How teams use agents to iterate, review, and ship PRs with proof.
Showing 12 of 122 posts
Why single-file coding challenges fail to predict real agent performance, and how multi-file, spec-driven evals with hybrid scoring surface the failures that matter.
AI code generation is solved - the new constraint is verification. Learn why preview environments and automated validation loops are essential for teams where designers generate ideas faster than engineers can review them.
Why saturated benchmarks give zero signal when choosing AI coding models, and how to build evals that actually distinguish performance for your team's workflows.
Learn the systematic methodology for finding optimal LLM temperature settings through rigorous testing rather than guessing - including surprising findings like Gemini 2.5 Pro at 0.72 and ByteDance Seed at 1.1.
Learn why the same AI model from different providers produces different results, and how policy-based routing solves provider variance, geographic compliance, and rate limit cascades for distributed engineering teams.
CLI coding agents enable parallel task execution, but every output still requires human review in your IDE. Learn how to structure your workflow for the reality of where AI models are today.
Why graceful failure recovery matters more than raw success rates when evaluating AI models for agentic coding workflows - and how to test for it.
Learn how power users run multiple AI coding agent tasks in parallel to build context faster and eliminate the single-task bottleneck that slows down development.
Why 1 million token context windows degrade to 300-400K usable tokens in agentic workflows, and how to design tasks for the effective limit.
Why 30% of agent PRs merge while 20% is typical - and how investing in type safety, test coverage, and module boundaries directly increases your AI coding agent's mergeable output.
Learn how engineering teams extract value from unmergeable PRs by treating AI coding agents as research tools that reduce uncertainty and accelerate development.
Why smart tab-complete loses relevance when AI agents write complete implementations instead of predicting your next token
Cloud Agents review code, catch issues, and suggest fixes before you open the diff. You review the results, not the process.
