{"id":651950,"date":"2026-03-13T05:27:12","date_gmt":"2026-03-13T05:27:12","guid":{"rendered":"https:\/\/www.europesays.com\/us\/651950\/"},"modified":"2026-03-13T05:27:12","modified_gmt":"2026-03-13T05:27:12","slug":"how-we-hire-ai-native-engineers-now-our-criteria","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/us\/651950\/","title":{"rendered":"How we hire AI-native engineers now: our criteria"},"content":{"rendered":"

How do you hire engineers when agents write 99% of the code?<\/strong><\/p>\n

We found ourselves asking that question after realizing our hiring process was built for a different world. Software engineers have always done more than just write code, but the ability to write code very well was the first pre-requisite to getting hired.<\/p>\n

But, as agents get better at implementation, the engineers who create the most leverage are the ones with product taste, architectural judgment, and the ability to direct systems of humans and agents toward the right outcome.<\/p>\n

At Augment, we’ve been writing openly about going AI-native<\/a> and what it actually takes. This is the next chapter of that story.<\/p>\n

As engineers begin working alongside increasingly capable AI agents, the nature of the job is shifting. Less time goes into writing code itself, and more time goes into deciding what should be built, designing systems that will hold up in production, orchestrating agents, and aligning teams around clear outcomes.<\/p>\n

Coding still matters. But increasingly, it\u2019s the part machines can help with.<\/p>\n

More important now is judgment<\/strong>: the ability to choose the right problems, make sound architectural decisions, and direct both humans and agents toward meaningful outcomes.<\/p>\n

If you zoom out, the shift looks something like this:<\/p>\nTraditional EngineeringAI-Native Engineering <\/tr>\n\nWriting code <\/td>\nSpecifying intent and evaluating tradeoffs<\/td>\n<\/tr>\n\nImplementing solutions <\/td>\nOrchestrating agents<\/td>\n<\/tr>\n\nSolving problems<\/td>\nChoosing the right problems <\/td>\n<\/tr>\n\n Individual output<\/td>\nSystem-level outcomes<\/td>\n<\/tr>\n

Recognizing that the craft of engineering is moving up the abstraction stack led us to a simple question:<\/p>\n

In an AI-native environment, what capabilities actually separate exceptional engineers from good ones?<\/p><\/blockquote>\n

It\u2019s helpful to ground in Augment\u2019s definition of AI-native engineering<\/a>:<\/p>\n

The human role is shifting from\u00a0author\u00a0to\u00a0architect and editor. You’re defining the intent, making design and trade-off decisions, setting guardrails, obsessing over user experience, and being the last line of quality.<\/strong><\/p><\/blockquote>\n

With that in mind, we took half a day with a cross functional team of EM\u2019s, IC\u2019s and recruiters to go deep on this topic and think about this from first principles. From this discussion, six capabilities consistently surfaced. These are the dimensions we believe will matter most as engineering becomes increasingly AI-native.<\/p>\nDimension Core Question <\/tr>\n\nProduct & Outcome Taste <\/td>\nAre we building the right thing?<\/td>\n<\/tr>\n\nSystem & Architectural Judgment <\/td>\nWill this survive production? <\/td>\n<\/tr>\n\nAgent Leverage <\/td>\nCan you turn AI into real engineering throughput?<\/td>\n<\/tr>\n\nCommunication & Collaboration <\/td>\nCan you communicate intent clearly and collaborate across perspectives? <\/td>\n<\/tr>\n\nOwnership & Leadership <\/td>\nDo you drive outcomes, not just tasks? <\/td>\n<\/tr>\n\nLearning Velocity & Experimental Mindset<\/td>\nCan you evolve as fast as the tools?<\/td>\n<\/tr>\n

One thing you may notice missing from this list: raw coding ability as a standalone dimension. Coding still matters, but it\u2019s no longer the primary differentiator of engineering talent.<\/p>\n

Product & outcome taste<\/p>\n

Are we building the right thing?<\/strong><\/p>\n

As code becomes cheaper to produce, the most expensive mistake is building the wrong thing. Engineers increasingly need to investigate user problems, cut through ambiguity, and define clear outcomes before implementation begins.<\/p>\n

The highest-leverage engineer isn\u2019t the one who writes the most code. It\u2019s the one who ensures the team is solving the right problem.<\/strong><\/p><\/blockquote>\n

System & architectural judgment<\/p>\n

Will this survive production?<\/strong><\/p>\n

Agents can generate working code, but they are far less reliable at judging whether the system around it is sound. Architectural judgment still requires understanding long-term tradeoffs, operational realities, and the hidden risks that emerge at scale.<\/p>\n

\u201cIt works\u201d is easy. \u201cIt will keep working in production\u201d is much harder.<\/strong><\/p><\/blockquote>\n

Agent leverage<\/p>\n

Can you turn AI into real engineering throughput?<\/strong><\/p>\n

AI-native engineers don\u2019t simply use agents for assistance. They structure problems so agents can execute effectively, guide them when they drift, and validate the results they produce.<\/p>\n

Think of it as delegation \u2014 except your reports are incredibly fast and occasionally confidently wrong.<\/strong><\/p><\/blockquote>\n

Communication & collaboration<\/p>\n

Can you communicate intent clearly and collaborate across perspectives?<\/strong><\/p>\n

As implementation speeds up, more of the work shifts toward clarifying problems, surfacing tradeoffs, and incorporating input from different parts of the team. Engineers increasingly need to communicate clearly, listen well, and build shared understanding quickly.<\/p>\n

The fastest teams aren\u2019t the ones that code the fastest \u2014 they\u2019re the ones that reach clarity the fastest<\/a>.<\/strong><\/p><\/blockquote>\n

Ownership & leadership<\/p>\n

Do you drive outcomes, not just tasks?<\/strong><\/p>\n

Great engineers own outcomes end-to-end, not just their piece of the code. When something blocks progress \u2014 whether it\u2019s slow builds, unclear workflows, or gaps between systems \u2014 they step in and fix it, even if it\u2019s outside their immediate scope.<\/p>\n

Ownership means removing whatever stands between the team and the outcome.<\/strong><\/p><\/blockquote>\n

Learning velocity & experimental mindset<\/p>\n

Can you evolve as fast as the tools?<\/strong><\/p>\n

Live session \u00b7 Fri, Mar 13<\/p>\n

How a principal designer goes between canvas and code<\/p>\n

Mar 13\/5:00 PM UTC\/Speaker: Amelia Wattenberger<\/p>\n

The tools we use today will not be the ones we use three months from now. Engineers who thrive here experiment constantly, change their workflows quickly, and drop old approaches when better ones show up.<\/p>\n

Experimentation isn’t a phase. It’s the job now.<\/strong><\/p><\/blockquote>\n

A framework only matters if it changes how you hire. The next step was translating these dimensions into observable signals<\/strong> \u2014 behaviors we can evaluate during interviews.<\/p>\n

For example:<\/p>\n