A student asked me last month what they needed to master to be in the top tier of AI-native developers.
I expected to say: prompting. Or model selection. Or maybe the ability to architect complex agent pipelines.
I said: learn to manage people.
They stared at me. "I thought this was about AI."
It is. That is exactly the point.
I have spent the last several months studying how the best engineers actually work — the ones operating in the top 0.1% of AI-native workflows. The patterns are consistent, and they are not what most people expect. The clearest way I know to explain what I found is this framework.
Table of Contents#
- The perfect storm nobody talks about
- What AI-native actually means
- The iterative approach that actually works
- The agent-friendly codebase
- Functional software versus incredible software
- Why junior engineers might actually win
- The experimentation imperative
- The allocation of intelligence
- FAQ
The perfect storm nobody talks about#
To understand why the AI-native engineer looks different from what you would expect, you need to understand what created this moment. Three forces collided, and none of them alone would have been enough.
The first was the post-COVID hiring surge. In 2021, companies were expanding fast. Headcounts ballooned. Entire engineering orgs doubled in eighteen months. Then came the correction: the realization that many of those companies could cut twenty to thirty percent of their workforce and still function. The layoffs that followed were large enough to shift the entire market.
The second force was a decade of growth in computer science graduates. The CS major roughly doubled, possibly tripled, in the number of students over the last ten to fifteen years. A tidal wave of new engineers entered a market that was already contracting.
The third force is the one that changes the math entirely. AI became genuinely useful. Not demo-useful. Not research-useful. Productively, daily useful for writing code, debugging systems, designing architectures. Employers started asking a question that would have sounded like science fiction five years ago: do we hire more people, or do we hire fewer people who are native at AI?
That question is now the subtext of nearly every engineering hiring decision. And it created a new class of engineer, one where AI is not a tool you pick up for certain tasks. It is the language you think in.
What AI-native actually means#
At its core, the AI-native engineer has a strong foundation in traditional programming, system design, and algorithmic thinking. But they are also deeply competent at agentic workflows — running agents, directing them, knowing when to intervene and when to step back.
The framework above shows how these two layers combine. Traditional CS discipline provides the foundation. The AI-native layer, agentic workflows, multi-agent orchestration, context-switching, agent management, sits on top. But notice what is at the center of both layers. Not the technology. Management.
This is the part that surprises people.
The engineers who do multi-agent work best are not necessarily the ones with the strongest CS fundamentals, though those matter. They are not the ones with the most AI experience. The trait that shows up most consistently among the developers who can orchestrate multiple agents without the whole system collapsing is that they have managed humans before.
They understand how to track what three different direct reports are working on simultaneously. They know how to shift attention between parallel workstreams without losing the thread of any of them. They have developed the instinct to check in at the right intervals, to notice when something is drifting before it compounds into something much harder to fix.
Think about what multi-agent orchestration actually involves. You have two, three, four agents working in parallel. Each one is making decisions, generating code, taking actions based on its current context. You need to know what each one is doing, whether its approach is sensible, where it might go wrong, when to intervene and when to leave it alone. You need to spot when two agents are about to create a conflict, before they create it.
That is management. Not metaphorically. In a real, operational sense.
Adding more agents does not automatically improve output. It often makes things significantly worse. Agents compound errors. One wrong assumption in step one gets built on in step two, then reinforced in step three. Spaghetti code does not emerge from bad agents. It emerges from unsupervised agents — exactly the way spaghetti processes emerge from unsupervised teams. If you have ever seen what happens when ten interns all work on the same codebase with no coordination, you have a preview of what ten unsupervised agents produce.
The iterative approach that actually works#
There is a well-known developer at Anthropic who runs ten Claude Code agents simultaneously. This fact travels fast in developer circles. And the natural reaction, understandably, is: I should do that too.
Do not start there.
The practitioners who get to that level of multi-agent fluency did not start there either. They started with one. They built something meaningful with a single agent, got genuinely confident in how it handles its scope, and only then identified a second task — something isolated, something that does not overlap with what the first agent is doing. One agent updates the logo. Another updates the header copy. These are independent tasks with clear boundaries and no shared files.
Only when the first agent's behavior feels predictable does a second agent enter the picture. Then a third. Piecemeal, the way a good manager onboards new team members. You do not give ten interns a project on day one and come back a week later. You bring people in deliberately, make sure each one is effective before adding the next, build up the coordination capacity gradually.
The context-switching is where the real skill lives. Watching two terminals at once, maintaining an accurate mental model of what each agent is doing, knowing when one is about to go sideways before it does — that is what takes time to develop. It is not a skill you can read about. You build it by doing it carefully, at low agent counts, until the pattern recognition becomes automatic.
For a practical walkthrough of how to structure this progression, I covered the specific steps in Before You Run 10 Claude Agents. The short version: one agent, then two isolated tasks, then three. And between each step, make sure the current level feels manageable before you add more.
The agent-friendly codebase#
Here is something that does not get nearly enough attention: the codebase itself determines whether agents succeed or fail.
Most conversations about multi-agent workflows focus on prompt design, model selection, orchestration patterns. Very few focus on the thing that an agent encounters before any of that matters — the actual state of your code.
If you released a Claude Code agent into your codebase right now, would it understand what is happening? Would it find consistent patterns to follow? Would it be able to verify whether its own work is correct?
The framework identifies three things that matter most.
Tests as contracts. Agents do not have colleagues to ask when they are uncertain. They operate on explicitly defined contracts — and your tests are those contracts. Without test coverage, an agent has no mechanism to verify its own work. It writes code that looks correct, and it has no way to discover that it is not. Every agent you run in an undertested codebase is flying with its instruments off.
README consistency. READMEs get out of date. This is an accepted fact of engineering life that every developer has made peace with. For a human engineer, a stale README is annoying but manageable — you read the code, you ask someone, you figure it out. For an agent, a stale README is a forced choice with no good options. When the code says one thing and the documentation says another, the agent has to pick one to believe. It will sometimes pick the wrong one, and it will not know it picked the wrong one.
Pattern uniformity. If part of your codebase creates objects using one API and another part uses a completely different API for the same purpose, which should an agent follow? A new engineer would ask. An agent picks one and commits. Sometimes it picks the worse one. Sometimes it introduces a third approach. In either case, whatever inconsistency existed in the codebase before the agent arrived exists in amplified form after.
The most important preparation you can do before running agents is not about the agents at all. It is about making sure your codebase is self-consistent. Well-tested, accurately documented, pattern-consistent. These are not agent problems. They are codebase hygiene problems that agents reveal with unusual efficiency.
I covered the architecture side of this in more depth in Building Production-Ready Multi-Agent Systems — particularly around how typed interfaces between agents prevent the error-compounding problem from becoming catastrophic.
Functional software versus incredible software#
The framework has a section that tends to generate the most discussion: functional versus incredible software, and the concept it calls "taste."
The best AI-native engineers are not the ones who hit the requirements and stop. They are the ones who keep going after they hit one hundred percent. They see something in their project — an edge case worth handling, a feature worth expanding, a pattern worth refining — and they cannot stop thinking about it.
This quality has nothing to do with AI and everything to do with being a certain kind of engineer. The last mile — where you iterate one more time, polish the edge case nobody asked about, make the system genuinely good rather than technically correct — is where taste develops. And taste is what separates software you ship and forget from software you are still proud of three years later.
Some of the engineers I have seen develop the most sophisticated AI-native skills are the ones who started treating their agent-assisted projects the same way they treated their best solo work. Not as a faster way to generate code, but as a way to raise the ceiling on what one person could build. The agents handle the execution. The taste, the judgment about what should be built and what good looks like, stays with you.
Agents do not have taste. They have capability. The combination of capable agents and an engineer with taste is something qualitatively different from either one alone.
Why junior engineers might actually win#
The contrarian take here is one I have become more confident in over time: junior engineers may be better positioned for this shift than many senior developers.
Senior developers with twenty years of experience carry two things that matter enormously in normal circumstances. Deep technical knowledge and strong intuitions about how to do things. In an AI-native workflow, the knowledge still matters. The intuitions sometimes work against them.
The most resistant engineers I have seen to adopting agentic workflows are often the most experienced. They have earned their expertise through years of doing things a particular way. That expertise is real. But it creates grooves that are hard to leave. When a tool asks you to think about your work differently, experience can become inertia.
Junior engineers do not have those grooves yet. They are sponges. They have not yet internalized that healthcare software is uniquely difficult, or that enterprise systems are inherently messy, or that "we tried that in 2018 and it did not work." They approach new problems with less baggage, which means they can build new instincts faster.
There is a useful naivety in that perspective, the same quality that makes some of the best startup founders. Not knowing enough to be scared is an asset when the landscape is genuinely changing. And the landscape is genuinely changing.
This does not mean fundamentals stop mattering. The ability to decompose complex systems, reason about algorithms, debug failures, understand what is actually happening inside a piece of software — these matter more in an agent-driven workflow, not less. Agents do not think for you. They amplify your thinking. If your thinking is shallow, agents will amplify that too. The CS discipline is the foundation on which the AI-native layer sits, and the foundation has to be solid.
But among engineers who have solid fundamentals, the ones adapting fastest right now are often the ones with fewer years of experience. That is worth paying attention to if you are hiring, teaching, or trying to figure out where to place your own bets.
The experimentation imperative#
Even the teams building the most sophisticated AI tools are figuring things out as they go. The Claude Code team at Anthropic essentially rewrites their own software every week or two, using Claude. They experiment constantly, iterate based on what they learn from users, and they will tell you directly that they do not have all the answers.
If the people building the tools are still experimenting, you should be too.
This is not a reassuring thing to say about an industry. It is an accurate one. The workflows that are most productive today are different from the workflows that were most productive six months ago. The best practices for running two agents in parallel are different from the best practices that will exist when tooling matures further. Nobody has the final answer yet.
The engineers who are thriving in this environment are the ones who have made experimentation a permanent mode rather than a phase. They try things. They see what works for their specific context and their specific codebase. They adjust. They try again. Hacking and testing and discovering is not a way to get started. It is the job.
The engineers who are struggling tend to be waiting for the field to stabilize before they commit to learning it properly. That stabilization is not coming on a timeline that is useful to them. The window for building genuine fluency while the tooling is still evolving, and before the skills become table stakes rather than differentiators, is right now.
If you want to see how this plays out in a specific domain, the LangGraph Deep Dive is a good example of what happens when you commit to understanding one tool deeply rather than staying at the surface of many.
The allocation of intelligence#
The most clarifying framing I have encountered for what is actually happening comes from researchers studying AI and entrepreneurship. The key shift is this: what matters now is your ability to allocate intelligence.
The naive version of AI adoption is using AI to do your existing work faster. Write code faster. Debug faster. Document faster. This is real value. But it is not the deepest level of what is available.
The more significant shift is embedding AI in the product so that the AI works directly with the customer. Moving the human out of the loop — not because the human is unnecessary, but because the human becomes the architect of the loop rather than a participant in it. The human decides where intelligence flows, what constraints it operates under, what outcomes it is optimizing for. That is a different job from the one most engineers have today.
And then there is the question beyond that, the one nobody has fully answered yet. What happens when AI agents start collaborating with each other? What do they need from one another? What emerges when you stop designing for human-to-agent interaction and start designing for agent-to-agent interaction?
The engineers who will answer that question are the ones developing multi-agent orchestration instincts right now. Not because they have read about it. Because they have watched two agents conflict with each other, figured out why, fixed it, and learned something from the failure. Because they have hit the limit of what one agent can do and worked out, through trial and error, how to extend it with a second without compounding the problems.
The management skills. The codebase hygiene. The taste for what "incredible" looks like. These are not soft skills adjacent to the real technical work. They are the real technical work of the AI-native era.
FAQ#
What is an AI-native engineer?#
An AI-native engineer has a solid foundation in traditional CS, including system design, algorithms, and debugging, and is also deeply fluent in agentic workflows. The key distinction from someone who "uses AI tools" is that for an AI-native engineer, working with agents is not a workflow addition. It is the primary mode of work.
Why does management experience matter for multi-agent orchestration?#
Running multiple agents in parallel requires the same mental skills as managing a team of people: tracking parallel workstreams, knowing when to intervene, preventing work from overlapping in destructive ways, and maintaining an accurate model of what each "direct report" is doing. Engineers with management backgrounds tend to develop multi-agent fluency faster because those instincts are already built.
Do junior or senior engineers adapt faster to AI-native workflows?#
Based on what I have observed, junior engineers often adapt faster, despite having less experience overall. They have fewer ingrained habits to overcome, and they approach new tools without assuming they already know how they work. Senior engineers have deeper foundational knowledge, which matters, but it sometimes creates resistance to genuinely new ways of working.
What makes a codebase agent-friendly?#
Three things matter most: adequate test coverage so agents can verify their own work, accurate documentation that matches what the code actually does, and consistent design patterns that an agent can infer and follow. An agent in a self-consistent, well-tested codebase performs significantly better than the same agent in a codebase with inconsistencies and stale documentation.
How many agents should I be running right now?#
As many as you can actually oversee. For most developers starting out, that is one. Once you have genuine confidence in how a single agent performs, add a second for an isolated task. Build up gradually. The number is not a target. It is a ceiling that rises as your orchestration skills develop.
Is this shift permanent or is it a phase?#
The specific tools will change. The workflows will evolve. But the underlying shift — toward fewer engineers managing more AI capability, with orchestration and judgment becoming more valuable than raw coding throughput — looks structural rather than cyclical. The exact skills will keep developing, but the direction of the shift seems durable.
What should I focus on learning first?#
Fundamentals, then one agent, then orchestration. The CS foundation matters more in an agentic workflow, not less. On top of that, get genuinely fluent with a single Claude Code agent on a real project before adding complexity. The orchestration skills compound quickly once the foundation is solid.
A class called "The Modern Software Developer" recently opened for enrollment and filled within hours. One hundred students competing for seats. The entire focus: AI across the software development lifecycle.
Something has shifted. The question is not whether to engage with it. The question is which instincts to build first.
The engineers I have seen develop the most effective AI-native workflows all started the same way. One agent. One real project. Patient enough to understand how it works before scaling to many. The management skills, the codebase hygiene, the taste for what good looks like — these compound. They are not shortcuts. They are the path.
What are you discovering in your own multi-agent experiments? The failure modes I see tend to repeat. The solutions usually do too.