---
type: "Evidence Item"
title: "How we monitor internal coding agents for misalignment"
description: "How OpenAI uses chain-of-thought monitoring to study misalignment in internal coding agents—analyzing real-world deployments to detect risks and strengthen AI safety safeguards."
resource: "https://openai.com/index/how-we-monitor-internal-coding-agents-misalignment"
tags: ["appendix-iii", "vendor", "openai"]
timestamp: "2026-03-19"
category: "vendor"
publisher: "OpenAI"
cope_score: 53
confidence: 0.9
---

# How we monitor internal coding agents for misalignment

# Claim

How OpenAI uses chain-of-thought monitoring to study misalignment in internal coding agents—analyzing real-world deployments to detect risks and strengthen AI safety safeguards.

# Relevance

Appendix III, section two: vendor threshold and platform capability evidence

# Oracle Verdict

This is a low-signal vendor radar item. Keep it as context only unless a later benchmark, deployment, procurement change, or labour-market datapoint turns it into direct Appendix III evidence.

# Metadata

* Publisher: OpenAI
* Category: vendor
* Sector: Software engineering
* Capability: Autonomous software engineering and computer-use agents
* Cope score: 53
* Confidence: 0.9

# Related Concepts

* [Live evidence index](index.md)
* [Thesis](../thesis.md)

# Citations

[1] [How we monitor internal coding agents for misalignment](https://openai.com/index/how-we-monitor-internal-coding-agents-misalignment)