The Complete Prompt Engineering Guide for Agents
Master system prompts, chain-of-thought, and tool-use prompting patterns for building reliable AI agents.
Key Takeaways
- Agent prompts need explicit boundaries, not just instructions
- Chain-of-thought improves reliability by 40%+ on multi-step tasks
- Tool descriptions are the most underinvested part of most agent systems
- Evaluation should drive prompt iteration — not intuition
Overview
This comprehensive guide covers everything you need to know about prompting AI agents for production use. From crafting system prompts that establish clear agent identity and boundaries, to advanced chain-of-thought patterns that improve multi-step reasoning, to tool-use prompting that makes function calling reliable.
You'll learn the patterns used by teams at Anthropic, OpenAI, and LangChain to build agents that handle edge cases gracefully. Each section includes real-world examples, anti-patterns to avoid, and templates you can adapt for your own agents.
Topics covered include: agent persona design, instruction hierarchy, output formatting, error recovery prompting, multi-turn context management, and evaluation-driven prompt iteration.
What's covered
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