Prompt Engineering for AI Agents: Beyond Basic Prompts

Prompt engineering for chatbots is easy. Prompt engineering for AI agents is a different discipline entirely. You're programming a reasoning system that makes autonomous decisions.

System prompts for agents define goals, available tools, mandatory behaviors, and explicit constraints. Every sentence constrains the agent's decision space.

Tool descriptions are the most underappreciated art. The LLM decides which tool to use based almost entirely on the description. Bad: "Queries the database." Good: Detailed description including available tables, constraints, and when to use vs. not use.

Planning prompts force the agent to decompose complex requests before acting. Agents with planning prompts complete multi-step tasks correctly 73% of the time, versus 41% without.

Multi-agent coordination prompts define how managers delegate to and supervise workers.

Error handling prompts: "NEVER make up data when a tool call fails." Without this, agents confidently hallucinate results.

Guardrail prompts constrain behavior without reducing capability. Version your prompts and A/B test changes.

About the Author

Durai Rajamanickam is a Business Transformation Leader and author of The AI Inflection Point: Volume 1 - Financial Services. With over two decades of experience, he specializes in AI-driven enterprise transformation, designing evidence-based ROI frameworks, and helping organizations modernize legacy systems with intelligent automation.

His work focuses on translating AI ambition into measurable business outcomes, with case studies spanning Ramp, Nubank, Coinbase, RBC, and Stripe—all showcasing AI ROI between 2.56× and 17×.

Connect on LinkedIn