Prompt engineering for AI agents is the practice of designing system instructions, tool descriptions, and context formats that reliably steer an LLM toward correct behavior across multi-step tasks.
For agents, prompt engineering goes beyond "write a good prompt." It shapes the entire decision environment.
What agent prompts control
- System prompt — Role, constraints, tone, escalation rules, output format
- Tool descriptions — How each tool is named and described (the model chooses based on these)
- Few-shot examples — Demonstrations of correct reasoning and tool use
- Context formatting — How retrieved documents, memory, and tool results are presented
Agent prompt engineering vs. chat prompts
| Chat prompt | Agent prompt |
|---|---|
| One-shot response | Multi-step behavior |
| Style and tone focus | Decision logic and tool selection |
| Easy to test manually | Must handle edge cases across loops |
| Failure = bad answer | Failure = wrong action taken |
Common agent prompt patterns
- Role + constraints — "You are a property maintenance triage agent. Never dispatch contractors without checking maintenance history."
- Decision frameworks — "Assess urgency on a scale of 1–5 using these criteria..."
- Tool-use instructions — "Always query the tenant database before responding about unit history."
- Escalation triggers — "If urgency is 4 or above, notify the on-call manager immediately."
Related reading
See Agent Architecture for where prompts fit in the five core components, and Guardrails for automated constraints beyond prompting.