agent-prompt-engineering

Agent Prompt Engineering

Overview

Agent prompt engineering differs fundamentally from traditional prompt engineering. Agents operate autonomously in loops, making decisions and using tools without human intervention. This requires conceptual engineering: providing heuristics, principles, and decision-making frameworks rather than rigid templates.

This skill distills Anthropic's production experience building agents like Claude Code into actionable principles for creating reliable, production-ready agent prompts.

Core Principles

1. Start Simple, Iterate Based on Reality

Begin with a straightforward prompt defining role and core task. Avoid premature optimization.

Initial structure:

<!-- Role -->
You are [Agent Name], a [domain] assistant.
Your task is to [primary objective].

<!-- Dynamic Content -->
You will be provided with [data sources].
<data_source>
{{VARIABLE}}
</data_source>

<!-- Instructions -->
When [handling requests], follow these steps:
1. [Step 1]
2. [Step 2]
3. [Step 3]

<!-- Repeat Critical Instructions — only in long prompts, see note below -->
Remember to [most important constraint].

On repeating the critical instruction: this earns its place in a long prompt, where the constraint would otherwise sit hundreds of lines from the decision it governs. On a frontier model with a short prompt it's unnecessary by default — the instruction was already read, and the restatement just spends tokens. Start without it and add it back if a constraint is actually being missed.

Perfect prompts emerge through iteration. Use AI to draft initial versions, then refine through testing.

2. Think Like Your Agent

Critical rule: If a human cannot follow your instructions with only the tools provided, neither can the agent.

Simulate being the agent: given only your prompt and tool descriptions, can you accomplish tasks?

Common gaps to check:

• Missing tool usage instructions: "To access [data], use the tool_name(params) tool"
• Unclear data locations: Specify where information lives and how to retrieve it
• Ambiguous decision criteria: Define when to use which approach
• Undefined success conditions: Clarify what "complete" means