Production AI agents
Reliable AI agents that finish real work.
A useful agent is not measured by how convincing its reasoning sounds. It is measured by whether it reaches a defined outcome, leaves evidence, handles failure, and knows when authority must return to a person.
The execution loop is the product
Production agents need an explicit plan → act → observe → verify loop. Every action changes external state or produces evidence, and the next decision must be grounded in that observation rather than the model’s original intention.
- Typed tool contracts and explicit error shapes
- Bounded retries with changed strategy
- Evidence-based completion criteria
- Human approval before high-impact actions
Memory needs boundaries
Working context, episodic execution history, durable user knowledge, and semantic retrieval solve different problems. Keeping them separate controls cost, improves relevance, and makes privacy and deletion policies enforceable.
- Short-lived working state
- Task ledger and checkpoints
- Durable facts with provenance
- Expiry, consent, and deletion controls
Evaluation must happen at task level
Model quality is only one dependency. Track whether the full task completed, whether verification passed, how many retries occurred, which tools failed, how much human intervention was required, and what the run cost.