Typed handoffs
Designed explicit state and capability boundaries between planning, execution, and review.
Infrastructure system
A modular, backend-neutral orchestration layer for planning, routing, execution evidence, QA, safety, and local-first model use.
Designed explicit state and capability boundaries between planning, execution, and review.
Captured receipts and QA artifacts so runs can be inspected after completion.
Separated task intent from provider choice to support private, swappable runtimes.
The challenge
Local AI systems often accumulate models and scripts faster than operational trust. The goal was to make capability, routing, evidence, and safety explicit across a modular platform.
Miguel used typed state, deterministic selection rules, an action boundary, and receipt-oriented QA to make multi-step work observable. The public replay is sanitized and does not connect to the private platform.
Sanitized architecture replay
No live models, services, addresses, or private topology.
Implemented capability
Turns an objective into explicit, bounded steps before tools are used.
Typed plans and checkpoint artifacts
Replay timeline
Create a private, source-grounded analytical brief.
Behind the build
Sanitized architecture, capability-ledger, routing, and QA artifacts from the local AI platform workspace.
Intent and capability requirements are evaluated before choosing a compatible runtime, keeping application logic independent from any one local model server.
Plans, actions, outputs, reviews, and exceptions create a review trail rather than disappearing into an opaque chat transcript.
Completed, experimental, and planned capabilities are labeled separately. The explorer does not imply universal production activation.