Software engineers at General Motors’ autonomous driving division now spend only about 15% of their time coding, with AI agents handling much of the other 85%, including data analysis, problem triage, and testing. According to Rashed Haq, GM’s VP of autonomous vehicles, this shift has led to roughly three times as many merged pull requests, faster releases, and fewer defects. GM’s strategy focused on redesigning the entire engineering workflow around AI agents rather than just adding coding assistants. The company connected these agents to internal tools and massive data sets using custom Model Context Protocol servers and gave them instruction sets called “skills” to perform specific tasks. This approach allows agents to access vehicle telemetry data, run experiments in parallel, and automate bottlenecks across simulation, road testing, and post-deployment monitoring. With engineers overseeing critical points and agent permissions matching user access, GM successfully increased development velocity while maintaining quality. This comprehensive integration of AI into workflows substantially improved productivity and reduced errors, far exceeding initial expectations.
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