AI Incident Response Copilot

A portfolio-ready, multi-agent system that investigates simulated cloud incidents and keeps humans in control of remediation.

Why this project

The copilot demonstrates the operating patterns required for production AI in cloud operations: specialist-agent orchestration, tool boundaries, role-based access, approval gates, and tamper-evident accountability. Its remediations are deliberately simulated, making the full workflow safe to explore.

Related MCP server: Incident Triage MCP

Architecture

flowchart LR
  U["Analyst / Commander"] -->|JWT| API["HTTP API"]
  API --> T["Triage agent"]
  T --> L["Log-analysis agent"]
  T --> M["Metrics agent"]
  T --> R["Runbook agent"]
  L & M & R --> E["Evidence + recommendation"]
  E --> H{"Human approval"}
  H -->|Approved| X["Simulated remediation tool"]
  H -->|Rejected| N["No action"]
  API -.-> A[("Hash-chained audit log")]
  MCP["MCP client"] --> TOOLS["Incident MCP server"]
  TOOLS --> T

Included scenarios

Quick start

Requires Node.js 20 or later; there are no third-party runtime dependencies.

npm start

Open http://localhost:3000. The dashboard uses these intentionally public demo identities:

• analyst / analyst-demo — investigate incidents

• commander / commander-demo — investigate and approve/reject remediation

• auditor / auditor-demo — inspect audit data via the API

For anything beyond local demonstration, set a strong JWT_SECRET and connect a real OAuth2/OIDC identity provider. Demo passwords are not production authentication.

API

TOKEN=$(curl -s http://localhost:3000/api/auth/token \
  -H 'content-type: application/json' \
  -d '{"username":"analyst","password":"analyst-demo"}' |
  node -pe 'JSON.parse(fs.readFileSync(0)).access_token')

curl http://localhost:3000/api/incidents -H "authorization: Bearer $TOKEN"
curl http://localhost:3000/api/investigations \
  -H "authorization: Bearer $TOKEN" -H 'content-type: application/json' \
  -d '{"incidentId":"INC-1001"}'

MCP server

The newline-delimited JSON-RPC server exposes list_incidents, investigate_incident, and list_approvals:

npm run mcp

Example MCP client configuration:

{
  "mcpServers": {
    "incident-copilot": {
      "command": "node",
      "args": ["/absolute/path/to/ai-incident-response-copilot/src/mcp-server.js"]
    }
  }
}

Read-only MCP tools intentionally cannot approve or execute remediation. Approval remains behind the commander-only HTTP boundary.

Security model

• HMAC-SHA256 JWT verification with expiration checks

• RBAC roles: analyst, commander, and auditor

• Explicit pending approval object for every proposed remediation

• Commander-only approve/reject endpoints

• No direct remediation method exposed over MCP

• Hash-chained JSONL audit events for authentication, agents, tools, approvals, and execution

• UI output escaping and request size limits

This is a demonstration architecture, not a production control plane. Production hardening should add an external OIDC provider, asymmetric JWT verification/JWKS, durable transactional storage, secret management, rate limiting, policy-as-code, and real cloud APIs with least-privilege service identities.

Development

npm test
npm run check
docker compose up --build

License

MIT