GRAPHIC APPAREL SHOPAI agents are transforming the way we solve real-world problems, but their capabilities depend heavily on the quality of the tools they use. Anthropic's insights underscore that developing, evaluating, and refining these tools is key to unlocking agent potential in practical settings.
Strategies for Building and Testing AI Tools
Rapid prototyping and iterative testing are at the heart of effective tool development. By leveraging platforms like Claude Code or Gemini CLI, developers can swiftly create prototypes, embed clear documentation, and conduct local or cloud-based tests. This hands-on approach ensures that new tools address genuine user needs from the outset.
- Prototype quickly: Use accessible documentation and test tools locally or on agent platforms for immediate feedback.
- Comprehensive evaluations: Simulate complex, real-world tasks rather than relying on basic scenarios to test tool effectiveness.
- Feedback-driven iteration: Analyze agent reasoning and usage metrics to identify bottlenecks or ambiguities.
- Agent-assisted improvement: Let agents analyze their own transcripts to suggest optimizations and refine tool design automatically.
Principles of High-Quality Tool Design
Designing tools for AI agents calls for a departure from rigid, deterministic software paradigms. Instead, flexible and context-aware solutions are needed. Anthropic recommends several best practices that have emerged from real-world experimentation:
- Prioritize impactful tools: Target workflows that deliver the most value, and avoid unnecessary complexity by consolidating related functionalities.
- Clear namespaces: Establish consistent naming to group similar tools and minimize confusion, making it easier for agents to choose correctly.
- Meaningful context in responses: Return human-readable and relevant information, not just technical data. Offer concise and detailed output options as needed.
- Token efficiency: Integrate pagination and filtering to prevent overwhelming the agent with excess data. Streamline error messages and instructions to encourage optimal usage.
- Explicit tool descriptions: Write precise, measurable specifications so agents clearly understand use cases, parameters, and expected behaviors.
Iterative, Evaluation-Driven Improvement
Continuous refinement is crucial for effective tool development. Anthropic's cycle includes:
- Prototyping tools and collecting feedback from both agents and users.
- Designing robust evaluation tasks that reflect real-world complexity.
- Analyzing agent outputs and identifying inefficiencies or error patterns.
- Refining tool instructions, implementations, and response formats based on findings.
- Conducting repeated testing with new data sets to ensure genuine progress and avoid overfitting.
This process not only identifies subtle design flaws but also allows agents to serve as co-developers, continually driving improvements.
Unlocking AI Agent Excellence
Moving beyond deterministic software, effective agent tools are those that are carefully scoped, contextually relevant, and thoroughly described. As AI agent technologies evolve, a systematic, evaluation-driven approach ensures that both tools and agents adapt to new challenges and opportunities. By following these principles, teams can maximize agent performance and maintain a competitive edge in the rapidly advancing field of AI.
Source: Anthropic Engineering Blog, "Writing effective tools for agents —with agents," published September 11, 2025.
Antrhopic's Unlocking Agent Potential by Crafting Effective Tools for AI Agents