How to build an AI agent that actually works

Building production-grade AI agents requires embedding them in structured workflows rather than letting them run autonomously. Drawing on CodeRabbit's agentic code review system and peer-reviewed research, the key principles are: start with a deterministic workflow skeleton and insert agentic steps only where judgment is needed; treat context engineering (not prompt engineering) as the core discipline, carefully selecting and filtering information per step since irrelevant context actively degrades performance; curate procedural skills manually (2-3 focused modules beats comprehensive docs); assign different models to different workflow stages based on capability, latency, and cost; build tool pipelines with explicit discovery, selection, invocation, and integration stages; maintain structured, curated memory rather than raw logs; use cross-model verification to catch hallucinations; invest continuously in multi-layered evaluation; and for multi-agent systems, prefer graph topology over star or chain. A 10-point checklist summarizes the build order.