Agentic Learning Center
Collaborative AI: Repo rules, tickets, and review history for the agentic SDLC
Collaborative AI keeps humans and agents working from shared repo rules, tickets, and review history so teams can trust and build on AI-generated code.
What is context engineering? A primer for AI-assisted teams
Context engineering gives AI agents the right information and structure. For teams shipping production code, it's what makes review trustworthy.
AI pair programming in the agentic SDLC: When review becomes the bottleneck
AI pair programming inverts the roles. The agent writes the code and you review it, and at agent throughput review becomes the bottleneck. Here's how to keep up.
Code context: The evidence behind trustworthy AI code review
Code context is the evidence an AI reviewer sees beyond the diff. Here's why deep context, not a bigger window, makes AI code review trustworthy.
Adopt agentic engineering without losing your review loop
Agentic engineering typically breaks in the review queue. In this piece we go over risk-tier reviews, adding an independent first pass, and tracking the metrics that hold.
Apply AI governance to coding agents in your org
AI governance for coding agents starts at the pull request merge gate. Verify agent-authored code, encode policy as config, and keep an audit trail.
Bring agentic code review to your existing PR workflow
Add agentic code review to your existing PR workflow without breaking branch protection. A practical rollout playbook with the metrics that prove it's working.
Build an AI second brain for engineering teams
An AI second brain for engineering teams captures codebase decisions and review history, then applies them at review time so knowledge stays when they leave.
How to design agentic workflows that actually ship
Agentic workflows ship reliably only when an independent verification step gates the merge. How to design that gate, instrument the risk, and keep accountability with a human.