Engineering
Halo
Shared records and useful actions for a person and an AI agent.
Halo is an experiment in giving a person and an AI agent access to the same records and tools: logging activity, tracking work and preparing briefings.
It also explores a harder question: how much machinery does that collaboration actually need? The project includes a distributed advisor system, but its complexity needs to justify itself against a simpler arrangement using the same tools and data.
Personal infrastructure experiment, not a packaged customer product. A smaller architecture is a direction to evaluate, not a completed redesign or a demonstrated improvement.
Technical detail
Based on the published tool-layer architecture, with the tracking write and event-publication path checked against the current source. The running cluster and comparative performance have not been tested for this account.
One operational surface
Modules for activities such as work tracking, briefing and orchestration expose explicit CLI commands. A human and an agent reach the same tool contract, avoiding a separate agent-only interface with different behaviour.
Local records and event delivery
Modules own isolated data stores, while an event stream supplies consumers with updates for their queryable views. Local writes and event delivery are separate operations. In the tracking path, a record is saved before publication is attempted, and the command ignores the publisher’s failure result. A local save can therefore succeed without the event reaching the stream. Replay alone cannot recover an event that was never published; the current implementation does not guarantee complete recovery from the event stream.
What would justify the machinery?
The records and domain tools can be assessed separately from the distributed advisor system that uses them. A useful next comparison would give one capable agent the same records and tools, then compare accepted outcomes, corrections, missed actions, operating cost and maintenance time on real tasks. That comparison has not yet established whether the fleet earns its additional complexity. Simplification remains a direction to test, not a claimed result.
Operating scope
The published implementation uses Python, SQLite, NATS JetStream, Docker and Kubernetes. It is personal infrastructure with distributed-runtime concerns, rather than a packaged client offering or the client-facing Human OS platform.
Stack / components
Python, SQLite, NATS, Docker, Kubernetes