The AI agent demo-to-production gap is enormous. A demo agent that works 90% of the time is impressive. A production agent that fails 10% of the time is a disaster. Building agents that handle real business workflows reliably requires thinking about failure modes from day one.
Design your agent's decision tree before you write any code. Map every possible input, every branch point, and every edge case. Identify where the agent can make autonomous decisions and where it needs human approval. This decision tree is your agent's architecture — treat it with the same rigor as a system design document.
Prompt chaining architecture is how you build complex agents. Break multi-step workflows into individual prompt steps, each with its own input validation, output parsing, and error handling. Chain them together with explicit state management. This makes debugging, testing, and iterating on individual steps possible.
Error handling separates production agents from demos. Every AI call can fail: API timeouts, rate limits, malformed responses, hallucinated outputs, or edge cases your prompts don't cover. Build retry logic with exponential backoff, fallback to simpler models, and human escalation paths for cases the agent can't handle.
Cost optimization through model routing is essential at scale. Use GPT-3.5 or Claude Haiku for simple classification and routing tasks. Reserve GPT-4 or Claude Opus for complex reasoning steps. Cache frequent query-response pairs. Batch similar requests. A well-optimized pipeline costs 70% less than routing everything through the most expensive model.
Build evaluation frameworks before you deploy. Define what 'correct' looks like for every agent action. Create test datasets with expected outputs. Run evaluations automatically in CI/CD. Track accuracy, latency, and cost per action over time. Without measurement, you're flying blind.
Production monitoring must catch failures before users do. Log every agent decision with full context. Set up alerts for: accuracy drops, latency spikes, cost anomalies, and human escalation rate increases. Build dashboards that show agent performance trends. The moment an agent starts degrading, you need to know.
