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tech 22 June 2026

Claude Code's "Extended Thinking": A Summary, Not Authentic Thinking

Claude Code's "extended thinking" tool merely summarizes reasoning without truly revealing it. Discover how this affects agent auditing and decision-making transparency.

Article inspired by the original source
Claude Code's "extended thinking" is a summary- not authentic thinking ↗ patrickmccanna.net

Introduction

Artificial Intelligence (AI) has made impressive strides in automation and reasoning, promising systems that can not only perform complex tasks but also explain the rationale behind their choices. However, a closer examination of Claude Code's "extended thinking" tool reveals a different reality: what you receive is merely a summary of the reasoning, not the reasoning itself.

What is "Extended Thinking"?

For the uninitiated, "extended thinking" is a feature of Claude Code designed to record and convey the reasoning behind decisions made by the AI agent. It sounds ideal for audits and transparency, but in practice, the information provided is far from complete.

The Summary vs. Reality

According to support documents, "extended thinking" provides a summary of the reasoning, not the full reasoning. The details of each session are encrypted, and only those with access to a specific key, held by Anthropic, can view it. In other words, without an enterprise agreement, you will never see the authentic reasoning.

Implications for Businesses

For businesses that rely on transparency and auditability in AI, this limitation poses significant challenges. For example, in banking or healthcare sectors, where decision traceability is crucial, not having access to the full reasoning can complicate regulatory compliance. According to a Gartner report, 75% of businesses using AI will see their productivity impacted by a lack of transparency by 2025.

A Concrete Example

Consider a tech startup developing an automated underwriting tool for insurance. The company wants to audit its AI agent's decisions to ensure there is no bias. With only a summary of the reasoning, it becomes difficult to pinpoint exactly why an application was accepted or rejected, rendering the audit ineffective.

Possible Alternatives

In light of this limitation, some companies are turning to open-source solutions that allow direct access to logs and full reasoning. However, these solutions often require extensive technical expertise and a significant time investment.

Conclusion

The ability to understand and audit AI reasoning is crucial for the trust and efficiency of automated systems. Claude Code's "extended thinking," as a mere summary, does not meet this essential need. For tech decision-makers and entrepreneurs, it's vital to understand the limitations of this tool before integrating it into their operations.

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