Agent Prompting
Agent prompting gives an AI an autonomous goal, a toolbox, and a set of operating rules, then lets it plan and execute multiple steps on its own. Agents observe results, adapt their strategy, and iterate until the objective is met — with minimal human steering.
How It Works
Define a clear, measurable goal.
Provide tools: search, code execution, APIs, memory.
Set operating rules: budget, safety limits, escalation paths.
The agent plans, executes, observes, and revises until done.
Real Example
"Research the top 3 competing AI note-taking apps, build a comparison table with pricing, features, and weaknesses, and draft a positioning memo for our product. Use web search and summarize sources at each step before moving on."
Best Practices
- State the goal with measurable success criteria.
- Limit tool access and add safety guardrails.
- Require the agent to summarize progress at checkpoints.
- Define when the agent must ask for human input.
Common Mistakes
- Undefined success criteria.
- Unlimited tool access without guardrails.
- No plan review step before acting.
When to Use Agents
Prompts using Agent Prompting
Ready-to-copy examples of this technique in action.
Frequently Asked Questions
When should I use Agent Prompting?
Multi-step research, Report generation, Automated workflows. If your task is more complex or your output is inconsistent, consider combining it with examples or chain-of-thought.
Does Agent Prompting work on all AI models?
Most prompting techniques transfer across modern models like ChatGPT, Claude, and Gemini, though results vary. Test the same prompt on two models and keep the best performer.
What is the next technique to learn after Agents?
Based on your related techniques, explore: System Prompting, ReAct Prompting, Prompt Chaining.
How do I practice Agents?
Use the prompts above as starting points, then modify one variable at a time. Track which changes improve your results in a prompt library.