How do I use agent-orchestration?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@agent-orchestration Run the full pipeline to design and implement a dark mode toggle." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

agent-orchestration

Multi-agent orchestration runtime with operator-aware autonomy. Six specialized agents collaborate through a typed artifact pipeline, with autonomy levels dynamically adjusted based on operator cognitive state.

Architecture

┌──────────────────────────────────────────────────────────────┐ │ MCP Runtime (41 RPC methods) │ │ Sessions · Artifacts · Worktrees · Agent Execution · Merge │ └──────────────────────────┬───────────────────────────────────┘ │ ┌──────────────────────────┴───────────────────────────────────┐ │ Orchestration Pipeline │ │ POLYMATH → RESONANT → ARCHITECT → EXECUTOR → HORIZON │ │ (optional) │ └────────┬─────────────────────────────────┬───────────────────┘ │ │ ┌────────┴────────┐ ┌────────┴────────┐ │ MIND Engine │ │ BASIS Engine │ │ Operator State │ │ Context Score │ │ Machine (FSM) │ │ Calculator │ │ │ │ │ │ fresh → mid → │ │ Domains × │ │ faded ↔ high_ │ │ Weights → │ │ pressure │ │ Caps/Penalties → │ │ │ │ CSS (0-100) │ │ → Autonomy │ │ → Tier A/B/C │ │ L0-L3 │ │ │ └─────────────────┘ └──────────────────┘

How It Works

Task enters the pipeline via the MCP runtime
MIND engine evaluates operator state (session duration, decision count, error rate, fatigue signals) and assigns an autonomy level (L0–L3)
BASIS engine calculates a Context Sufficiency Score across weighted domains — determines whether enough context exists to proceed
Agents execute in sequence, each producing a typed artifact:

Agent	Role	Artifact
Polymath	Divergent exploration, hypothesis generation	TaskMap
Resonant	Evidence validation, risk assessment	EvidencePack
Architect	Design specification from evidence	DesignSpec
Executor	Implementation in isolated worktrees	PatchSet
Horizon	Ship gating (5 gates), release decision	ShipDecision
Alchemist	Creative reframing when work is stuck	OptionsSet

Permission matrix enforces read/write guardrails per agent per autonomy level. Only Executor can write code, and only at L2+.

Project Structure

slate/ ├── agents/ # 6 agent runners (polymath, resonant, architect, executor, horizon, alchemist) ├── engines/ │ ├── basis/ # Context Sufficiency Score calculator │ └── mind/ # Operator state machine + autonomy resolver ├── libs/ │ ├── context-schema/ # Zod artifact schemas (TaskMap, EvidencePack, DesignSpec, etc.) │ ├── context-loader/ # Markdown + YAML context loading │ ├── orchestration/ # Pipeline coordination + session management │ └── llm-provider/ # LLM router (OpenAI primary, Anthropic fallback) ├── apps/ │ └── mcp-runtime/ # JSON-RPC server (41 methods) ├── tests/ # E2E test suites └── tools/ # Agent manifests, contracts, templates

Tech Stack

Language: TypeScript 5.4
Build: Nx monorepo
Schema Validation: Zod
Testing: Vitest
LLM Integration: OpenAI SDK, Anthropic SDK
Runtime: Node.js, vite-node
Protocol: MCP (Model Context Protocol) via JSON-RPC

Run

# Install dependencies npm install # Build all packages npx nx run-many --target=build # Run tests npx nx run-many --target=test # Start MCP runtime npx nx run slate-runtime:serve

Status

Working prototype, actively developed. Core pipeline, all 6 agents, both engines, and the MCP runtime are functional with passing test suites. Not production-hardened — session persistence is file-based, no monitoring/observability layer yet.