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Simon Willison

Conceptual integrity and counting lines of code

Last week I recorded an episode of the Talking Postgres podcast with Claire Giordano on the subject of "How AI is changing software development". We had a really great conversation.

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arXiv AI
Sep 7

Beyond Code Generation: Reliability, Verification, and Cost Economics in the Agentic Software Development Lifecycle

The paper examines how AI coding agents are evolving beyond simple autocomplete to perform complex tasks such as repository inspection, multi-file editing, tool execution, test writing, pull request creation, and long-duration work with minimal supervision. It highlights that while these agents boost coding activity, significant bottlenecks remain in review, integration, testing, security, deployment, and production operations, and that the economics of software development are shifting toward variable token, tool, sandbox, CI, and rework costs. The authors synthesize recent research and industry data to propose four engineering concepts—Agentic SDLC Throughput Paradox, Production-Qualified Change, Verification Tax, and an Agentic SDLC Control Plane—to guide the allocation of autonomy within cost, reliability, and human-attention constraints, ultimately reframing the research focus to production-qualified value per dollar, reviewer-hour, and operational risk.

By Happy Bhati
agentsefficiencybenchmarks
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Simon Willison
Sep 11

Feeling sad about AI

Simon Willison reflects on the emotional impact of AI tools that can produce code quickly, noting that many developers experience an initial sense of disheartenment. He argues that recognizing the shift from coding to higher‑level problem solving allows experienced engineers to leverage new tools and add greater value. Willison emphasizes that software engineering has always faced rapid change, so adapting to AI is part of the profession’s ongoing evolution.

llmsagents
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arXiv AI
Sep 7

$\tau^\tau$-Bench: An Environment for End-To-End, Realistic Agent Construction

The paper introduces $ au^ au$-Bench, a benchmark that turns the construction of AI agents into a measurable task. In this environment a developer agent receives real business records, client requirements, a production API, an existing codebase, and constraints on cost and models, and must deliver a complete customer‑service agent. The benchmark evaluates performance by deploying the agent against simulated users, revealing that current state‑of‑the‑art models achieve only 23.9% success while an expert‑written reference scores 82.2%.

By Quan Shi, Keshav Dhandhania, Karthik Narasimhan, Victor Barres
llmsagentsbenchmarks
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Simon Willison
Sep 6

There's No Limit to How Bad Code Can Get

Simon Willison discusses the pitfalls of attempting to replace a legacy system with a new one when technical debt is overwhelming. He explains that while the old system continues to evolve, developers lack incentive to improve it, and the new team, initially fast, eventually struggles to understand and deliver the required functionality. The result is often two partially functional systems in production, with the new one abandoned and the old one still running, increasing risk and complexity.

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