DevFlow Conf 2027

A rapid-fire Q&A on running AI agents in production: what breaks, what monitoring actually catches problems, and the guardrails that matter most when agents touch real systems.

Code generation is easy; trusting it is hard. This session covers verification patterns for AI-generated code — property tests, mutation coverage, snapshot judges, and CI gates — with data from 18 months of running them on a 200-engineer codebase. Includes what we stopped doing because it didn't catch anything.

Longer context is not free. A cost model for context spend, with the break-even points where retrieval beats stuffing the window.

Unglamorous optimisations with outsized effects, benchmarked on production traffic rather than a synthetic loop.

A 10-minute tour of turning a static docs site into one that answers questions with citations, stays honest when it doesn't know, and costs under $50/month to run. Live demo, real failure cases, and a checklist you can apply to your own docs this week.

Our monorepo CI took 40 minutes on a good day. This talk walks through how we cut it to 6 minutes with content-addressed caching, remote execution, and a test-selection model — including the two migrations that failed first. You'll leave with a decision framework for which incremental-build investments pay off at which repo sizes, and the graphs to convince your platform team. This session now includes a live demo of remote build caching. Attendees should bring a laptop.

A migration story across four teams: the ownership model, the CI that made it safe, and the two rewrites we would skip if we did it again.

Review queues, escalation and undo, designed so the humans in the loop stay willing to be in it. Patterns, anti-patterns and measured throughput.