Summary

For twenty years the constraint on building software was how fast humans could write it, and agile's sprints and ceremonies were built to manage that constraint. Agentic coding removed it: a team can now produce in a day what took a sprint. So the heavy process becomes the bottleneck, slower than the engineering it is meant to coordinate. What works instead is not planning the whole journey, which is impossible in unmapped territory, but setting a destination and navigating to it daily: a stream-aligned team deciding together in the room, delivering daily, with a one-week horizon and daily course-correction. It is faster because the loop is short, and better because every decision pulls on everyone's judgement. The speed is earned by strong foundations, not instead of them.

For twenty years, the constraint on building software was how fast humans could write it. Agile, with its sprints and ceremonies, was built to manage that constraint: coordinate the people, plan the work into two-week chunks, keep everyone moving in roughly the same direction. It worked because building was slow.

Building is not slow anymore. With agentic coding tools, a capable team can now produce in a day what used to take a sprint. And here is the uncomfortable consequence: when building gets that fast, the process built to coordinate slow building becomes the thing holding you back. The ceremonies, the two-week planning cycles, the handoffs between roles, are now slower than the engineering they are supposed to organise. Agile, or at least the heavy machinery that grew up around it, is now the bottleneck.

To be clear, the spirit of agile is not wrong. The adaptive, learn-as-you-go core is more right than ever. What breaks is the ceremony: the assumption that you should plan work in fixed batches and move it through stages, when the work itself now moves in hours.

You cannot plan a journey with no map

Here is the mental model I use instead. Building something genuinely new is a journey through territory nobody has mapped. The old instinct is to plan the whole route in advance: the sprints, the roadmap, the estimates. But you cannot plan a route through country you have never seen. The plan is fiction the moment you start walking.

So we do what sailors and explorers have always done. We set a destination, and we navigate towards it daily. Each day we take our bearings, see how far the last day's work actually got us, and set a corrected course for the next. We do not pretend to know the whole path. We commit to the direction, and we correct constantly. Repeat until we arrive.

One organism, not a relay

This only works if the people who make the decisions are in the same room, every day. Not a stand-up where everyone reports status, but a real working group where product, engineering, and domain knowledge sit together and decide on the spot, pulling on everyone's perspective at once. A fast, cross-functional organism, not a relay of handoffs where work waits in a queue between specialists.

The cadence is simple. Delivery is daily. The longest planning horizon is a week. We course-correct every day. Decisions that used to wait for a ceremony get made in the moment, by the people with the context to make them.

Faster, and better

The obvious gain is speed: a short loop means less waiting, less work sitting in queues, less time spent planning things that change anyway. But the bigger gain is quality. When the whole team decides together, every choice pulls on more knowledge than any one role has alone. And because the loop is short, you get many more iterations, many more chances to learn and correct. Better decisions, made more often, on more information.

There is an honest caveat, and the evidence is clear about it. Google's DORA research finds that AI lifts a team's throughput but damages its stability unless the foundations, testing, version control, a solid platform, are already strong. AI amplifies what is already there. Run this fast loop on weak foundations and you will simply produce your mistakes faster. The speed is earned by the discipline underneath it, not instead of it. The leverage is real, but a word of caution: cutting your team on the strength of AI does not, on its own, produce a return. This is an operating model for building better, not a headcount argument.

The teams that win the next few years will not be the ones with the best tools. Everyone will have those. They will be the ones who stopped trying to plan a journey through unmapped territory, set a clear destination, and learned to navigate to it together, one corrected day at a time.


This is roughly how I run every engagement now. If you want to see what it looks like in practice, here is the fuller version, or let's talk about your delivery.

Related: You're not a 10x engineer, you're an orchestrator · From AI that talks to AI that works · The quality you can't see is the one that kills you

Frequently asked questions

Is agile dead in the age of AI?
The adaptive, learn-as-you-go core of agile matters more than ever. What breaks at agentic speed is the heavy machinery around it: fixed two-week batches, staged handoffs, and ceremony-heavy planning, which become slower than the engineering they are meant to coordinate.
Why is agile a bottleneck now?
Because agile's ceremonies were built for a world where building was the slow part. Agentic coding makes building fast, so the planning cycles and handoffs now hold the work back rather than speeding it up.
What replaces sprint planning in the agentic era?
Setting a destination and navigating to it daily. Instead of planning fixed batches of work in advance, a cross-functional team delivers daily, keeps the longest planning horizon to about a week, and course-corrects every day based on what the last day's work revealed.
How big should an agentic-era delivery team be?
Small and stream-aligned, with the people who make the decisions in the same room. The leverage of agentic tools means a small cross-functional team can do what a larger, multi-team structure did before. But shrinking a team for its own sake does not produce a return; the model is about better decisions, not headcount.
Does moving faster with AI hurt quality?
It can, if the foundations are weak. Research from Google's DORA programme finds AI lifts throughput but harms stability without strong testing, version control, and platform quality. A fast daily loop produces better results only when the engineering discipline underneath it is solid.
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