Practical Prompt Structure for Claude AI Execution Agents

Prompt Engineering for Execution Agents

A developer with months of experience building Claude AI agents that execute real actions—API calls, data extraction, multi-step decision trees, and error recovery—shares the prompt structure that solved persistent hallucination problems. Early attempts resulted in agents hallucinating fields, chaining unnecessary API calls, silently swallowing errors, and returning partial data as if everything was fine.

Key Prompt Structure Principles

• Write prompts like contracts, not instructions: Natural language prompts like "find relevant leads and send a personalized message" invite improvisation. What works: define inputs (exact schema, types, edge cases), outputs (exact shape including error states), and decision rules (if X then Y, if Z then stop).
• Dedicate 40% of your prompt tokens to error handling: Without explicit error paths, Claude either retries infinitely or silently returns garbage. Every possible failure mode needs its own instruction: what to do when the API returns a 429, what to do when a required field is missing, what to do when data looks ambiguous.
• Separate "wait" from "stop": Claude can't distinguish between "you don't have enough info to act yet" and "the info is bad, abort" unless you spell out both cases. One agent was supposed to pause on ambiguous data but instead skipped everything that wasn't 100% clean. Adding explicit instructions for both states improved accuracy overnight.
• Pattern matching > role playing: "You are a senior engineer" does almost nothing for execution quality. Pasting an actual example of good output and saying "match this pattern exactly" works 10x better. Claude is better at replicating a concrete example than interpreting an abstract persona.
• Route by complexity, not by habit: Use Opus exclusively for multi-step decisions where context and edge cases matter. Everything else goes to Sonnet or Haiku. Most agent tasks don't need Opus, and the cost difference adds up fast. API costs went from brutal to manageable with proper routing.
• Force chain-of-thought before every action: This costs maybe 15% more tokens, but when something breaks at 2am, you can read exactly why the agent decided to do what it did. Without this, you're debugging blind.

The developer notes this prompt structure matters way more than model choice for building agents that execute real actions (not just generate text).