Anthropic’s “Think” Tool: A Game-Changer for Complex AI Problem-Solving

Anthropic recently published an insightful article on their Engineering Blog titled “The ‘think’ tool: Enabling Claude to stop and think in complex tool use situations.” This March 20, 2025 piece reveals a simple yet powerful approach that significantly enhances Claude’s problem-solving capabilities.

What Is the “Think” Tool?

The “think” tool gives Claude “the ability to include an additional thinking step—complete with its own designated space—as part of getting to its final answer.” While it might sound similar to Claude’s extended thinking capability, it serves a different purpose.

While extended thinking focuses on what Claude does before generating a response, the “think” tool allows Claude to pause during response generation to consider whether it has all the information needed to proceed. This makes it particularly valuable for long chains of tool calls or multi-step conversations.

How Regular Users Can Implement It

Even without developer access, you can apply the principles of the “think” tool in your prompts to Claude. Here’s how:

1. Create a Thinking Space: Ask Claude to use a specific format (like <thinking>...</thinking> tags) to show its reasoning process before providing the final answer. Example prompt: “When solving this problem, first use <thinking> tags to work through your approach step-by-step, then provide your final answer outside these tags.”
2. Provide Clear Instructions: Tell Claude exactly when and how you want it to “think” during complex tasks. Example prompt: “Before making any decisions about this data, I’d like you to use a <thinking> section to analyze the patterns you see, check for inconsistencies, and outline your approach.”
3. Use Domain-Specific Examples: Like Anthropic’s research showed, giving Claude examples of the kind of thinking you expect improves results dramatically. Example prompt: “When reviewing this legal document, use <thinking> tags to check for:
   • Contradictions between sections
   • Undefined terms
   • Potential compliance issues with relevant regulations
   • Missing information that might affect interpretation”
4. Sequential Task Breakdown: For multi-step problems, instruct Claude to think through each step before proceeding. Example prompt: “For this coding challenge, use <thinking> tags at each step to:
   1. Analyze what the problem is asking
   2. Consider different algorithmic approaches
   3. Evaluate tradeoffs between approaches
   4. Plan your implementation
   5. Check for edge cases before finalizing”

When to Use It

According to Anthropic’s research, the “think” tool delivers the most value in three key scenarios:

1. Tool output analysis – When Claude needs to carefully process the output of previous tool calls
2. Policy-heavy environments – When Claude needs to follow detailed guidelines and verify compliance
3. Sequential decision making – When each action builds on previous ones and mistakes are costly

Impressive Performance Gains

Anthropic evaluated the “think” tool using τ-bench (tau-bench), a comprehensive benchmark for testing tool use in realistic customer service scenarios. The results were remarkable – in the airline domain, the “think” tool with an optimized prompt achieved a 54% relative improvement over the baseline. Even in the simpler retail domain, the tool delivered noticeable gains without additional prompting.

The tool also contributed to Claude 3.7 Sonnet achieving a state-of-the-art score of 0.623 on SWE-Bench, with experiments showing an isolated improvement of 1.6% on average just from adding the “think” tool.

Our Perspective at Pulse AI Solutions

At Pulse AI, we see the “think” tool as a perfect example of how seemingly simple enhancements can dramatically improve AI reasoning capabilities. This aligns with our philosophy that effective AI solutions don’t always require complex architectures—sometimes, they just need thoughtful design that mimics human cognitive processes.

The performance improvements seen in policy-heavy environments are particularly relevant to many of our clients in regulated industries who need AI solutions that reliably follow complex guidelines while maintaining flexibility.

Final Thoughts

As Anthropic notes, the “think” tool isn’t a “one-size-fits-all solution,” but it offers “substantial benefits for the correct use cases, all with minimal implementation complexity.” It enables more capable, reliable, and transparent AI systems without significant overhead.

We’re excited to see how this approach might be applied across different models and use cases. What do you think about giving AI systems dedicated space to think? And how might your organization benefit from AI that takes a moment to carefully consider complex problems before acting?