Why build a local lab?
A dedicated lab lets you explore AI agent behavior without sending test data to external services. Your starting point can include an agent runtime, a local model, a test MCP server and a separate identity provider.
A reference architecture
- Inference layer: Run a locally hosted model using Ollama.
- Agent layer: Configure one sandboxed agent for each test use case.
- Identity layer: Use a test identity provider and short-lived credentials.
- Tool boundary: Implement an MCP server with a strict allowlist.
- Observability: Export tool and authentication events to a central log.
Security controls to test
| Test | Expected outcome |
|---|---|
| Unauthorized tool call | Denied and logged |
| Cross-agent identity reuse | Rejected |
| Malicious retrieved content | Does not override the agent's policy |
| Tool configuration drift | Detected during the next inventory scan |
Next steps
Start with small, isolated test agents. Record an evidence trail for each test and keep credentials out of public repositories. Publish sanitized configuration samples once they have been reviewed.
These tutorials are starter editorial examples; test commands and update all claims before public release.