Agent infrastructure · Measured Intuition
Giving AI agents shared context and verifiable handoffs
Built agent harnesses and a hosted plugin to carry work across conversations, preserve decisions and check whether implementation still matched agreed requirements.
The operating problem
When work spans multiple agents and conversations, decisions fragment. Specialists act on different assumptions, context gets repeated and a successful test can become outdated after the next change.
The challenge was to preserve continuity while making completion claims inspectable. Adding more agents increases the need for shared context, clear handoffs and current evidence.
What was built
Connected specialist agents through shared project context, reusable skills and explicit handoffs. Built a hosted MCP plugin with OAuth and SQL-backed persistence so work could be saved, retrieved and continued across conversations.
A Python/SQLite verification layer links decisions to requirements, code, tests and client-facing explanations. Checks bind to specific file versions. Relevant changes invalidate earlier evidence until it has been reviewed against the new state.
Separate interpretation from verification
- Agents interpret requests, investigate problems and propose changes.
- Persistent records preserve decisions, work state and supporting evidence.
- Code checks versions, duplicate writes and whether verification remains current.
- Independent review assesses whether implementation satisfies the intended behavior.
This structure allows work to be distributed without treating an agent’s success message as sufficient proof of completion.
Measured retrieval result
A bounded evaluation returned 96.9% less data for a scoped retrieval than a full-state read: 3,513 bytes compared with 113,598. The relevant work and evidence remained included.
Resuming unchanged work preserved record IDs and versions. Hosted persistence and readback were demonstrated.
This is a retrieval-volume result from an evaluation fixture. It is not a claim of equivalent token savings, lower operating costs or faster production delivery.
Business significance and current boundary
The system addresses coordination overhead: repeated explanations, conflicting context and rechecking work after changes. It supplies infrastructure for longer-running delegation with traceability and review.
Unattended hosted acceptance and some platform behaviors remain incomplete. The verification layer checks declared scope and current evidence. It does not establish that every possible requirement has been discovered or that human supervision is unnecessary.
Discuss a workflow worth changing.
Start with the operating constraint, the current process and the business result that would justify changing it.