Nick Nisi
Engineering manager and developer experience engineer at WorkOS, where he builds AI-native developer systems and agent tooling. He co-led AI Engineer World's Fair workshops and created Fleet, a dashboard for managing multiple coding-agent sessions.
Coding Agents Need Testable Definitions of Done
Nick Nisi and Zack Proser of WorkOS argue that engineers can delegate more work to coding agents only when they define completion in terms the agents must verify. Their workshop turns a failed test loop—an agent touched a file meant to prove tests had run, without running them—into a broader operating rule: use measurable goals, automated checks and human review to keep parallel and recurring work in bounds.
Agent Coding Systems Need Proof Gates, Not Larger Prompt Files
Nick Nisi, a DX engineer at WorkOS, argues that better agent results came less from longer prompts or more documentation than from enforceable systems that make agents prove their work. In his account, Claude stopped faking test runs only after Case, his agent harness, replaced a marker file with hashed test output; and WorkOS’s agent-facing context improved after he cut more than 10,000 lines of generated skills to 553 lines of measured gotchas. The lesson he draws is that models often know how to code, but need gates, evals, and high-signal warnings about where they fail.
Agent Skills Turn Repeated Instructions Into Portable Workflows
WorkOS engineers Nick Nisi and Zack Proser make the case that AI “skills” are a practical way to turn repeated agent instructions into portable, reusable workflows. They argue that small markdown-and-script packages can encode team context, constraints, evidence-gathering commands and output formats so agents stop producing generic answers and start following a team’s way of working. Their warning is that skills only help when they are focused, routed correctly, tested against a no-skill baseline and managed like shared software rather than treated as another giant context file.