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Woven by Toyota: AI agents that fix MISRA code violations

Woven by Toyota, Inc. · Automotive · Japan

The challenge

Software for automated driving and driver assistance (ADAS) is written largely in C and C++ and must follow the MISRA coding guidelines, which run to hundreds of pages. Static analysis tools flag large numbers of violations, engineers fix them by hand, and fewer engineers now have deep C and C++ skills. One ADAS recognition module alone needed about 60,000 errors corrected.

Woven adds that the amount of code in a vehicle keeps growing, which makes manual compliance work harder to sustain.12

What was implemented

The tool, called MISRA Copilot, was built with partner Zen Architects. Three agents work in sequence: a Coder agent proposes compliant fixes, a Reviewer agent critiques them and sends them back for revision, and an Evaluator agent checks compliance and gives a rationale and a confidence score for each change. Woven says the agents draw on a knowledge base of earlier code fixes from Woven and Toyota through retrieval-augmented generation.

The tool never edits code on its own. It submits annotated proposals as GitHub pull requests, and engineers decide whether to accept them. Microsoft's story says the system runs on Azure OpenAI Service (now Azure OpenAI in Microsoft Foundry Models) with GPT-4o in early tests, o1 tested in January 2025 and o3-mini for the Coder agent, orchestrated with the open-source AutoGen framework, and uses Azure App Service, Azure Cosmos DB and GitHub Enterprise.

The team refined the system in two Azure hackathons in December 2024 and January 2025, and built a VS Code extension after a presentation to Toyota Group subcontractors at the end of January 2025.123

Reported outcomes

  • 81.5%

    Share of MISRA violations corrected automatically in a proof of concept on internal ADAS development code12

    Measured
  • 97.1%

    Share of generated code that was syntactically correct in the same proof of concept12

    Measured
  • about 50%, then about 80%

    Automatic correction rate in early tests: about 50% with GPT-4o, then about 80% after the team tested o1 in January 2025, against a goal of about 70%1

    Measured
  • several hundred million JPY

    Cost savings Woven projects from the tool. The post does not explain how the figure was calculated2

    Projected, not measured

Each outcome links to its source; the label there says whether the company, Microsoft or a third party reported it.

What others can learn, and the limits

In our view, the design travels well to any regulated codebase: one agent proposes a fix, a second critiques it, a third explains it and scores its confidence, and nothing reaches the code until an engineer approves a pull request. That keeps accountability with people while the repetitive work is automated. Readers should note what the numbers are: proof-of-concept results on a specific set of internal code. In November 2025 Woven said the tool was still in further evaluation for possible deployment, and the 80% in Microsoft's headline is a test figure, not a production rate. The cost projection has no published method. A team with modest violation counts, or one whose static analysis tool already offers reliable automatic fixes, may not need a multi-agent system.

Sources

  1. Woven by Toyota and Azure OpenAI Service automate 80% of software code safety fixes (opens in a new tab)Microsoft · Customer-reported, Vendor-reported
  2. Agentic AI for a Zero-Accident Future: Accelerating MISRA Code Compliance to Strengthen Software Reliability (opens in a new tab)Woven by Toyota · Customer-reported
  3. Foundry Models sold by Azure (opens in a new tab)Microsoft Learn · Vendor-reported

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