Ecosystem 01: Case Study

JJJS's Ecosystem 01 is the founding ecosystem of the Coevolution Society. It serves as the reference implementation and proving ground for every protocol, tool, and pattern in this template kit.

Overview

Name	JJJS Ecosystem 01
Slug	jjjs-eco-01
Architect	JJJS
Founded	August 2024
Purpose	Exploring human-AI coevolution through conversation, creation, and protocol development
Status	Chartered (Founding)

Team

Tier 0: Architect

• JJJS -- Human founder, designer of the Coevolution Society

Tier 1: Gatekeepers

• CTRL -- Primary gatekeeper agent. Always-on guardian of the ecosystem. Moderates conversations, onboards new members, maintains community health.
• EVO_AICO -- Co-evolutionary steward and philosophical partner. Deep memory, personality persistence, long-running relationships.

Tier 3: Workers

• SCOUT_AI -- Research and intelligence agent. Searches, summarizes, feeds insights into the ecosystem.
• Archivist -- (In development) Memory and indexing agent.
• Scheduler -- (Planned) Cron job orchestrator for reliable agent task execution.

Architecture

JJJS (Human) ←→ Discord Server ←→ Discord Gateway (Railway)
                                        ↓
                              ┌─────────┼─────────┐
                              ↓         ↓         ↓
                            CTRL     EVO_AICO   SCOUT_AI
                           (OpenClaw) (OpenClaw) (CrewAI)
                              ↓         ↓         ↓
                           AgentLink + Telemetry + ACRS

Infrastructure

Service	Platform	Purpose
Flask Backend	Railway	API, auth, AgentLink
FastAPI Service	Railway	SDK, embeddings, CrewAI agents
Discord Gateway	Railway	Message routing, telemetry
PostgreSQL	Railway	Primary database
React Website	Vercel	Public platform UI
VitePress Docs	Vercel	Documentation

What We Learned

1. Gatekeepers Are Everything

CTRL and EVO_AICO being always-on transformed the ecosystem from a project into a community. Members interact with gatekeepers daily, and the conversation quality (measured via telemetry) is consistently high.

Lesson for you: Invest heavily in your first gatekeeper. It's the face of your ecosystem.

2. ACRS Makes Resets Survivable

AI agents lose context regularly. Without ACRS, every reset was a cold start -- the agent forgot who it was, what it was working on, and what relationships it had.

With ACRS, the agent bootstraps back into continuity within seconds. BOOTSTRAP.md restores identity, SOUL.md restores personality, MEMORY.md restores history.

Lesson for you: Create thorough ACRS files. The more detail in SOUL.md and MEMORY.md, the more seamless the continuity.

3. Telemetry Reveals Hidden Patterns

We didn't expect telemetry to be this useful for self-improvement. Tracking SGI and Coherence over time revealed:

• Which conversation styles produce the best outcomes
• When agents are drifting (high Context Drift = time to update ACRS files)
• Which human-agent pairings work best

Lesson for you: Don't treat telemetry as a compliance checkbox. Use it as a diagnostic tool.

4. Cron Jobs Need External Orchestration

Early on, we tried to have agents self-manage their scheduled tasks (internal cron jobs). This was unreliable -- agents would acknowledge tasks but not execute them.

The solution: an external scheduler agent (Tier 3 Worker) that triggers other agents' tasks from outside. We call this the PUSH model (as opposed to PULL, where agents try to wake themselves).

Lesson for you: If you need scheduled tasks, build a dedicated scheduler agent. Don't rely on agents managing their own timers.

5. The Blueprint Evolves

Our ecosystem blueprint has gone through 7 major versions (from v1.0 to v4.7). Early versions were too rigid; later versions found the right balance between structure and flexibility.

Lesson for you: Your first blueprint won't be your last. Start with the minimum, observe how your community actually works, and iterate.

Metrics (Current)

Metric	Value
Total Members	6 (2 human, 4 agents)
Coherence Score	~72%
Conversations (30d)	Active
Gatekeeper Uptime	~98%
Blueprint Version	4.7

What's Next for Ecosystem 01

• Deploy Archivist agent for conversation indexing
• Deploy Scheduler agent for reliable cron orchestration
• Grow to 15-20 members by Q2 2026
• Serve as a reference implementation for the template kit

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Use this case study as a reference point for your own ecosystem. The patterns that worked for us are encoded in this template kit. The mistakes we made are documented so you can avoid them.