The Art of Agent Prompting: Lessons from Anthropic’s AI Team

Source: Dev.to

Most “prompt engineering” advice was written for single‑turn chatbots — not for agents running in a loop with tools, memory, and side effects.

Anthropic’s Applied AI team recently shared what they learned from building agents like Claude Code and their research agents. Below is a practical guide for building real systems.

Key Takeaways

• Rigid few‑shot / Chain‑of‑Thought templates can hurt modern agents.
• Prompt design must account for the model operating in a tool loop, not a single response.
• Provide agents with heuristics (search budgets, irreversibility, “good enough” answers).
• Concrete guidance on tool selection and avoiding MCP‑style tool collisions.
• Strategies to guide the agent’s thinking (planning, interleaved reflection, when to stop).

Running Example

Cameron AI, a personal finance assistant, illustrates how these principles can be applied in practice.

If you’re working with LangGraph, custom backends, or just trying to keep your agents from over‑searching or looping forever, this guide may save you painful iteration.

Read the full article: The Art of Agent Prompting