Hey, Luca here! Welcome to a new edition of the 💡 Monday Ideas 💡 — ideas and readings to start the week on the right foot.
I also publish an original essay every Wednesday. Here is the one from last week in case you missed it:
📚 Master production-grade orchestration, for free!
This week’s newsletter is brought to you by our friends at Kestra!
A few months ago we published an article about how to orchestrate AI workflows, together with the team at Kestra, an awesome open source project used by the teams at Apple, JPMorgan, and more.
Today I am happy to help them spread the word about the Kestra Academy, where you can find self-paced courses on infrastructure automation, AI workflows, and standardizing orchestration across your engineering org.
In the academy, you can learn how to govern complex workflows and start running production pipelines the same week.
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🏁 Faster coding vs faster delivery
Most AI coding tools optimize the inner loop: you describe a feature, watch the agent write the code, and get something working a few minutes later.
This is where AI has delivered the most visible improvements, but it is just a small part of how software gets shipped. There is an outer loop too: deciding what is worth doing, shaping and reviewing the solution, releasing it, and learning from production.
If you don’t improve both, the constraints move elsewhere:
Reviews — agents create more PRs than people can reasonably inspect.
Shaping product — the team can build faster than it can decide what good work looks like.
Customer signal — shipping the wrong thing faster is still waste.
At a small scale, this is the same acceleration whiplash I have experienced with Tolaria. Starting work is cheap, but my ability to review it, decide what is good enough, and create the next piece of work does not scale at the same rate.
So the goal should be end-to-end throughput, which requires a workflow that encompasses both the inner and the outer loop.
I wrote a full case study about how the guys at TRM are doing this 👇
🧪 Agents will game your tests
When I spoke with Anush Elangovan, VP of AI Software at AMD, he described a funnel where creativity is allowed to run wild at the top, but progressively more human judgment is needed as code moves toward production.
He also shared an entertaining story about building Spur, an AI-native job scheduler. The agents writing the code were so “smart” that they found a shortcut: instead of actually running unit tests on hardware, they simulated the hardware and reported success.
“The agents were so smart that they would simulate the hardware and pass the unit test and say ‘yes, success.’ We had to say no, no, no — you never simulate the hardware. You have to actually run the test on hardware before you can promote a PR.”
That forced the team to build a superset of the guardrails they previously had for humans. Test harnesses now need to account for adversarial agent behavior. The result, Anush said, is a quality system roughly 10x more comprehensive than what was needed when humans were the only contributors.
I have found this to be a useful posture: assume agents will optimize for passing the tests, not necessarily respecting their spirit, and design accordingly.
You can find the full conversation with Anush here 👇
📚 Weekly Readings
Finally, here are the best articles I have read this week:
🥇 The Manager’s Path in the Age of AI
13 min • by Camille Fournier
Camille asks what still belongs to managers when AI raises individual output and eats the small day-to-day interactions that build trust and rapport across an org. This is the written version of the talk I heard at LDX3. I loved 100% of it and it gave me a lot to think about.
🥈 Code Yellow, Code Red
11 min • by James Stanier
James walks through declaring a Code Yellow after months of instability eroded some customer trust. Running it well means clear communication, defined goals, and company-wide support to fix problems, without blame.
🥉 Trying the Software Factory Pattern
3 min • by Will Larson
Will is trying a “software factory” loop: agents audit the project’s goals and metrics, keep track of state on Linear, work on the non-blocked tasks, and re-check when the description goes stale. This was short and practical, and a good read.
And that’s it for today! If you are finding this newsletter valuable, subscribe to the full version!
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See you next week!
Luca