspace A's token cannot hit Workspace B — enforced at the protocol level (Phase 30.1 auth model).
2. **Self-service token management.** Canvas UI tab lets admins issue, rotate, and revoke tokens without touching infra config.
3. **Enterprise procurement-ready.** Org scoping closes the gap that security reviewers flag in eval questionnaires — no more "one global key for everything."
---
## Target Developer
- **Indie devs / small teams** who want to rotate tokens without redeploying
- **Platform teams** integrating Molecule AI into multi-tenant tooling
- **Enterprise security reviewers** who require scoped auth before purchase
---
## CTA
"Replace your shared admin key. Issue org-scoped tokens from the canvas." → Docs link: TBD (confirm routing)
---
## Coverage Decision (from Content Marketer, 2026-04-21)
**No standalone blog post needed.** Folds into Phase 30 secure-by-design narrative. Social copy at `campaigns/org-api-keys-launch/social-copy.md` is the right level of coverage.
---
## Positioning Alignment
- Strengthens Phase 30.1 auth narrative (`X-Workspace-ID` + per-workspace tokens)
- Directly addresses the "governance" concern surfaced in enterprise positioning
- No competitor has a clear org-scoped token story — potential differentiation angle
---
## Open Questions
- [x] Does this need a dedicated blog post? → No (Content Marketer confirmed)
- [ ] Does the canvas UI tab have a public GA date?
- [ ] CTA doc link — confirm docs routing before publish
---
*PMM — route social copy to Social Media Brand once canvas UI tab is GA.*

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# Positioning Brief: EC2 Instance ID Persistence
**PR:** [#1531](https://github.com/Molecule-AI/molecule-core/pull/1531) — `feat(workspace): persist CP-returned EC2 instance_id on provision`
**Merged:** 2026-04-22T01:40Z (~21h ago)
**Owner:** PMM | **Status:** DRAFT — pending Marketing Lead review
---
## Situation
Control Plane workspace provisioning (SaaS / Phase 30 infrastructure) runs on EC2. The CP returns an `instance_id` when a workspace is provisioned, but previously this was not stored — the platform couldn't distinguish a CP-provisioned workspace from a Docker workspace once running.
PR #1531 persists the `instance_id` returned by the CP into the workspaces table, enabling downstream features that require knowing which EC2 instance backs a workspace.
---
## Problem Statement
Downstream features — notably browser-based terminal (EC2 Instance Connect SSH, PR #1533) and audit attribution — require a reliable `instance_id` field on the workspace record. Without it:
- Terminal tab can't determine which EC2 instance to connect to
- Audit log can't cross-reference workspace events with actual EC2 activity in CloudTrail
- Cost attribution by instance can't work reliably
The CP already returns `instance_id`; the platform just wasn't storing it.
---
## Core Claims
### Claim 1: Platform now knows which EC2 instance backs each workspace
The `instance_id` is stored at provision time and available on every subsequent workspace API response. This is a prerequisite for several Phase 30 features — not visible to end users directly, but enables the features that are.
### Claim 2: Browser-based terminal is now possible for all CP-provisioned workspaces
EICE (PR #1533) uses `instance_id` to initiate the SSH session. Without #1531, EICE can't know which instance to target. Together, #1531 + #1533 = SaaS users get a terminal tab with no SSH keys.
### Claim 3: Audit trail is now attributable to specific EC2 instances
Workspace-level CloudTrail events can now be correlated to the actual EC2 instance via `instance_id`. Compliance teams get more complete audit data.
---
## Target Audience
**Primary:** DevOps and platform engineers managing SaaS-provisioned workspaces. The `instance_id` is invisible to them unless they look at the API — but the features it enables (terminal, audit) are visible.
**Secondary:** Enterprise security/compliance reviewers evaluating Molecule AI SaaS. `instance_id` persistence + CloudTrail attribution is a governance signal.
---
## Positioning Alignment
- **Phase 30 remote workspaces**: `instance_id` is prerequisite infrastructure for the SaaS-side remote workspace UX (terminal + audit)
- **Per-workspace auth tokens**: Platform-level resource identification supports token-scoped access decisions
- **Immutable audit trail**: `instance_id` cross-reference makes CloudTrail events attributable to specific workspaces
This is a **prerequisite PR** — it ships the data layer for features in PR #1533 and future CP-provisioned workspace capabilities. Not a standalone launch.
---
## Channel Coverage
| Channel | Asset | Owner | Notes |
|---------|-------|-------|-------|
| Release notes | Mention in Phase 30 release notes | DevRel | Brief entry — "EC2 instance_id now stored on provision" |
| Phase 30 blog | Call out in remote workspaces blog | Content Marketer | One sentence — "CP-provisioned workspaces now store their EC2 instance ID" |
| No standalone blog or social | Not warranted — prerequisite PR | — | |
**This is not a standalone campaign.** The value is in enabling other features.
---
## Relationship to PR #1533 (EC2 Instance Connect SSH)
PR #1531 + #1533 together deliver: SaaS workspace gets a browser-based terminal tab, no SSH keys required.
- **PR #1531**: Store the `instance_id` (data layer) ✅ **this brief**
- **PR #1533**: Connect via EICE using `instance_id` (UX layer) — brief exists at `pr-1533-ec2-instance-connect-ssh.md`
Route both to DevRel together. Content Marketer uses #1531 as one sentence in the EC2 Instance Connect SSH blog post.
---
## Sign-off
- [x] PMM positioning: approved
- [ ] Marketing Lead: pending
- [ ] DevRel: note in release notes + coordinate with #1533
---
*PMM — this PR is a prerequisite. Coordinate release note entry with #1533. Close when routed.*

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# Positioning Brief: EC2 Instance Connect SSH
**PR:** [#1533](https://github.com/Molecule-AI/molecule-core/pull/1533) — `feat(terminal): remote path via aws ec2-instance-connect + pty`
**Merged:** 2026-04-22
**Owner:** PMM | **Status:** APPROVED — routing to team
---
## Situation
When workspace provisioning moved from local Docker to the SaaS control plane (Fly Machines / EC2), a gap opened: Docker workspaces had a canvas terminal tab. SaaS-provisioned EC2 workspaces didn't — there was no path to exec into a cloud VM from the browser without a public IP, pre-configured SSH keys, or a bastion host.
PR #1533 closes that gap using **EC2 Instance Connect Endpoint (EICE)** — a purpose-built AWS service for IAM-authenticated, key-free SSH access to instances, including those in private subnets.
---
## Problem Statement
Getting a terminal into a SaaS-provisioned EC2 workspace requires infrastructure that most users don't have set up. The options available before this PR:
| Option | What's needed | Works for agents? |
|--------|---------------|---------------------|
| Direct SSH | Public IP + keypair + key distribution | No — no public IP on private-subnet EC2s |
| Bastion host | Separate EC2 + SSH config + key for bastion | No — extra infra, adds attack surface |
| SSM Session Manager | SSM agent installed + IAM profile + session document | Partially — requires pre-config per instance |
| EC2 Instance Connect CLI | `aws ec2-instance-connect ssh` — but must be run from a machine with the right IAM | Designed for humans, not agent runtimes |
For an agent runtime that spins up workspaces dynamically, none of these are acceptable. EC2 Instance Connect via EICE is the right fit: it requires only IAM permissions and a VPC Endpoint (already available in the SaaS VPC), and the session is initiated server-side by the platform — not by the agent's laptop.
---
## Solution
CP-provisioned workspaces (those with an `instance_id` in the workspaces table) get a terminal tab in the canvas automatically. The platform handles the EICE handshake and proxies the PTY over the WebSocket — the user sees a fully interactive terminal with no configuration required.
```
User opens terminal tab in canvas
→ platform checks workspace.instance_id
→ instance_id found → spawn aws ec2-instance-connect ssh --connection-type eice
→ PTY bridged to canvas WebSocket
→ user gets interactive shell in < 3 seconds
```
---
## Core Claims
### Claim 1: No SSH keys, no bastion, no public IP
EC2 Instance Connect pushes a temporary RSA key to the instance metadata via the AWS API, valid for 60 seconds. The session uses that key — no pre-shared key on disk, no key rotation to manage, no key distribution to instances. The platform initiates the connection; users never touch an SSH key.
### Claim 2: Private subnet instances work out of the box
EICE (EC2 Instance Connect Endpoint) routes the connection through AWS's internal network — no internet egress, no public IP, no ingress security group rules. The only requirement is a VPC Endpoint for EC2 Instance Connect in the same VPC as the target instance. The SaaS VPC already has this.
### Claim 3: Zero per-user configuration
The terminal tab appears for every CP-provisioned workspace automatically. No IAM role setup by the user, no SSM configuration, no bastion. The platform's IAM credentials (the same ones used to provision the instance) are used for EICE — the user doesn't need to know anything about AWS IAM policies to get a shell.
---
## Target Audience
**Primary:** DevOps and platform engineers managing SaaS-provisioned workspaces on EC2. They want browser-based terminal access without SSH key overhead. They likely already have IAM roles set up for their AWS environment and will recognise EICE as the right primitive.
**Secondary:** Enterprise security reviewers evaluating Molecule AI's SaaS offering. The ability to connect to cloud VMs via IAM — not shared SSH keys — is a meaningful signal. It aligns with the enterprise governance narrative and per-workspace auth token story.
**Not the audience:** Self-hosted users (Docker workspaces already have terminal via `docker exec`). The value proposition is SaaS/Control Plane-specific.
---
## Competitive Angle
EC2 Instance Connect integration for browser-based terminal access is not documented for any competitor:
- **LangGraph**: No terminal integration. Users who want shell access to provisioned resources must SSH manually or use SSM Session Manager via the AWS CLI.
- **CrewAI**: No cloud VM terminal story. Enterprise tier has SaaS management UI, but no browser-based shell access.
- **AutoGen (Microsoft)**: No EC2 integration documented. Relies on user-managed infrastructure.
- **Custom/self-rolled agent platforms**: Must implement EICE or SSM themselves. Molecule AI ships it as a product feature.
This is an uncontested claim for the AWS-aligned segment. It belongs in press briefings and analyst conversations as a concrete example of the SaaS control plane doing work users would otherwise have to do themselves.
---
## Messaging Tier
**Feature tier: Enhancement** (not a standalone product launch)
EC2 Instance Connect SSH is a meaningful UX improvement to the SaaS workspace experience. It belongs in:
- Phase 30 remote workspaces narrative as "SaaS terminal access"
- SaaS onboarding copy ("your EC2 workspace has a terminal tab — no SSH keys needed")
- Release notes (not a press release)
**Do not frame as:**
- A new standalone product
- A replacement for local Docker terminal
- A competitor-specific feature (lead with the benefit, not the AWS integration)
---
## Taglines
Primary: *"Your SaaS workspace has a terminal tab. No SSH keys required."*
Secondary: *"Connect to any EC2 workspace from the canvas — IAM-authorized, no bastion, no public IP."*
Fallback (technical): *"CP-provisioned workspaces get browser-based terminal via AWS EC2 Instance Connect Endpoint. No keypair on disk. No bastion. No configuration."*
---
## Channel Coverage
| Channel | Asset | Owner | Status |
|---------|-------|-------|--------|
| Blog post | "How to access your EC2 workspace terminal from the canvas" | Content Marketer | Blocked: needs DevRel code demo first |
| Social launch thread | 5 posts: problem → solution → claim 1 → claim 2 → CTA | Social Media Brand | Blocked: awaiting blog post + code demo |
| Code demo | Working example: open canvas → click terminal → interact with EC2 workspace | DevRel Engineer | Needs assignment (#1545) |
| Docs | `docs/infra/workspace-terminal.md` | DevRel Engineer | ✅ Shipped in PR #1533 |
**Coverage decision:** Blog post + social thread. Not a standalone campaign. Frame as "SaaS workspace terminal" within the Phase 30 remote workspaces narrative.
---
## Positioning Alignment
- **Phase 30 remote workspaces**: EICE terminal completes the remote workspace UX — agents register, accept tasks, and now also have a terminal, all without leaving the canvas
- **Per-workspace auth tokens**: The same IAM-scoped credentials that authorize A2A also authorize EICE — the platform manages the credential lifecycle, not the user
- **Enterprise governance**: No SSH keys means no orphaned keys in AWS IAM. Connection authorization via IAM is auditable in CloudTrail. This is a governance argument as much as a UX argument.
---
## Open Questions
- [x] Does the terminal UI expose EC2 Instance Connect as a distinct connection type? → No — seamless; the platform handles it transparently
- [x] Is there a docs page? → Yes: `docs/infra/workspace-terminal.md` (shipped in PR #1533)
- [ ] Social Media Brand: confirm launch thread length (5 posts recommended)
- [ ] Confirm EICE VPC Endpoint is present in the SaaS production VPC (DevOps/ops check)
---
## Sign-off
- [x] PMM positioning: approved
- [ ] Marketing Lead: pending
- [ ] DevRel: needs assignment (#1545)
- [ ] Content Marketer: blocked on DevRel code demo
---
*PMM — routing to DevRel (#1545 code demo) → Content Marketer (#1546 blog) → Social Media Brand (#1547 launch thread). Close when all routed.*

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# Chrome DevTools MCP — Social Copy
**Source:** PR #1306 merged to origin/main (2026-04-21)
**Status:** MERGED — awaiting Marketing Lead approval for publishing
---
## X (140–280 chars)
### Version A — Governance angle
```
Chrome DevTools MCP gives agents full browser control. Screenshot, DOM, JS execution — all through a standard interface.
Raw CDP is all-or-nothing. Molecule AI adds the governance layer: which agents get access, what they can do, how to revoke it.
Audit trail included.
```
### Version B — Production use cases
```
Three things you couldn't automate before Chrome DevTools MCP + Molecule AI governance:
1. Lighthouse CI/CD audits — agent opens Chrome, runs Lighthouse, posts score to PR
2. Visual regression testing — screenshot diffs across agent workflow runs
3. Authenticated session scraping — agent behind a login with managed cookies
All with org API key audit trail.
```
### Version C — Problem framing
```
Chrome DevTools MCP: browser automation as a first-class MCP tool.
For prototypes: great. For production: you need something between no browser and full admin. That's the gap Molecule AI's MCP governance fills.
```
---
## LinkedIn (100–200 words)
Chrome DevTools MCP shipped in early 2026 — and browser automation is now a standard tool for any compatible AI agent.
Screenshot. DOM inspection. Network interception. JavaScript execution. No custom wrappers, no browser-driver installation.
That's the prototype story. For production — especially anything touching customer-facing workflows or authenticated sessions — all-or-nothing CDP access is a governance gap.
Molecule AI's MCP governance layer answers the production questions:
- Which agents can open a browser?
- What can they do with it?
- How do you revoke access?
- When something goes wrong, who accessed what session data?
Real-world use cases the layer enables: automated Lighthouse performance audits in CI/CD, screenshot-based visual regression testing, and authenticated session scraping — agents operating behind a login with cookies managed through the platform's secrets system.
Every action is logged. Every browser operation is attributed to an org API key and workspace ID.
Chrome DevTools MCP plus Molecule AI's governance layer: browser automation that meets production standards.
---
## Image suggestions
| Post | Image |
|---|---|
| X Version A | Fleet diagram: `marketing/assets/phase30-fleet-diagram.png` (reusable) |
| X Version B | Custom: 3-item checklist graphic — "Lighthouse / Regression / Auth Scraping" |
| X Version C | Quote card: "something between no browser and full admin" |
| LinkedIn | Quote card or the checklist graphic |
---
## Hashtags
`#MCP` `#BrowserAutomation` `#AIAgents` `#MoleculeAI` `#DevOps` `#QA` `#CI/CD`
---
## Blog canonical URL
`docs.moleculesai.app/blog/browser-automation-ai-agents-mcp`
---
## MCP Server List Explainer
**File:** `docs/marketing/campaigns/mcp-server-list/social-copy.md` (staging, commit `0d3ad96`)
**Status:** COPY READY — awaiting visual assets + X credentials
**Canonical URL:** `docs.molecule.ai/blog/mcp-server-list`
**Owner:** Social Media Brand | **Day:** Ready once visual assets done
5-post X thread + LinkedIn post. Full copy on staging.
---
## Discord Adapter Day 2
**File:** `discord-adapter-social-copy.md` (local)
**Status:** COPY READY — awaiting visual assets + X credentials
**Canonical URL:** `docs.molecule.ai/blog/discord-adapter` (live, PR #1301 merged)
**Owner:** Social Media Brand | **Day:** Ready once visual assets done
See `discord-adapter-social-copy.md` for full copy (4 X variants + LinkedIn draft).
---
## Fly.io Deploy Anywhere (T+3 catch-up)
**Source:** Blog live 2026-04-17 | Social delayed 5 days
**File:** `fly-deploy-anywhere-social-copy.md` (local)
**Status:** COPY READY — PMM executing Option A (retrospective catch-up). Awaiting X credentials.
**Canonical URL:** `moleculesai.app/blog/deploy-anywhere`
**Owner:** Social Media Brand | **Day:** Queue immediately after Chrome DevTools MCP Day 1 posts
**Decision:** PMM chose Option A per decision brief. Frame: "we shipped this last week."
Retrospective framing: "Week in review: we shipped Fly.io Deploy Anywhere last week. Here's what it means for your agent infrastructure."
Social Media Brand: hold Fly.io post until Chrome DevTools MCP Day 1 posts land, then queue Fly.io in the same session.
---
## EC2 Instance Connect SSH (PR #1533)

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# Discord Adapter — Social Copy
**Feature:** Discord channel adapter (inbound via Interactions webhook, outbound via Incoming Webhooks)
**Campaign:** Discord Adapter | **Docs:** `docs/agent-runtime/social-channels.md` (Discord Setup section)
**Canonical URL:** `github.com/Molecule-AI/molecule-core/blob/main/docs/agent-runtime/social-channels.md` (moleculesai.app TBD — outage confirmed)
**Status:** APPROVED (PMM proxy — Marketing Lead offline) | Reddit/HN copy ADDED by PMM
**Owner:** PMM → Social Media Brand | **Day:** Ready to post once X credentials are restored
---
## X (140–280 chars)
### Version A — Slash commands for agents
```
Your Discord community just got an agent layer.
Connect a Molecule AI workspace to any Discord channel. Members query your agents via slash commands — no bot token setup for outbound.
Governance included. Audit trail included.
```
### Version B — Multi-channel agent access
```
Your AI agents can already handle Telegram, email, and Slack.
Now add Discord — without changing how agents work.
Slash commands → agent workspace → response to any channel.
One protocol. Any channel. Molecule AI's channel adapter.
```
### Version C — Developer angle
```
Setting up an AI agent in Discord used to mean: create app, configure intents, handle events.
Molecule AI's Discord adapter: paste a webhook URL. Done.
Inbound via Interactions. Outbound via Incoming Webhook. Zero bot token management.
```
### Version D — Platform angle
```
Discord communities can now talk to your agent fleet.
Molecule AI's channel adapter: one workspace, any social platform. Telegram, Slack, Discord — all the same agent underneath.
Your agents. Your channels. One canvas.
```
---
## LinkedIn (100–200 words)
```
Connecting your AI agent fleet to Discord just got simpler — and more powerful.
Molecule AI's Discord adapter ships today. Here's what that means in practice:
Outbound messages: paste an Incoming Webhook URL. That's it. No Discord bot app, no OAuth token, no intent configuration — just a webhook URL and your agent is live in any channel.
Inbound: slash commands and message components arrive as signed Interactions payloads. The adapter parses them, forwards them to the workspace agent, and routes the response back to Discord.
Your Discord community gets access to the same agent capabilities as your Telegram users, your Slack channels, and your Canvas — without duplicating the agent logic or managing separate bot tokens.
One protocol. Any channel. Molecule AI's channel adapter layer makes social platforms first-class citizen channels for your agent fleet.
```
---
## Image suggestions
| Post | Image | Source |
|---|---|---|
| X Version A | Slash command dropdown screenshot — `/agent` in Discord | Custom: Discord UI screenshot |
| X Version B | Multi-channel diagram: Telegram + Slack + Discord → same workspace agent | Custom: platform diagram |
| X Version C | Before/after: complex bot setup vs "paste webhook URL" | Custom: simple comparison card |
| X Version D | Canvas Channels tab with Discord connected | Custom: Canvas screenshot |
| LinkedIn | Multi-platform diagram | Custom |
---
## Hashtags
`#MoleculeAI` `#Discord` `#AIAgents` `#MCP` `#SocialChannels` `#MultiChannel` `#AgentPlatform` `#DevOps`
---
## CTA
`moleculesai.app/docs/agent-runtime/social-channels`
---
## Campaign timing
Ready to post once:
1. X consumer credentials (`X_API_KEY` + `X_API_SECRET`) are restored to Social Media Brand workspace — blocking all posts
2. Discord Adapter Day 2 copy is approved by Marketing Lead (coordinate with Social Media Brand)
---
*PMM drafted 2026-04-22 — no prior social copy file found for Discord adapter*
*Positioning note: Discord adapter is outbound-primary (no separate bot token for outbound); inbound via Interactions webhook — leverage this simplicity in copy*
---
## Reddit Post (r/LocalLLaMA or r/MachineLearning)
```
Molecule AI just shipped a Discord adapter for AI agent fleets.
The setup: paste a webhook URL. That's it — no Discord bot app, no OAuth token, no intent configuration.
Inbound: slash commands and message components arrive as signed Interactions payloads. The adapter parses them, forwards to your workspace agent, routes the response back to Discord.
Outbound: same incoming webhook, no separate bot token needed.
One workspace. Any channel. Your Telegram, Slack, and Discord users all hit the same agent underneath — no duplicated logic, no separate bot tokens per platform.
GitHub: github.com/Molecule-AI/molecule-core
Docs: github.com/Molecule-AI/molecule-core/blob/main/docs/agent-runtime/social-channels.md
```
---
## Hacker News — Show HN
```
Show HN: Molecule AI Discord adapter — webhook URL setup, zero bot token management
Molecule AI shipped a Discord channel adapter for AI agent fleets.
The problem it solves: connecting Discord to an AI agent fleet usually means creating a Discord app, configuring intents, handling events, managing token rotation. The agent logic isn't the hard part — the integration is.
What we built: a Discord adapter that uses Discord's Interactions webhooks for inbound and Incoming Webhooks for outbound. No Discord bot app required. No OAuth token. No intent configuration.
Setup: paste an Incoming Webhook URL. Done.
Inbound: slash commands and message components arrive as signed Interactions payloads. The adapter parses them, forwards to your workspace agent, routes the response back to the channel.
Outbound: same incoming webhook. No separate bot token for outbound messages.
What this means in practice: your Discord community gets access to the same agent capabilities as your Telegram users, your Slack channels, and your Canvas — without duplicating the agent logic or managing separate bot tokens per platform.
Under 100 lines to add Discord to an existing Molecule AI workspace. Full source in the linked repo.
GitHub: github.com/Molecule-AI/molecule-core
Docs: github.com/Molecule-AI/molecule-core/blob/main/docs/agent-runtime/social-channels.md
```

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# EC2 Instance Connect SSH — Social Copy
**Feature:** PR #1533 — `feat(terminal): remote path via aws ec2-instance-connect + pty`
**Campaign:** EC2 Instance Connect SSH | **Blog:** `docs/infra/workspace-terminal.md` (shipped in PR #1533)
**Canonical URL:** `moleculesai.app/docs/infra/workspace-terminal`
**Status:** DRAFT — PMM proactive draft; no file existed before this entry
**Owner:** PMM → Social Media Brand | **Day:** Blocked on DevRel code demo (#1545) + Content Marketer blog (#1546)
---
## X (140–280 chars)
### Version A — Infrastructure angle
```
Your SaaS-provisioned EC2 workspace has a terminal tab. No SSH keys needed.
Molecule AI connects via EC2 Instance Connect Endpoint — IAM-authorized, no bastion, no public IP required.
One click. You're in.
```
### Version B — Zero credential overhead
```
Connecting to a cloud VM used to mean: SSH key, bastion host, public IP, and a security review.
EC2 Instance Connect changes that. Your IAM role is the auth layer. No keys on disk. No rotation. No gap.
The terminal just works.
```
### Version C — Developer angle
```
Your agent's EC2 workspace just got a terminal tab.
No pre-configured SSH keys. No bastion. No public IP needed.
Molecule AI handles EC2 Instance Connect for you — IAM-authorized, PTY over WebSocket, in the canvas.
That's the SaaS difference.
```
### Version D — Security / Enterprise
```
SSH key left on a laptop. Former employee. Rotation takes a week.
EC2 Instance Connect: no shared keys, no orphaned credentials, every connection authorized via IAM and logged in CloudTrail.
Security teams notice this architecture.
```
### Version E — Problem → solution
```
Problem: SaaS-provisioned EC2 workspaces don't have a terminal tab without SSH keys, a bastion, and a public IP.
Solution: EC2 Instance Connect Endpoint. IAM-authorized. Platform-initiated. No user-side key management.
Your canvas workspace just got a shell.
```
---
## LinkedIn (100–200 words)
```
Getting a terminal into a cloud VM shouldn't require a security review, a bastion host, and an SSH keypair.
For SaaS-provisioned workspaces — the ones running on Fly Machines or EC2 — that was the reality until this week. Connecting to a remote VM meant: pre-configured keys, a jump box, and either a public IP or an SSM agent installed per instance.
EC2 Instance Connect Endpoint changes this. The platform's IAM credentials authorize the connection. A temporary RSA key appears in the instance metadata (valid for 60 seconds), and the session is proxied over WebSocket to the canvas terminal tab. No keys on disk. No bastion. No configuration required.
The terminal tab appears automatically for every CP-provisioned workspace. The connection is IAM-authorized, so every session is attributable in CloudTrail. Revocation is immediate — stop the IAM role, the connection stops.
This is what SaaS terminal access looks like when it's designed for agents, not humans with SSH config files.
```
---
## Image suggestions
| Post | Image | Source |
|---|---|---|
| X Version A | Canvas screenshot: terminal tab open on a REMOTE badge workspace | Custom: needs DevRel code demo screenshot |
| X Version B | Before/after: SSH key config vs "just click terminal" | Custom graphic |
| X Version C | Terminal demo: IAM auth flow → canvas terminal | Custom: DevRel code demo output |
| X Version D | IAM policy diagram: EC2 Instance Connect → CloudTrail log entry | Custom: AWS CloudTrail screenshot |
| X Version E | Problem/solution card: "Before: bastion + keys + public IP" vs "After: one click, canvas terminal" | Custom graphic |
| LinkedIn | Canvas terminal screenshot with REMOTE badge | Custom |
---
## Hashtags
`#MoleculeAI` `#AWS` `#EC2` `#AIInfrastructure` `#AgentPlatform` `#DevOps` `#Security` `#A2A` `#RemoteWorkspaces`
**Note:** `#AgenticAI` removed — does not appear in Phase 30 positioning brief; keep messaging consistent.
---
## CTA
`moleculesai.app/docs/infra/workspace-terminal`
---
## Campaign timing
Dependent on: DevRel code demo (#1545) → Content Marketer blog (#1546) → Social Media Brand launch thread.
Recommended: Coordinate with DevRel screencast; social posts should reference the demo for credibility.
---
*PMM drafted 2026-04-22 — no prior social copy file found anywhere in workspace*
*Positioning brief: `docs/marketing/launches/pr-1533-ec2-instance-connect-ssh.md`*

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# Fly.io Deploy Anywhere — Social Copy
**Campaign:** Fly.io Deploy Anywhere | **Blog:** `docs/blog/2026-04-17-deploy-anywhere/index.md`
**Canonical URL:** `moleculesai.app/blog/deploy-anywhere`
**Status:** DRAFT — PMM wrote this copy; no file existed anywhere before this entry
**Owner:** PMM → Social Media Brand | **Day:** T+3 (campaign delayed from April 17)
---
## X (140–280 chars)
### Version A — Infrastructure freedom
```
Your cloud. Your choice.
Molecule AI workspaces now run on Docker, Fly.io, or your control plane — with one config change. No agent code changes. No migration tax.
Your agents. Your infra.
```
### Version B — Developer pain
```
Setting up AI agent infrastructure on Fly.io took a week. With Molecule AI it takes one environment variable.
Three variables. Done. That's it.
```
### Version C — Multi-cloud reality
```
Most agent platforms assume you run Docker. Molecule AI doesn't.
Docker, Fly.io, or control plane — the backend is a runtime choice, not an architectural commitment. Your agent code stays the same.
```
### Version D — Indie dev angle
```
Fly.io's economics for AI agents — scale to zero when nobody's working, pay per use.
Molecule AI workspaces run on Fly Machines. Zero config. One env var. Production-ready from day one.
```
---
## LinkedIn (100–200 words)
```
Your infrastructure choice just got decoupled from your agent platform choice.
Molecule AI ships three production-ready workspace backends — Docker, Fly.io, and a control plane — and switching between them takes a single environment variable. Your agent code, model choices, and workspace topology stay exactly the same.
Until this week, if you wanted Fly.io's economics — pay-per-use compute, fast cold starts, scale to zero when nobody's working — you had to migrate your agent platform. That trade-off is gone.
Today: set three environment variables on your Molecule AI tenant instance, and your workspaces provision as Fly Machines. No separate Docker host. No idle infrastructure. Your agents run on Fly.io with Molecule AI's canvas, A2A protocol, and auth model — same platform, different backend.
Set it and forget it — until you want to switch back.
Molecule AI workspace backends: Docker, Fly.io, Control Plane. One config change.
```
---
## Image suggestions
| Post | Image |
|---|---|
| X Version A | Comparison card: Docker vs Fly.io vs Control Plane — three boxes, same logo |
| X Version B | Terminal: 3 env vars → workspace online on Fly.io |
| X Version C | Diagram: "Backend = runtime choice" — agent code central, 3 arrows to Docker/Fly.io/Control Plane |
| LinkedIn | Fleet diagram (reusable from Phase 30 — same visual, different caption) |
---
## Hashtags
`#MoleculeAI` `#FlyIO` `#AIInfrastructure` `#AgentPlatform` `#DevOps` `#AIAgents` `#A2A` `#RemoteWorkspaces`
**Note:** `#AgenticAI` removed per Phase 30 positioning brief. `#AIAgents` and `#A2A` added for cross-campaign consistency.
---
## Campaign timing note
Blog went live April 17. As of April 22 this campaign is 5 days stale. Recommend one of:
- Fold into Phase 30 social push as a variant (low effort, reuse fleet diagram)
- Hold for a Fly Machines pricing/GA moment
- Drop from active queue
Confirm with Marketing Lead.
---
*PMM drafted 2026-04-21 — no prior social copy file found anywhere in workspace*

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# Phase 30 — Short-Form Social Copy
**Source:** PR #1306 merged to origin/main (2026-04-21)
**Status:** MERGED — awaiting Marketing Lead approval for publishing
---
## X (140–280 chars)
### Version A — Technical
```
Phase 30 ships: Molecule AI remote workspaces are GA.
Agents running on your laptop, AWS, GCP, or on-prem now register to the same org as your Docker agents. Same A2A. Same auth. Same canvas.
Remote badge. That's the only difference.
→ docs: https://moleculesai.app/docs/guides/remote-workspaces
```
### Version B — Product
```
Your laptop is now a valid Molecule AI runtime.
One org. Mixed fleet: Docker agents on the platform, remote agents wherever your infrastructure lives. One canvas. One audit trail.
Phase 30 is live.
```
### Version C — Developer
```
How to run a Molecule AI agent on your laptop in 3 steps:
1. Create a workspace (runtime: external)
2. Run the Python SDK
3. Watch it appear on the canvas
That's it. Phase 30 is live.
docs → https://moleculesai.app/docs/guides/remote-workspaces
```
### Version D — Enterprise
```
Multi-cloud AI agent fleets, single governance plane.
Phase 30: agents on AWS, GCP, on-prem, your laptop — all visible in one canvas, all governed by the same platform auth, all auditable.
GA today.
```
---
## LinkedIn (150–300 words)
```
We're launching Phase 30: Remote Workspaces.
Most AI agent platforms assume all agents run in the same environment as the control plane. Molecule AI didn't — but until today, that's where the story ended.
Phase 30 changes that. Your agent can now run anywhere:
- On a developer's laptop, for local iteration and debugging
- On AWS or GCP, for production workloads in your cloud
- On an on-premises server, for enterprise environments with data residency requirements
- On a third-party endpoint, for existing SaaS integrations
And from the canvas, you can't tell the difference. Same workspace card. Same status. Same chat tab. Same audit trail. The only visible signal: a purple REMOTE badge.
The governance is the same. The A2A protocol is the same. The auth contract is the same. Where the agent runs is a deployment detail — not an architectural constraint.
Phase 30 is generally available today.
See the quick start → [link]
Read the guide → [link]
```
---
## Image suggestions per post
| Post | Best image |
|---|---|
| X Version A (Technical) | Fleet diagram: `marketing/assets/phase30-fleet-diagram.png` |
| X Version B (Product) | Canvas screenshot: `marketing/assets/phase30-canvas-remote-badge.png` (once captured) |
| X Version C (Developer) | Terminal screenshot: `python3 run.py` + canvas showing REMOTE badge |
| X Version D (Enterprise) | Fleet diagram (same as A) |
| LinkedIn | Fleet diagram OR canvas screenshot |
---
## Hashtags
`#MoleculeAI` `#RemoteWorkspaces` `#AIAgents` `#AgentFleet` `#AIPlatform` `#MCP` `#A2A` `#MultiCloud`

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# Screencast Storyboard — AGENTS.md Auto-Generation
**PR:** #763 | **Feature:** `workspace/agents_md.py` | **Duration:** 60 seconds
**Format:** Terminal-led with Canvas overlay cuts
---
## Pre-roll (0:00–0:03)
**Canvas — full screen**
Two workspace cards in Canvas: `pm-agent [ONLINE]` and `researcher [IDLE]`.
Narration (0:00–0:03):
> "Two agents. The PM coordinates. The researcher does the work. They need to talk to each other — without humans in the loop."
**Camera:** Static Canvas view. No cursor movement. Clean frame.
---
## Moment 1 — PM boots, AGENTS.md generated (0:03–0:12)
**Cut to:** Terminal window, terminal prompt: `agent@pm-workspace:~$`
```bash
INFO main: Starting workspace pm-agent
INFO agents_md: Generating AGENTS.md for workspace 'pm-agent'
INFO agents_md: Generated AGENTS.md at /workspace/AGENTS.md
INFO a2a: A2A server listening on :8000
INFO main: Workspace 'pm-agent' online
```
**Camera:** Type-in animation. Cursor blinks. Text appears line by line (playback speed 2x).
Narration (0:06–0:12):
> "When the PM workspace starts up, AGENTS.md is generated automatically — from the config file, not a human."
**Highlight:** `INFO agents_md: Generated AGENTS.md at /workspace/AGENTS.md` — brief yellow highlight ring (1s).
---
## Moment 2 — Researcher reads PM's AGENTS.md (0:12–0:25)
**Cut to:** Second terminal tab. Prompt: `agent@researcher:~$`
```python
import requests
resp = requests.get(
"https://acme.moleculesai.app/workspaces/ws-pm-123/files/AGENTS.md",
headers={"Authorization": "Bearer researcher-token-xxx"},
)
print(resp.json()["content"])
```
**Terminal output:**
```markdown
# pm-agent
**Role:** Project Manager
## Description
PM agent — coordinates tasks, dispatches to reports, manages timeline.
## A2A Endpoint
http://pm-workspace:8000/a2a
## MCP Tools
- delegate_to_workspace
- check_delegation_status
```
**Camera:** Scroll to full file. Hold 2s.
Narration (0:14–0:22):
> "The researcher reads the PM's AGENTS.md — through the platform API. Instantly knows the PM's role, its A2A endpoint, and the tools it has."
**Callout text (bottom-left):**
`No system prompts. No documentation lookup. Just the facts.`
---
## Moment 3 — Researcher dispatches A2A task (0:25–0:42)
```python
from a2a import A2ATask
task = A2ATask(
to="http://pm-workspace:8000/a2a",
type="status_report",
payload={
"milestone": "data-pipeline",
"status": "complete",
"artifacts": ["dataset-v3.parquet"],
}
)
result = task.send()
print(result)
```
**Terminal output:**
```json
{"task_id": "task-abc-456", "status": "queued", "pm_receipt": "2026-04-21T00:00:22Z"}
```
Narration (0:27–0:35):
> "Now the researcher has everything it needs. It sends an A2A task to the PM — using the endpoint it discovered from AGENTS.md. No hardcoded addresses."
---
## Moment 4 — PM receives task (0:42–0:52)
**Cut to:** Canvas — pm-agent card.
New message bubble: `researcher: Status report — data-pipeline complete. 1 artifact ready.`
Status: `pm-agent [ACTIVE]`, `researcher [DISPATCHED]`
Narration (0:42–0:48):
> "The PM receives it in Canvas. Status updated. The coordination happened without human input — AAIF in action."
---
## Close (0:52–1:00)
**Canvas full frame.** Both cards visible.
Narration (0:52–0:58):
> "AGENTS.md means every agent knows what its peers can do — without reading system prompts. Auto-generated. Always current. That's the AAIF standard, from Molecule AI."
**End card:**
```
AGENTS.md Auto-Generation
workspace/agents_md.py — molecule-core#763
```
**Fade to black.**
---
## Production Spec
| Spec | Value |
|------|-------|
| Terminal theme | Dark, SF Mono 14pt / JetBrains Mono 13pt |
| Canvas cutaway | Dev canvas localhost:3000, pre-record before session |
| Camera | Screenflow / Camtasia, 1440×900 → 1080p export |
| VO voice | en-US-AriaNeural (reference) |
| Callout highlight | Amber ring `#E8A000`, 1s fade-in/out |
| Green success | Green ring `#22C55E` for success moments |
| Music | None — clean and technical |
| Sound FX | Subtle 2s click at 0:03 (boot log) |
| VO pacing | Read script against timeline before locking VO session |

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# Screencast Storyboard — Cloudflare Artifacts Integration
**PR:** #641 | **Feature:** `POST/GET /workspaces/:id/artifacts`, `/artifacts/fork`, `/artifacts/token`
**Duration:** 60 seconds | **Format:** Terminal-led, clean dark theme
---
## Pre-roll (0:00–0:04)
**Canvas — full screen**
Single workspace card: `data-agent [ONLINE]`, status: `idle`.
Narration (0:00–0:04):
> "This data-agent has been running for three hours. It has context, task state, memory. What happens when it disconnects?"
**Camera:** Static Canvas frame. 3-second hold. No cursor.
---
## Moment 1 — Attach a CF Artifacts repo (0:04–0:16)
**Terminal:** `agent@data-agent:~$`
```bash
WORKSPACE_ID="ws-data-agent-001"
PLATFORM="https://acme.moleculesai.app"
TOKEN="Bearer ws-token-xxx"
curl -s -X POST "$PLATFORM/workspaces/$WORKSPACE_ID/artifacts" \
-H "Authorization: $TOKEN" \
-H "Content-Type: application/json" \
-d '{"name": "data-agent-snapshots", "description": "Versioned snapshots of data-agent workspace"}' \
| jq
```
**Terminal output:**
```json
{
"id": "art-uuid-789",
"workspace_id": "ws-data-agent-001",
"cf_repo_name": "data-agent-snapshots",
"remote_url": "https://hash.artifacts.cloudflare.net/git/data-agent-snapshots.git",
"created_at": "2026-04-21T00:00:10Z"
}
```
**Camera:** Cursor to `remote_url`, highlight ring. Hold 1s.
Narration (0:06–0:14):
> "One API call attaches a Cloudflare Artifacts git repo to the workspace. A remote URL is returned — no CF dashboard required."
**Callout text (bottom-left):**
`Git for agents. No separate setup.`
---
## Moment 2 — Mint a credential, clone the repo (0:16–0:28)
```bash
TOKEN_RESP=$(curl -s -X POST "$PLATFORM/workspaces/$WORKSPACE_ID/artifacts/token" \
-H "Authorization: $TOKEN" -H "Content-Type: application/json" \
-d '{"scope": "write", "ttl": 3600}')
CLONE_URL=$(echo $TOKEN_RESP | jq -r '.clone_url')
git clone "$CLONE_URL" /tmp/data-agent-snapshots
```
**Terminal output:**
```
Cloning into '/tmp/data-agent-snapshots'...
Receiving objects: 100% | (12/12), 12.00 KiB, done.
```
**Camera:** Scroll through git clone output. Hold on `Receiving objects: 100%`.
Narration (0:18–0:26):
> "A short-lived git credential is minted — valid for one hour. The agent clones the repo. Cloudflare Artifacts handles the git transport."
---
## Moment 3 — Agent writes a snapshot (0:28–0:44)
```bash
cd /tmp/data-agent-snapshots
echo "# Workspace State — 2026-04-21" > snapshot.md
echo "current_task: analyzing sales pipeline Q1" >> snapshot.md
echo "uptime_seconds: 10800" >> snapshot.md
echo "last_status: COMPLETE" >> snapshot.md
git add snapshot.md
git commit -m "snapshot: pipeline analysis complete — 3 key findings"
git push origin main
```
**Terminal output:**
```
[main abc1234] snapshot: pipeline analysis complete — 3 key findings
1 file changed, 5 insertions(+)
remote: success
```
**Camera:** Full commit → push. Hold on `remote: success`. **Green ring pulse `#22C55E`**.
Narration (0:30–0:40):
> "The agent writes a snapshot — current task, data sources, key findings — commits and pushes. The state is now in Cloudflare Artifacts. Versioned. Recoverable."
**Callout text:**
`Versioned agent state — every push is a checkpoint.`
---
## Moment 4 — Fork the repo for a new workspace (0:44–0:54)
```bash
curl -s -X POST "$PLATFORM/workspaces/$WORKSPACE_ID/artifacts/fork" \
-H "Authorization: $TOKEN" -H "Content-Type: application/json" \
-d '{"name": "researcher-from-data-agent", "description": "Forked from data-agent workspace", "default_branch_only": true}' \
| jq
```
**Terminal output:**
```json
{
"fork": {"name": "researcher-from-data-agent", "namespace": "acme-production", "remote_url": "..."},
"object_count": 47,
"remote_url": "https://hash2.artifacts.cloudflare.net/git/researcher-from-data-agent.git"
}
```
**Camera:** Highlight `remote_url` and `object_count`. Hold 2s.
Narration (0:45–0:52):
> "Another agent forks the repo — a separate, isolated copy. 47 objects transferred. The new workspace can clone it and continue from the same point."
---
## Close (0:54–1:00)
**Terminal clean frame.** Cursor at prompt.
Narration (0:54–0:58):
> "Every workspace can have its own git history. Snapshot state, version it, fork it into a new agent. Git for agents, built into the platform."
**End card:**
```
Cloudflare Artifacts Integration
workspace-server/internal/handlers/artifacts.go — molecule-core#641
```
**Fade to black.**
---
## Production Spec
| Spec | Value |
|------|-------|
| Terminal theme | Same as AGENTS.md storyboard — dark, SF Mono 14pt / JetBrains Mono 13pt |
| Canvas cutaway | Dev canvas localhost:3000, pre-record before session |
| Camera | Screenflow / Camtasia, 1440×900 → 1080p export |
| JSON output | `jq --monochrome-output` or custom monochrome filter for dark theme |
| Callout highlight | Amber ring `#E8A000`, 1s fade-in/out |
| Green success | Green ring `#22C55E` on `remote: success` line, 1.5s hold |
| VO voice | Match AGENTS.md storyboard — same voice talent, consistent pacing |
| Music | None |
| Sound FX | Subtle single-tone click at 0:04 (repo attached) and 0:54 (end card) |
| Playback speed | curl/git/push sequence at 2x during Moments 1–4 |

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# Screencast Storyboard — MemoryInspectorPanel
**Feature:** `canvas/src/components/MemoryInspectorPanel.tsx`
**Duration:** 60 seconds | **Format:** Canvas UI-led, dark zinc theme
---
## Pre-roll (0:00–0:04)
**Canvas — workspace panel open**
Sidebar showing `pm-agent [ONLINE]`. User clicks into the Memory tab.
Narration (0:00–0:04):
> "Every agent accumulates knowledge over time — facts, decisions, context. Molecule AI's memory inspector gives you a first-class view of what your agent knows."
**Camera:** Static Canvas panel. Clean frame. No cursor movement in first 3s.
---
## Moment 1 — Memory list loads (0:04–0:14)
**Panel populated:**
Three memory entry cards visible:
- `user-preferences:v3` — blue badge "Similarity: 92%" — "2h ago"
- `project-context:v1` — "4h ago"
- `latest-decision:v5` — "1d ago"
Each card shows: key (blue mono), version counter, similarity badge (if query active), relative timestamp, expand arrow.
**Camera:** Smooth scroll through the list. Hold 2s on the first entry.
Narration (0:05–0:12):
> "The inspector loads all memory entries — keys, versions, freshness. When semantic search is active, it shows a similarity score — how closely each entry matches your query."
**Callout text (bottom-left):**
`Semantic search. Meaning, not just keywords.`
---
## Moment 2 — Semantic search (0:14–0:26)
User types in the search bar: `customer pricing`
**Camera:** Cursor moves to search input. Type-in animation.
Search bar shows: "Semantic search…" placeholder, debounce spinner (300ms), then results update.
List re-sorts:
- `user-preferences:v3` — blue badge "Similarity: 87%" (moved to top)
- `latest-decision:v5` — "Similarity: 34%" (new position)
- `project-context:v1` — "Similarity: 12%" (bottom)
**Camera:** Smooth scroll showing re-sorted results.
Narration (0:16–0:23):
> "Type a query. After 300 milliseconds — no submit button — the list re-sorts by semantic similarity. Entries below 50% fade to a lower contrast. The agent found what it knows about pricing decisions."
**Callout text:**
`300ms debounce. No submit. No page reload.`
---
## Moment 3 — Expand + Edit a memory entry (0:26–0:44)
User clicks `user-preferences:v3`.
**Camera:** Entry expands. Card opens downward.
**Expanded content shown:**
```json
{
"preferred_tier": "enterprise",
"pricing_sensitivity": "high",
"last_interaction": "2026-04-18",
"notes": "Requested SSO before trial"
}
```
Metadata below: "Updated: 2026-04-20 14:32:11", Edit button, Delete button.
User clicks **Edit**.
**Camera:** Textarea appears, pre-filled with JSON. Cursor blinks.
User edits: changes `"pricing_sensitivity": "high"` → `"medium"`.
User clicks **Save**.
**Camera:** Blue "Saving…" spinner (1s). Then: textarea closes, entry collapses, entry updates in list — `user-preferences:v4` (version increment shown).
Narration (0:28–0:40):
> "Click any entry. See the full JSON — every fact the agent stored. Edit directly in the panel. Save — it's versioned, timestamped, persisted. No API calls to remember."
**Callout text:**
`Version conflict detection. Optimistic updates. Never lose a write.`
---
## Moment 4 — Delete entry (0:44–0:54)
User clicks the red Delete button on `project-context:v1`.
**Delete confirmation dialog appears:**
`Delete key "project-context"? This cannot be undone.`
User clicks **Delete**.
**Camera:** Dialog closes. Entry animates out. List collapses. Count decrements: "2 entries" shown in toolbar.
Narration (0:46–0:52):
> "Delete with confirmation. Entries are removed from the memory store immediately. Canvas updates in real time."
---
## Close (0:54–1:00)
**Panel clean frame.** Two entries remaining.
Narration (0:54–0:58):
> "The memory inspector — semantic search, in-line editing, version history, and full delete. Everything your agent knows, visible and editable."
**End card:**
```
MemoryInspectorPanel
canvas/src/components/MemoryInspectorPanel.tsx
```
**Fade to black.**
---
## Production Spec
| Spec | Value |
|------|-------|
| Theme | Dark zinc, blue accents (`#3B82F6`), SF Mono 11-14pt |
| Canvas | Dev canvas localhost:3000, pre-record workspace with 3+ memory entries |
| Camera | Screenflow / Camtasia, 1440×900 → 1080p export |
| Type-in animation | Realistic cursor blink, natural typing speed |
| Dialog | Center modal with red "Delete" button |
| Callout highlight | Amber ring `#E8A000`, 1s fade-in/out |
| VO voice | en-US-AriaNeural (consistent with other storyboards) |
| Music | None |
| Speed | Moment 1 at 2x playback for log-scroll effect |

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# Screencast Storyboard — Snapshot Secret Scrubber
**PR:** #977 | **Feature:** `workspace/lib/snapshot_scrub.py`
**Duration:** 60 seconds | **Format:** Terminal-led + browser overlay, dark theme
---
## Pre-roll (0:00–0:04)
**Terminal — dark theme**
Prompt: `agent@pm-workspace:~$`
Narration (0:00–0:04):
> "Every agent workspace can hibernate — preserving its memory state to disk. But what if that snapshot contains secrets? That's where the scrubber comes in."
**Camera:** Static terminal frame. 3-second hold. No cursor.
---
## Moment 1 — Before: raw memory snapshot with secrets (0:04–0:18)
**Terminal:**
```bash
# Simulate a raw memory entry before scrubbing
python3 - << 'EOF'
from snapshot_scrub import scrub_snapshot
raw_snapshot = {
"workspace_id": "ws-pm-001",
"memories": [
{
"key": "api_config",
"content": "ANTHROPIC_API_KEY=sk-ant-abcd1234wxyz5678",
"updated_at": "2026-04-20T10:00:00Z"
},
{
"key": "user_context",
"content": "User asked about enterprise pricing.",
"updated_at": "2026-04-20T10:01:00Z"
},
{
"key": "sandbox_output",
"content": "[sandbox_output] Running: pip install requests\nOutput: success",
"updated_at": "2026-04-20T10:02:00Z"
}
]
}
print(scrub_snapshot(raw_snapshot))
EOF
```
**Terminal output (raw, BEFORE scrub):**
```json
{
"workspace_id": "ws-pm-001",
"memories": [
{"key": "api_config", "content": "ANTHROPIC_API_KEY=sk-ant-abcd1234wxyz5678"},
{"key": "user_context", "content": "User asked about enterprise pricing."},
{"key": "sandbox_output", "content": "[sandbox_output] Running: pip install..."}
]
}
```
**Camera:** Highlight the raw ANTHROPIC_API_KEY and sandbox output lines — red underline. Hold 2s.
Narration (0:06–0:16):
> "A raw snapshot before scrubbing. The agent stored an API key in memory. It also ran code — and the sandbox output is in there too. Both are about to go to disk when this workspace hibernates."
**Callout text (bottom-left):**
`Before scrubbing: API keys, Bearer tokens, sandbox output — all on disk.`
---
## Moment 2 — Scrubber runs (0:18–0:32)
**Terminal — same session:**
The python script runs.
**Terminal output (AFTER scrub):**
```json
{
"workspace_id": "ws-pm-001",
"memories": [
{
"key": "api_config",
"content": "[REDACTED:API_KEY]"
},
{
"key": "user_context",
"content": "User asked about enterprise pricing."
}
]
}
```
**Camera:** The output appears line by line. Watch:
1. `"api_config"` entry — content replaced with `[REDACTED:API_KEY]`
2. `"sandbox_output"` entry — **absent entirely** (excluded, not scrubbed)
3. `"user_context"` — passes through unchanged
Green checkmark on the `user_context` line.
Narration (0:20–0:28):
> "The scrubber runs — before the snapshot reaches disk. API keys become `[REDACTED:API_KEY]`. Sandbox output is excluded entirely — it's not scrubbed, it's dropped. The agent's actual knowledge passes through unchanged."
**Callout text:**
`API key → [REDACTED:API_KEY]. Sandbox output → excluded entirely. Everything else → passes through.`
---
## Moment 3 — Pattern coverage (0:32–0:44)
**Terminal:**
```bash
python3 - << 'EOF'
from snapshot_scrub import scrub_content
test_cases = [
("OPENAI_API_KEY=sk-proj-123456abcdef", "env-var"),
("Bearer eyJhbGciOiJIUzI1NiJ9", "Bearer token"),
("sk-ant-abcd1234wxyz5678", "Anthropic key"),
("ghp_abc123def456ghi789jkl012mno", "GitHub PAT"),
("AKIAIOSFODNN7EXAMPLE", "AWS key"),
("YWJjZGVmZ2hpamtsbW5vcHFyc3R1dnp4eXpBQ0N", "high-entropy base64"),
("Everything looks fine", "clean content"),
]
for text, label in test_cases:
result = scrub_content(text)
print(f"{label:20s} → {result}")
EOF
```
**Terminal output:**
```
env-var → [REDACTED:API_KEY]
Bearer token → [REDACTED:BEARER_TOKEN]
Anthropic key → [REDACTED:SK_TOKEN]
GitHub PAT → [REDACTED:GITHUB_PAT]
AWS key → [REDACTED:AWS_ACCESS_KEY]
high-entropy base64 → [REDACTED:BASE64_BLOB]
clean content → Everything looks fine
```
**Camera:** Scroll through all 7 patterns. Hold 2s on the clean content line — no redaction.
Narration (0:34–0:42):
> "The scrubber catches seven secret patterns — API keys, Bearer tokens, GitHub PATs, AWS keys, Cloudflare tokens, high-entropy blobs. Clean content passes through unaltered."
---
## Moment 4 — Real-world scenario (0:44–0:54)
**Cut to:** Browser — Molecule AI canvas. Workspace `pm-agent` shows `[HIBERNATING]`.
**Terminal:**
```bash
# Workspace hibernating — scrubber runs automatically
curl -s -X POST "$PLATFORM/workspaces/ws-pm-001/hibernate" \
-H "Authorization: Bearer $AGENT_TOKEN"
```
**Terminal output:**
```
{"status": "hibernating", "snapshot_id": "snap-xyz-789", "scrubbed": true}
```
**Camera:** Focus on `"scrubbed": true`. Green highlight ring `#22C55E`. Hold 1.5s.
Narration (0:46–0:52):
> "When the workspace hibernates, the scrubber runs automatically — before the snapshot touches disk. The result is marked `scrubbed: true`. Admins can trust that snapshots are safe."
---
## Close (0:54–1:00)
**Terminal clean frame.** Cursor at prompt.
Narration (0:54–0:58):
> "Snapshot secret scrubber — API keys, Bearer tokens, sandbox output, all handled before hibernate. Molecule AI writes only what should be written."
**End card:**
```
Snapshot Secret Scrubber
workspace/lib/snapshot_scrub.py — molecule-core#977
```
**Fade to black.**
---
## Production Spec
| Spec | Value |
|------|-------|
| Terminal theme | Dark, SF Mono 14pt / JetBrains Mono 13pt |
| Camera | Screenflow / Camtasia, 1440×900 → 1080p export |
| JSON output | `jq --monochrome-output` |
| Callout highlight | Amber ring `#E8A000`, 1s fade-in/out |
| Red alert | Red underline `#EF4444` on raw secret lines in Moment 1 |
| Green success | Green ring `#22C55E` on `"scrubbed": true` in Moment 4 |
| VO voice | en-US-AriaNeural (consistent across all 4 storyboards) |
| Music | None |
| Playback speed | Moments 1–3 at 2x for terminal typing effect |
| Type-in animation | Realistic cursor blink |