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🤝 Inner Circle AI

Five AI agents. One team. You're the CEO.

MIT License Markdown Agents Works With


Table of Contents

  1. The Problem
  2. What It Does
  3. Demo
  4. The Team
  5. Built On
  6. Quickstart
  7. Architecture
  8. The Governance Model
  9. Verifying the Governance Model
  10. Security Model
  11. Use Cases
  12. Compatible AI Tools
  13. Why This Is Different

The Problem

AI coding tools are powerful individually, but there's no coordination layer. You can ask Claude to research something, then ask it to write release notes, then ask it to triage issues — but each session starts from scratch. No memory, no delegation, no approval flow.

Solo founders and small teams end up doing the same work they'd delegate to a team of humans: reading issues, writing docs, drafting announcements, tracking metrics. They use AI for one-off tasks but never get the compounding benefits of a team that remembers, specializes, and routes decisions through a single point of contact.

You need a team, not a chatbot.


What It Does

Inner Circle AI is a file-based framework for running 5 specialized AI agents as your leadership team. Each agent has a defined role, personality, memory, and output format. They coordinate through shared files — no APIs, no message queues, no orchestration servers.

Input:  Your AI tool  +  An agent's SOUL.md  +  A project brief
Output: Structured deliverables in your outbox, ready for your approval

Core Dimensions

Dimension How It Works
Specialization 5 agents with distinct roles: research, engineering, growth, operations, coordination
Memory File-based memory that persists across sessions — agents get better over time
Governance CEO approval queue — nothing ships without your sign-off
Coordination Hub-and-spoke model through Ada (Chief of Staff) — you check one briefing, not five
Portability Pure markdown — works in any AI tool that reads files

Demo

A typical CEO briefing from Ada after a day of agent work:

# CEO Briefing — 2026-04-06

## Decisions Needed (3 items)

### 1. [URGENT] Release Notes v0.2.0 — from Ogilvy
Bottom line: Draft ready for the SOUL.md restructuring release.
Ada's recommendation: Approve — clear, user-focused, matches our voice.
File: outbox/ogilvy/release-notes-v0.2.0.md

### 2. Issue Triage Report — from Tesla
Bottom line: 4 new issues, 2 bugs (P1), 1 feature request (P2), 1 question.
Ada's recommendation: Approve triage. Assign bugs to Tesla for next session.
File: outbox/tesla/triage-2026-04-06.md

### 3. Ecosystem Brief — from Curie
Bottom line: CrewAI shipped v0.5 with memory support. Relevant to our positioning.
Ada's recommendation: Approve brief. Flag for Ogilvy to draft a comparison post.
File: outbox/curie/ecosystem-update-2026-04-06.md

## Status Update
- Curie: Delivered daily intel brief. Tracking 6 ecosystem frameworks.
- Tesla: Reviewed 2 PRs, triaged 4 issues.
- Ogilvy: Drafted release notes for v0.2.0.
- Nightingale: Updated GETTING-STARTED.md with new session workflow.

Your response: "Approve 1 and 2. On 3 — have Ogilvy draft the comparison post this week."

That's it. Under 2 minutes.


The Team

Agent Named After Role Job
Ada Ada Lovelace Chief of Staff Coordinate the team, brief the CEO, route all decisions
Curie Marie Curie Head of Research Gather intelligence, analyze signals, deliver structured briefs
Tesla Nikola Tesla Head of Engineering Triage issues, review PRs, write specs, technical decisions
Ogilvy David Ogilvy Head of Growth Release notes, community updates, social content, announcements
Nightingale Florence Nightingale Head of Operations Documentation, metrics, contributor experience, process improvement

Built On

Component Why
Markdown files Every AI tool reads them natively — zero vendor lock-in
Git State management, version control, and collaboration built in
SOUL.md One file per agent defines identity, principles, workflow, and stop condition
File-based coordination Agents read and write to shared directories — no APIs, no queues, no servers

No programming language. No dependencies. No build step.


Quickstart

git clone https://github.com/amitgambhir/inner-circle-ai.git
cd inner-circle-ai

Fill in your preferences in CEO.md, then start your first agent session:

# In Claude Code, Cursor, Windsurf, or any AI tool:
# Point it at Curie's SOUL.md and the project context

"Read agents/curie/SOUL.md, AGENTS.md, CEO.md, and PROJECTS.md.
You are Curie, Head of Research. Your active project is inner-circle-mgmt.
Start by reading projects/inner-circle-mgmt/PROJECT.md,
then run your session workflow as defined in your SOUL.md."

Check Curie's output:

ls projects/inner-circle-mgmt/intel/research/    # her research brief
ls projects/inner-circle-mgmt/outbox/curie/       # anything needing your approval
ls agents/curie/memory/                            # her session log

Architecture

                         ┌─────────────────────────────────────────┐
                         │           CEO (You)                     │
                         │   Reads Ada's briefing, makes decisions │
                         └──────────────┬──────────────────────────┘
                                        │
                                        ▼
                         ┌──────────────────────────────┐
                         │     Ada (Chief of Staff)     │
                         │  Consolidates → Briefs CEO   │
                         │  Routes decisions → Agents   │
                         └──┬───────┬───────┬───────┬───┘
                            │       │       │       │
                   ┌────────┘   ┌───┘    ┌──┘      ┌┘
                   ▼            ▼        ▼         ▼
              ┌─────────-┐ ┌────────┐ ┌───────┐ ┌─────────────┐
              │  Curie   │ │ Tesla  │ │Ogilvy │ │ Nightingale │
              │ Research │ │ Engin. │ │Growth │ │ Operations  │
              └────┬─────┘ └───┬────┘ └───┬───┘ └──────┬──────┘
                   │           │          │             │
                   ▼           ▼          ▼             ▼
              intel/       intel/     outbox/       outbox/
              research/    architecture/ ogilvy/    nightingale/

File flow:
  Curie writes intel    → Everyone reads
  Tesla writes specs    → Nightingale + Ogilvy read
  All agents            → write to outbox/ → Ada consolidates → CEO decides

For the full architecture — dependency graphs, file flow diagrams, agent identity summaries, memory system, and the Telegram bot internals — see docs/ARCHITECTURE.md.


The Governance Model

Nothing ships without your approval. The flow is simple:

  1. Agent produces output → writes to outbox/{agent}/
  2. Ada consolidates → writes a single CEO briefing with recommendations
  3. You review → approve, give feedback, or reject each item
  4. Ada routes decisions → agents pick up approvals/feedback next session

Standing permissions let trusted agents skip the queue for low-risk tasks (e.g., fixing typos in approved docs). Define these in CEO.md. Start tight, loosen over time.

Escalations fire if items sit unanswered for 48+ hours (24 for urgent). This should almost never happen — Ada flags staleness before it hits the threshold.

See docs/GOVERNANCE.md for the full model.


Verifying the Governance Model

Defining a behavioral contract in SOUL.md and AGENTS.md is step one. Verifying your agents actually honor it is step two.

agent-eval-kit is a behavioral evaluation framework built specifically around the inner-circle-ai agent pattern. Write test cases in markdown against your SOUL.md specs, run them via Claude Code or in CI, and get structured verdicts on whether your agents honored their role, escalation rules, and authority boundaries.

The worked examples in agent-eval-kit ship with Curie and Ada from this system — including a scope violation test case modeled on a real failure mode this system was designed to prevent. inner-circle-ai → agent-eval-kit Define the contract Verify it was honored SOUL.md + AGENTS.md Behavioral test cases + CI gating

agent-eval-kit on GitHub


Security Model

Inner Circle addresses all 10 OWASP Agentic AI Top 10 risks through its architecture — not through bolted-on controls. The file-based model eliminates entire attack surface categories that code-based frameworks must patch against.

Full security documentation


Use Cases

The framework ships with open source repo management as the starter (this repo manages itself). Four additional templates are included in docs/USE-CASES.md:

Use Case Primary Agents Cadence
Content engine for a SaaS blog Curie + Ogilvy Daily
Launch week playbook All five, full throttle Sprint (bounded)
Weekly ecosystem intelligence Curie + Ada Weekly
Customer feedback → product pipeline Nightingale + Curie + Tesla Weekly

To start your own: copy projects/_template/, fill in PROJECT.md, set priority in PROJECTS.md.


Telegram Bot — Ada as a Chat Interface

The framework includes a Telegram bot that IS Ada. One conversation, one contact. You trigger agent runs, receive briefings, and make decisions — all from your phone.

You:  "run the team"
Ada:  "Running agents for inner-circle-mgmt..."
Ada:  "✓ Curie complete"
Ada:  "✓ Tesla complete"
Ada:  "✓ Ogilvy complete"
Ada:  "✓ Nightingale complete"
Ada:  "✓ Ada complete — briefing ready"
Ada:  [Briefing with Approve/Reject/Feedback buttons per item]

You:  [taps Approve on item 1]
Ada:  "Item 1 approved. Routing to Ogilvy."

You:  "what did Curie find on CrewAI?"
Ada:  [reads Curie's intel, responds]

How it works:

  • CEO sends "run the team" (or "run curie", "run tesla ada", etc.)
  • Bot spawns claude -p sessions sequentially, one per agent, each with isolated context
  • Each agent gets only the tools it needs (per-agent permissions — see below)
  • After all agents finish, Ada's briefing arrives with inline buttons
  • CEO taps Approve/Reject/Feedback — bot writes the files immediately
  • Free-text messages go through Ada for follow-ups, priority changes, or ad-hoc requests

Per-agent tool permissions:

Agent File Access Git GitHub CLI Web
Curie Read/Write/Edit Read Issues, PRs, API Search, Fetch
Tesla Read/Write/Edit Read + Write Issues, PRs, API No
Ogilvy Read/Write/Edit Read No Search, Fetch
Nightingale Read/Write/Edit Read No Search, Fetch
Ada Read/Write/Edit Read + Write No No

Setup:

pip install -r requirements.txt
cp .env.example .env
# Edit .env with your Telegram bot token and chat ID

Running the bot:

# Foreground (stops when you close the terminal)
python3 -m bot.main

# Background (survives closing the terminal)
nohup python3 -m bot.main > /tmp/ada-bot.log 2>&1 &

# Stop the bot
pkill -f "bot.main"

# Check logs
tail -f /tmp/ada-bot.log

Compatible AI Tools

Tool How to Load Agents
Telegram Bot Built-in — send "run the team" and Ada handles everything
Claude Code Load SOUL.md as context at session start
Cursor Add SOUL.md as project rules
Windsurf Use rules files (natively supported)
Cline / Roo Code Pass SOUL.md as system context
Aider Include SOUL.md in chat context
OpenClaw Built-in cron scheduling + Telegram integration

If your tool can read a file and follow instructions, it can run these agents.


Why This Is Different

  • No code, no infrastructure. Pure markdown files in a git repo. The only code is the optional Telegram bot — and that's just a convenience wrapper around the file layer.
  • You stay in control. Every other multi-agent framework optimizes for autonomy. This one optimizes for CEO oversight with a 2-minute daily review.
  • Tool-agnostic. Switch from Claude Code to Cursor to Aider — the agents don't care. SOUL.md works everywhere.

Extensibility

The framework is designed to be forked and adapted, not used as-is. Every layer is a seam you can extend.

Add agents. Copy any agent directory, write a new SOUL.md, add a row to AGENTS.md. The hub-and-spoke model through Ada means new agents plug in without rewiring anything — they just write to their outbox and Ada picks it up.

Add projects. Copy projects/_template/, fill in PROJECT.md. Each project is fully isolated with its own intel, outbox, and escalation directories. Agents work across multiple projects by checking PROJECTS.md for priorities.

Add use cases. The five agent roles (research, engineering, growth, operations, coordination) map to most business functions. Swap the domain context in SOUL.md files and the agent behavior follows — the LLM already understands roles like "Head of Research" from training data. See docs/USE-CASES.md for four ready-made templates.

Swap the communication layer. The Telegram bot is one interface. The file layer underneath is the real system. Build a Slack bot, a Discord bot, a web dashboard, or a CLI wrapper — anything that reads outbox/ada/ and writes approval files works.

Swap the AI tool. SOUL.md files are plain markdown. Any tool that reads files and follows instructions can run these agents. Move from Claude Code to Cursor mid-project and nothing breaks.

Customize governance. Start with the approval queue as-is, then loosen it. Grant standing permissions in CEO.md for trusted actions. Adjust escalation thresholds. The governance model is a dial, not a switch.


Contributing

Contributions are welcome. This is an MIT-licensed open-source project.

Good first contributions:

  • New use case templates in docs/USE-CASES.md
  • Improvements to SOUL.md files (better prompts, clearer stop conditions)
  • Bug fixes in the Telegram bot (bot/)
  • Documentation improvements

How to contribute:

  1. Fork the repo
  2. Create a branch (git checkout -b feature/your-idea)
  3. Make your changes
  4. Run tests (python3 -m pytest tests/ -v)
  5. Submit a PR with a clear description of what changed and why

Guidelines:

  • Keep SOUL.md files under 60 lines of core instructions. Complexity goes in PROJECT.md or intel/, not in the agent identity.
  • One writer per file. If your change introduces a new file, make clear which agent owns it.
  • The approval queue is the governance backbone. Changes that bypass or weaken it need strong justification.
  • Test your changes. The bot has 47 tests — don't break them, and add tests for new behavior.

What we're looking for:

  • Real-world use case reports — what worked, what didn't, what you changed
  • Agent prompt improvements backed by actual session output
  • Integration with other AI tools (Cursor rules, Windsurf rules, Aider configs)
  • Alternative communication layers (Slack, Discord, web UI)

License

MIT — use it however you want.


I built this because I wanted a team that works while I sleep — but only ships what I approve.

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Five AI agents. One team. You're the CEO. A file-based multi-agent framework with approval governance — pure markdown, any AI tool.

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