Peter Norvig: Rewire software engineering for agentic AI
Norvig argues that advances in models and agents require changes to specifications, development workflows, security, and monitoring — and he illustrates the gap with a recent Codex mishap.
Norvig argues that advances in models and agents require changes to specifications, development workflows, security, and monitoring — and he illustrates the gap with a recent Codex mishap.
AI agents are now capable enough that software engineering practices — specs, documentation, CI, and monitoring — must be redesigned.
Real incidents show current workflows miss agent-specific risks: models can create unexpected artifacts, interact with pipelines, and perform actions without adequate oversight.
Norvig points to historical shifts in programming abstractions and says the community must capture evolving program behavior and control recursive agent behaviors.
What comes next after a moment when companies sell existential risk but pursue public funding and DIY regulation, even as software developers question their life choices? Peter Norvig, distinguished education fellow at Stanford HAI and former Google research director, used his opening keynote at The AI Conference in San Francisco on Wednesday to speculate on the answer. Almost certainly, it's another AI conference – on Thursday in fact – given the two or three dozen of them that have bubbled up in San Francisco this year or are scheduled in the remaining three months.
He acknowledged the milestones of AlexNet and ImageNet in 2012, the Transformer architecture for machine learning in 2017, the debut of ChatGPT in 2022, and the emergence of reasoning and coding agents in 2024. REG AD Norvig went on to cite Linux kernel creator Linus Torvalds' changing attitude toward AI. "Six months ago he was a skeptic, he became a cautious adopter, and now he's a dogmatic advocate," said Norvig.
To get to the present, he took a detour through the past. REG AD He also touched on the sudden competency of AI models for math, noting that two years ago, GPT-4 couldn't count the number of "r"s in "strawberry." Yet as of August this year, he observed, 25 percent of math preprint papers on arXiv acknowledged AI assistance. Norvig says the process of software engineering needs to change.

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