Production AI: Designing
the improvement loop

Let agent boundaries emerge through iteration

The guide’s line, “Multi-agent architectures emerge through iteration,” resonated with me. It describes Ask DAVID’s architecture evolving as earlier versions exposed limitations. ^[1]

Building small and testing quickly helps reveal where a useful boundary belongs. I would:

• Start with an observed failure. If source context disappears during analysis, investigate that specific loss.
• Try the smallest useful fix. Clearer inputs, a better tool, or a simpler workflow may be enough.
• Make specialization earn its place. Add a role when it improves a measured outcome or establishes a necessary control.

For every specialist role, I would define:

• Responsibility: the task it owns and the tools it may use.
• Handoff: the evidence, assumptions, and unresolved questions the next step needs.
• Failure path: how it recovers, stops, or escalates.

Modularity can support transparency, but every additional handoff still needs to be tested.

Make traces usable for the people improving the product

The guide emphasizes inspecting prompts, retrieved context, tool calls, and outputs to diagnose agent behavior. ^[1] My next question is how easily people can find and use that information.

• Start from the problem. Link a failed answer or stakeholder comment directly to its run.
• Find patterns quickly. Filter by task, agent, version, and failure type.
• Open the relevant step. Highlight retries, missing evidence, and validation failures.

For me, the useful measure is how quickly a team can find the cause and verify a fix. I would also:

• Capture observable actions and supporting evidence.
• Apply appropriate access, redaction, and retention controls.
• Treat the trace as an aid to review, not proof that the answer is correct.

Make evaluation easier to operate

This is the pain point I keep coming back to: how do we organize tests, results, and feedback so that comparing agent versions feels as approachable as comparing a UI experiment?

The guide describes Chime using concrete examples and legal-reviewer annotations to align evaluation across teams. It also reports longer evaluation cycles for Ask DAVID despite faster development. ^[1]

I would borrow the discipline of A/B testing:

• A clear hypothesis: state what the proposed change should improve.
• Comparable conditions: begin with paired offline tests on the same versioned cases, including held-out cases.
• Agreed measures: separate representative tasks, edge cases, and tool failures.
• A visible decision record: explain what changed and why it should move forward.

• Repeat cases where variability matters. Compare the spread of results, not only the average.
• Inspect serious regressions separately. An overall gain may hide a consequential failure.
• Control any later live experiment. Match safeguards to the actions and risks involved.

A shared scorecard, with task-specific measures

I would define a rubric, thresholds, and reviewer for each task. These dimensions are proposed starting points, not results from the guide.

• Keep critical failures separate from aggregate quality. Better wording or lower cost should not outweigh an unauthorized action.
• Check components and the complete workflow. A good individual score does not establish a good final answer.

Connect feedback to the version it can improve

My ideal evaluation record would connect:

• The case: test input, expected behavior, and dataset version.
• The run: trace, result, and agent configuration.
• The environment: model, prompts, tools, and retrieval data.
• The review: evaluator rubric, stakeholder comments, and final decision.

• Structure stakeholder feedback. Capture category, severity, evidence, and suggested correction.
• Preserve disagreements. Calibrate the rubric and record how human or automated-review disagreements were resolved.
• Measure critic and revision loops. Check whether they fix verified errors, introduce new ones, or add delay.
• Bound retries and define escalation. Keep the improvement process controllable.

I want a team to move easily from a version comparison to the changed cases, then to their traces and feedback.

My product takeaway

My interest is in making improvement a repeatable part of the product.

I want the process of improving an agent to be as thoughtfully designed as the experience of using it.