Individuals can rewire their workflow overnight; teams can't. The teams that win with agents change how they work rather than sprinkling AI on top, share their know-how as skills and memory, keep humans accountable, and measure honestly: speed and quality together.
Key takeaways
- Results vary wildly. The difference is rarely the tool; it's whether the team changed how it works.
- Expect to slow down before you speed up, while you make the codebase and process agent-ready.
- Share know-how: checked-in memory files and skills mean every new teammate's agent knows your practices.
- You own the loop. Never send something you haven't read.
- Measure honestly: speed should rise while escaped defects stay flat or fall. Aim for 2–3×, then compound.
Try it with your agent
Draft a team agent charter:
Draft a one-page agent charter for our team, based on this repo and our recent history: what we delegate to agents, what humans always review, how we share memory files and skills, our security rules, and how we'll measure success (speed AND escaped defects).
Transcript
Intro
A solo developer can rebuild their workflow over a weekend. A team can't. And it's in teams, with shared code, reviews and production on-call, that agentic engineering gets genuinely hard.
Wildly different results
Team results vary wildly. Amazon's Clare Liguori reports pilots with a median four and a half times productivity improvement. Boris Cherny says productivity per engineer at Anthropic rose almost seventy percent, measured mostly by pull requests. Yet Armin Ronacher finds agents work much better on side projects than at work, and that token spending grows faster than measurable productivity.
Change how you work
So what separates them? In a pilot with fifty teams, Liguori found the difference wasn't the tools; nearly all used the same one. The teams with step-function improvements intentionally changed the way they worked. The others sprinkled AI on top of their existing process.
Slow down to speed up
And almost every successful team reported the same shape. Productivity went down first, while they did deliberate work to make their codebase and process ready for agents. Then came the hockey stick. GitHub adds a warning: pilots often fail to scale because organizations copy the tool, but not the conditions that made the pilot work.
The rift
Teams also split. Dex Horthy describes a growing rift: staff engineers who don't adopt because it doesn't speed them up, junior engineers who use agents heavily and ship slop, and seniors who spend their weeks cleaning it up. Charity Majors says both camps are seeing real things. Her advice: when you share wins, tell the whole story, including the costs.
You own the loop
Whatever your process, accountability doesn't move. Charity Majors puts it perfectly: there is no human in the loop. You own the loop. And her rule for AI-assisted communication: never send anyone something you haven't read. If it would take them longer to read it than it took you to make it, it's probably slop.
Shared know-how
The big team advantage is shared know-how. Check in your memory files, skills and permission settings, so every lesson benefits everyone. Anthropic's Mahesh Murag describes the goal: when someone joins your team, their agent already knows what your team cares about. Anthropic says technical onboarding dropped from two or three weeks to two or three days. And OpenAI's harness team held a weekly garbage collection day to turn the week's slop into durable rules.
Deciding is the bottleneck
As implementation gets cheap, the bottleneck moves. DHH says the constraint now is figuring out what to build, how it should be built, and which customers to talk to. Clare Liguori sees frontier teams spending more time making decisions than writing code. So invest in fast, reversible decisions.
Measure honestly
Finally, measure honestly. Remember the study where developers felt faster while measuring slower: self-reports aren't enough. GitHub's health test: as agent use grows, escaped defects should stay flat or fall while delivery speeds up. Dex Horthy's realistic target is two to three times faster, then two to three times on top of that. And watch out for burnout: DHH says the people most all-in on agents are working harder than ever.
Quick check
Quick check. Since your team adopted agents, pull requests per engineer tripled, but defects reaching production doubled. What does that tell you? Tap your answer.
Try it
Draft a one-page agent charter for your team. Let your agent propose it from your repo and history, then argue about it with your teammates. That conversation is the real value.
Recap
Recap. Change how you work, don't just add tools, and expect a dip first. Share know-how through checked-in memory and skills. You own the loop. And measure speed and quality together. Next, our final module: the engineer you become.