How teams use agents to iterate, review, and ship PRs with proof.
Showing 12 of 122 posts
How AI coding agents transform bug fixing from a backlog bottleneck into instant beach cleanup - anyone can spot trash, an agent picks it up, engineers review the PR.
Learn why running two AI code reviewers with different perspectives catches more issues than a single reviewer - human or AI - and how to set up dual review coverage for your team.
How product managers, ops, and support teams use AI coding agents to query codebases directly - reducing engineering interruptions and eliminating meeting bottlenecks
When AI agents can open PRs from a Slack message, the cost of creating changes drops to near zero. Learn why treating PRs like hypotheses and closing more of them is the new operating model for engineering teams.
How to implement cross-functional approval gates when AI agents enable non-engineers to generate production code. Learn the three-role review process that keeps agent-generated PRs safe.
Why prompts that achieve 90% success on one LLM can crater to 50% on another - and how explicit prompt writing unlocks model-agnostic reliability
Why different embedding models interpret semantic similarity differently, and how the choice between exact match and contextual match affects codebase search results.
Why every settings toggle creates cognitive load and carrying cost that compound over time, and how to apply the Pareto gate before shipping configuration options.
Why 500 tokens per second still feels slow for local LLMs - the hidden bottleneck of prompt processing time in agentic coding workflows and how to design around it.
Why AI coding agents are shifting the economics of package dependencies - and how owning focused implementations beats managing fork maintenance and vulnerability churn.
Why parallel AI coding agents create coordination problems beyond git, and how to manage shared resources like databases, migrations, and dev servers.
Learn why AI coding agents lose formatting rules and workflow constraints after context condensing, and how preserving the first message fixes instruction drift in long sessions.
Cloud Agents review code, catch issues, and suggest fixes before you open the diff. You review the results, not the process.
