AI
Designing AI agents for production, not demos
The architecture, evaluation and operational controls that turn an agent prototype into reliable software.
Production software improves when teams make the operating assumptions visible, test the risky decisions early and preserve room for change.
Engineering review
Questions to answer before delivery
Can the team state the agent’s bounded job and completion criteria?
Does every tool define permissions, validation, timeout and recovery behavior?
Can a reviewer trace which context and evidence shaped a consequential decision?
Does the evaluation set include tool failures, ambiguous requests and human hand-offs?
Are latency, cost, quality and escalation visible together in production?
Start with the workflow, not the model
A useful agent begins with a bounded job, clear completion criteria and an honest map of the systems and judgment involved. The model is one component inside that operating design. A team should be able to describe what starts the job, which evidence is required, which actions are permitted and what makes the work complete before discussing prompts or frameworks.
Treat tools and permissions as product architecture
Every action needs an explicit interface, permission boundary, failure path and recovery behavior. High-risk or irreversible actions should introduce review or confirmation rather than rely on confidence alone. Typed inputs, idempotency keys and audit records are more durable controls than asking a model to be careful.
Build evaluation into delivery
Test representative tasks, edge cases, tool failures and hand-off behavior before release. Production traces and user feedback should extend that evaluation set as the agent encounters new conditions. Evaluate the complete outcome—evidence selection, decision, action and escalation—not only the final sentence.
Operate the whole system
Reliability depends on context quality, application code, integrations, latency, cost, observability and human support—not only model performance. Ownership must cover the complete system. A model update, source-data change or tool outage should be diagnosable without reconstructing the task from disconnected logs.