Diagnostic-loop workflow for improving an underperforming AI agent. Starts with baseline measurement using rubrics and LLM-as-judge scoring (agent-evaluation), then diagnoses architectural problems like task-model mismatch and pipeline bottlenecks (agent-project-development), redesigns the agent topology — supervisor, swarm, or specialized agents (multi-agent-patterns), adds the right memory frame
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Evaluate and Improve Agent
The most dangerous state for an agent is "it mostly works." Teams stop measuring, stop improving, and slowly accumulate failure modes that nobody notices until a customer does. This workflow breaks that plateau by forcing a baseline measurement before any changes and a comparison measurement after.
Agent improvement without measurement is hope. You change the prompt, the tools, the architecture — and you feel better about it, but you don't know if it's better. Th
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