agent-driven-design

Design production-grade LLM agent systems by applying a structured framework that separates probabilistic reasoning in the Model from deterministic control flow in the Harness. This approach ensures maintainable, observable, and performant multi-agent deployments.

The best agent topology for multi-agent code review is the orchestrator plus specialist pattern. This topology defines specific context boundaries for each agent, coordinating specialized tasks efficiently while maintaining clear separation of concerns.

Poor performance and high latency in LLM agent systems often stem from misplaced logic between the Model and Harness components or poor topology choices. Diagnosing these issues requires production observability to track tool call success rates and identify bottlenecks.

Implement LLM agent observability by setting up monitoring to track tool call success rates, cost optimization, and latency. This production-grade strategy helps diagnose poor performance and avoid anti-patterns in single and multi-agent deployments.

Choose fine-tuning over prompting for LLM agent architecture when evaluating tradeoffs between cost, latency, and reasoning capabilities. A structured framework helps determine the correct logic placement and evaluation strategy for optimal performance.

Sequential pipelines and reflection loops are distinct agent topology patterns tailored to different task requirements. While sequential pipelines execute tasks linearly, reflection loops enable iterative refinement, and choosing the correct topology prevents production failures.