How teams use agents to iterate, review, and ship PRs with proof.
Showing 12 of 122 posts
Google's Gemma 3N at 4 billion parameters outperforms Gemini 1.5 Pro on key benchmarks while running on a laptop. Learn what this means for your AI coding workflow.
Learn why measuring learning velocity instead of productivity during AI adoption protects your initiative through the change management dip and leads to lasting transformation.
Google reports that over 50% of production code passing review each week is AI-generated. Learn what this threshold means for engineering teams and how to adopt workflows that let AI contribute code that survives review.
The economics of software specification have flipped. When prototypes ship faster than PRDs can be written, teams are discovering that working code is the best documentation.
Evidence from a month of testing shows Opus delivers identical output quality with thinking mode disabled - here's how to cut your token spend by 10-20% without losing anything.
Why sticky developer experiences drive adoption faster than superior model benchmarks, and how the infrastructure layer becomes swappable once workflows are established.
Learn why thinking mode should be a tool, not a default. Discover how task-aware AI agent configuration reduces token costs and latency while maintaining output quality.
Learn why AI coding context evaporates after each task and how teams like Smartsheet use shared modes and memory banks to turn individual learnings into compounding team infrastructure.
Learn why centralizing AI tool spend and measuring output instead of cost unlocks productivity gains - insights from Smartsheet's engineering leadership approach.
Engineering leaders already know how to configure AI agents - the same org design principles that work for teams apply to AI modes, orchestrators, and responsibility delegation.
Agent-generated PRs serve as structured handoff artifacts that explain what changed and transfer context between modes - merging is optional, learning is the goal.
Learn how to debug slow AI coding tasks by asking reasoning models to explain their own behavior - a technique that transforms prompt iteration from guesswork to direct diagnosis.
Cloud Agents review code, catch issues, and suggest fixes before you open the diff. You review the results, not the process.
