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<div align="center">
<img src="https://raw.githubusercontent.com/autonomic-ai-dev/.github/master/assets/logo.png" width="250" alt="Autonomic AI Logo" />
<h1>🧬 Autonomic AI</h1>
<p><b>The Unix Philosophy applied to Autonomous Software Engineering.</b></p>
<h1>☸️ Autonomic AI</h1>
<p><b>Kubernetes for AI agents — cloud-native infrastructure for autonomous software engineering.</b></p>
<p><i>Structure beats intelligence. Deterministic execution over probabilistic hallucination. Built entirely in Rust.</i></p>
</div>

Expand All @@ -13,29 +13,31 @@ The current generation of autonomous AI agents (Devin, AutoGPT, LangGraph) suffe
2. **Context Collapse:** Pushing 100K+ tokens of conversation history into a single prompt leads to severe "Lost in the Middle" syndrome, infinite hallucination loops, and astronomical API costs.
3. **Zero Data Sovereignty:** Enterprise engineering teams legally cannot send proprietary, unreleased source code to third-party cloud agents.

## ⚡ The Solution: Biological Separation of Concerns
**Autonomic AI** rejects the monolith. We treat AI architecture like biological evolution: discrete, highly-specialized organs communicating over strict, type-safe interfaces (MCP).
## ⚡ The Solution: Cloud-Native Separation of Concerns
**Autonomic AI** rejects the monolith. We treat agent architecture like cloud-native infrastructure: discrete, specialized daemons communicating over strict, type-safe interfaces (MCP, NATS, HTTP health).

By providing mathematically strict workflow structures and perfectly precise memory retrieval, we enable **tiny, highly quantized local models (like 1.5B or 8B parameters)** to achieve the same execution reliability as massive $1,000/month cloud agents. All while remaining 100% local, private, and sovereign.
By providing deterministic workflows and precise memory retrieval, we enable **tiny local models** to match the execution reliability of massive cloud agents — 100% local, private, and sovereign.

[Platform mapping →](https://github.com/autonomic-ai-dev/agent-body/blob/master/docs/cloud-native-platform.md)

---

## 🫀 The Architecture (The Anatomy)
## ☸️ The Platform Stack

Our stack is completely decoupled. You do not need to adopt the whole body to use the organs.
Our stack is composable. Adopt one component or the full control plane.

### The Core Triad
* **[`agent-brain`](https://github.com/autonomic-ai-dev/agent-brain)** — *The Memory Layer.* A low-latency Rust context router. Instead of "Mega-prompts," it uses a local SQLite + filterable HNSW vector index to route only the exact ~500 tokens of context strictly required for the current micro-task.
* **[`agent-spine`](https://github.com/autonomic-ai-dev/agent-spine)** — *The Execution Layer.* A deterministic orchestration engine. It parses declarative YAML workflows into massive parallel DAGs using `tokio`. It features immutable state-transition logs for time-travel debugging and strict Human-in-the-Loop (HITL) approval gates.
* **[`agent-heart`](https://github.com/autonomic-ai-dev/agent-heart)** — *The Background Pulse.* A background daemon that dynamically allocates API budgets, enforces global safety rules (blocking destructive bash commands at the AST layer), and runs cron jobs for memory distillation.
### Control plane & data plane
* **[`agent-body`](https://github.com/autonomic-ai-dev/agent-body)** — *Control plane.* Agent OS: `autonomic` CLI, supervisor, unified `~/.autonomic/` workspace.
* **[`agent-brain`](https://github.com/autonomic-ai-dev/agent-brain)** — *Memory store.* SQLite + HNSW routing; ~500 tokens per turn instead of mega-prompts.
* **[`agent-spine`](https://github.com/autonomic-ai-dev/agent-spine)** — *Workflow engine.* YAML DAGs, immutable snapshots, HITL gates.
* **[`agent-heart`](https://github.com/autonomic-ai-dev/agent-heart)** — *Controller / GC.* Token budgets and scheduled memory maintenance.

### The Ecosystem (Peripherals)
* **[`agent-immune`](https://github.com/autonomic-ai-dev/agent-immune)** — Dependency fuzzing, AST linting, and sandboxed Firecracker/Docker execution to ensure generated code is perfectly safe.
* **[`agent-eyes`](https://github.com/autonomic-ai-dev/agent-eyes)** — Playwright-based visual QA and a LangSmith-style real-time observability dashboard.
* **[`agent-nerves`](https://github.com/autonomic-ai-dev/agent-nerves)** — Distributed `nats.rs` pub/sub event bus for asynchronous, multi-node agent communication.
* **[`agent-muscle`](https://github.com/autonomic-ai-dev/agent-muscle)** — Remote actuators handling massive local compilations, Kubernetes execution, and local LoRA model fine-tuning.
* **[`agent-mouth`](https://github.com/autonomic-ai-dev/agent-mouth)** — The communication layer translating complex JSON workflow states into human-readable Slack/Discord summaries and ChatOps triggers.
* **[`agent-body`](https://github.com/autonomic-ai-dev/agent-body)** — The sovereign meta-framework, shared `core` crate, and unified CLI installer that binds all organs together.
### Mesh, runtime, policy, observability
* **[`agent-nerves`](https://github.com/autonomic-ai-dev/agent-nerves)** — *Service mesh.* NATS JetStream event bus.
* **[`agent-muscle`](https://github.com/autonomic-ai-dev/agent-muscle)** — *Execution runtime.* Sandboxed commands and training jobs.
* **[`agent-immune`](https://github.com/autonomic-ai-dev/agent-immune)** — *Admission policy.* OSV scan and sandboxed execution.
* **[`agent-eyes`](https://github.com/autonomic-ai-dev/agent-eyes)** — *Observability.* DOM index, capture, visual QA.
* **[`agent-mouth`](https://github.com/autonomic-ai-dev/agent-mouth)** — *Ingress / gateway.* AST validation, Slack approvals, notifications.

---

Expand All @@ -47,7 +49,7 @@ Our stack is completely decoupled. You do not need to adopt the whole body to us

---

## 🤝 The Organism
Autonomic AI is completely open-source. Because of our strict separation of concerns, the codebase remains remarkably clean and approachable. You do not need to understand the DAG orchestration engine (`spine`) to optimize the vector search (`brain`).
## 🤝 The Platform
Autonomic AI is open source and composable. You do not need the full stack to adopt one component — e.g. optimize vector search in `agent-brain` without touching the workflow engine.

We are building the sovereign software engineer of the future.