Agentic Coding: What It Is and How to Evaluate Engineers Who Actually Do It

Almost every engineer now uses AI. Far fewer know how to turn it into validated, production-ready work. That gap is the new hiring problem for engineering leaders, and classic technical interviews were not built to see it.

The numbers show the split. In the 2025 DORA report, 90% of nearly 5,000 technology professionals said they use AI at work. Yet 30% reported little or no trust in the code it generates. The 2025 Stack Overflow Developer Survey found that 66% of developers are frustrated by AI output that is “almost right, but not quite.”

So “uses Copilot” or “knows Claude Code” on a resume tells you very little. This guide explains what agentic coding is, why it changes what a strong engineer looks like, and how to evaluate that skill in a hiring process. It draws on public research and on how BEON evaluates and places AI-first engineers with U.S. companies.

What is agentic coding?

Agentic coding is a way of building software where AI agents plan, write, test and modify code across multiple steps, while an engineer sets the goal, the constraints and the bar for done. Google Cloud describes the shift as moving “from ‘chatting with AI’ to ‘assigning tasks to AI.'”

What makes it agentic is autonomy inside a loop. A coding agent can navigate a repository, run terminal commands, execute tests, read the error, and try a fix, without a human typing each step. Tools like Claude Code, Cursor’s agent mode and OpenAI Codex work this way.

That autonomy moves the engineer’s job up a level. Less time goes into typing code. More goes into framing the problem, giving the agent the right context, breaking work into safe steps, and verifying what comes back.

Agentic coding vs. vibe coding vs. AI-assisted coding

The three terms get mixed up, and the difference matters when you hire.

	AI-assisted coding	Vibe coding	Agentic coding
What the AI does	Suggests lines or functions	Generates whole features from loose prompts	Plans and executes multi-step tasks with tools
Who drives	The engineer, line by line	The prompt; the output is accepted if it seems to work	The engineer, through specs, context and checkpoints
How output is checked	Engineer reads each suggestion	Often not reviewed in depth	Tests, reviews and validation built into the workflow
Best fit	Everyday coding speed-ups	Prototypes, throwaway demos	Production work at higher throughput
Hiring risk	Low signal: almost everyone does it	Fast output that breaks at scale	Hard to fake, hard to assess with classic interviews

Vibe coding and agentic coding can use the same tools. The difference is discipline. An engineering leader at a mortgage technology company put it this way: a vibe-coded answer “may work, but break as soon as three clients use it.” Agentic coding done well means knowing when the model is wrong and saying no, even when the code runs.

Why traditional technical interviews break with agentic coding

Most technical interviews still test whether a person can write correct code from scratch, alone, under a time limit. Agents now do that part well. What they cannot do on their own is decide what to build, notice when the output is subtly wrong, and own the result in production.

Three problems show up in practice:

• Classic problems are solved in seconds. Canva found that AI tools “produced correct, well-documented solutions in seconds” to its algorithm questions. In June 2025 it replaced its computer-science fundamentals screen with an AI-assisted coding interview where candidates are expected to use Copilot, Cursor or Claude.
• Take-homes stop separating candidates. Anthropic reported that by May 2025, over 50% of candidates would have scored better by handing its take-home to Claude Code entirely. It rebuilt the test three times.
• Banning AI measures the wrong job. An AI-free interview checks a skill the engineer will rarely use alone at work, and misses the skills they will use every day: scoping, reviewing and validating.

The most useful finding came from Canva. Strong candidates asked clarifying questions, reviewed AI output critically and improved it. Weak candidates accepted the first answer. And “candidates with minimal AI experience often struggled,” even when they could code well, because they lacked the judgment to direct the tool.

That is the real signal: judgment over the agent’s work, not typing speed and not tool familiarity.

Redefining engineering speed: from lines of code to validated outcomes

Before you can evaluate an AI-first engineer, you need a clear definition of fast. Lines of code, pull requests per week and “hours saved” all reward volume. With agents, volume is cheap.

At BEON, when we say an engineer works three times faster, we don’t mean they write three times as many lines of code. The bigger change is in what the engineer’s role actually is. Engineers are becoming more domain-oriented and business-oriented, and more focused on shipping production-ready outcomes.

Knowing how to use AI tools is not enough for that. Engineers need workflows around them that cover the full cycle:

Research (understand the domain) → Plan (spec, steps, risks) → Execute (agents + engineer) → Validate (tests, review, checks) → Ship (production-ready). When validation fails, the loop goes back to planning.

Speed is how quickly an engineer moves through that whole loop and delivers a validated result. Quality is part of the definition, not a trade-off: if something ships fast but has to be reworked, it wasn’t fast.

The research supports measuring it this way:

• AI amplifies what is already there. The 2025 DORA report found AI adoption now improves delivery throughput, but still hurts delivery stability when teams lack strong testing, version control and fast feedback loops.
• People misjudge their own speed. In METR’s 2025 randomized trial, experienced open-source developers took 19% longer with AI tools, yet believed AI had made them 20% faster. METR’s late-2025 follow-up pointed to an 18% speedup for the same developers, and says selection effects probably understate the real gain.

The takeaway for hiring: self-reported productivity is unreliable, and raw output hides rework. Measure how fast validated work reaches production, and evaluate the workflow that produces it.

A framework to evaluate engineers in agentic workflows

An AI-first engineer is someone who uses AI to create impact. Sometimes that means building an automation. Sometimes it means improving a workflow, validating a plan, or validating the final output. The framework below turns that idea into things an interviewer can observe.

It has four core practices, a workflow maturity layer, and two foundations that AI does not replace.

The four core practices

Practice	What it means	What you observe
Planning	Breaks an ambiguous task into steps before asking the agent to act	Asks clarifying questions, states assumptions, writes a short plan or spec first
Prompting and context	Gives the agent the context it needs to succeed	Points to files, constraints and acceptance criteria instead of pasting the whole task
Review and verification	Treats agent output as a draft to be checked	Reads the diff, runs tests, catches edge cases, rejects code that works but is wrong
Ownership	Stays accountable for what ships	Can explain every line, owns the trade-offs, does not blame the tool

Ownership is the one most often missing. Some candidates hand the task to the agent and disengage. Others refuse to use AI at all. Neither shows the judgment the role needs.

Workflow maturity

Beyond the four practices, look at how much structure the engineer has built around their tools. Each of these is a concrete, checkable sign:

• Rules and context files (for example CLAUDE.md or Cursor rules) so the agent knows the codebase conventions
• Reusable skills and slash commands for repeated tasks
• MCP tools that connect the agent to issue trackers, docs or databases
• Subagents for research, review or testing in separate contexts
• Parallel agents and git worktrees to run several tasks at once
• Tests as guardrails, written or required before the agent’s code is accepted
• Hooks and CI checks that block bad output automatically

An engineer does not need all seven. But someone with none of them is likely chatting with AI, not working agentically.

Two foundations AI does not replace

1. Engineering fundamentals. System design, data modeling and debugging still decide whether agent output is good. As an engineering director at a mortgage technology company told us, AI makes code a commodity; what matters is “a deeper knowledge of how systems work” and the ability “to push back on the AI.”
2. Domain and business orientation. The fastest engineers understand the product and the customer well enough to know what is worth building. That is where most of the three-times gain comes from.

How BEON evaluates AI-first engineers

AI evaluation runs through BEON’s whole vetting process instead of being a separate test. Clients hire roughly one in three of the profiles we present, so each stage has to filter for the signals above.

1. Screening interview: AI habits and ownership. A structured interview records how often the candidate uses AI tools and whether they have built AI features in production. It also scores ownership, accountability and comfort with ambiguity, the traits that decide whether someone supervises an agent or hides behind it.

2. Technical interview with AI allowed. A senior engineer runs technical questions and a live coding exercise. Candidates use their usual tools, in their usual setup. The interviewer watches the process, not only the final code.

3. A structured AI scorecard. After the interview, the interviewer records:

• Tools and models used, from Claude Code, Cursor, Codex and Copilot to open-source options
• Workflow practices, from the maturity checklist above
• The four core practices (planning, prompting, review, ownership), each rated No, Usually or Always
• Hands-on experience building AI solutions: none, basic or solid
• An overall AI coding level, on three tiers:

Level	Definition	Share of candidates we interview
Native	AI is built into the daily workflow, with structure: context files, agents, verification	30%
Assisted	Uses AI regularly as a helper, with little workflow around it	48%
Non-adopter	Rarely or never uses AI for coding	22%

4. Calibrated to how your team works. Before sourcing, we align the assessment with your tech lead. A team that works almost only with coding agents gets a heavier AI weighting than one that is just starting. The scorecard travels with every profile, so your own interviews start from evidence.

What this means if you are hiring

• AI-native engineers are still the minority. Only about 3 in 10 candidates we interview work agentically. If your team already does, you are hiring from a narrow slice of the market, and your process has to be able to spot it.
• AI fluency and engineering strength are different things. Native and Assisted candidates scored almost the same on overall technical score (about 72 out of 100), and both beat Non-adopters (about 68). Score tool fluency and fundamentals separately, or one will hide the other.
• Expect Claude Code, and look for guardrails. It is now the most common agent tool we see, ahead of Copilot and Cursor. Context files, reusable skills and MCP tools are common among Native candidates; tests used as guardrails are still rare, which makes them a strong differentiator.

AI coding interview: questions, exercises and signals

You can run this in any hiring process. The goal is to see the engineer’s real workflow, then probe the judgment behind it.

Design the exercise for agents, not against them

• Use a real, messy codebase, not a blank file. Agents are good at greenfield code; engineers earn their pay in existing systems.
• Leave the requirements slightly ambiguous. Good candidates ask before they prompt.
• Plant one subtle issue, such as an edge case, a performance trap or a wrong assumption in the spec. See who catches it in the agent’s output.
• Let candidates use their own setup. Their context files, tools and habits are part of what you are evaluating.
• Ask for a short explanation at the end: what they changed, what they rejected and why.

Interview questions that reveal agentic skill

1. Walk me through the last feature you shipped with an AI agent. What did you do before you wrote the first prompt?
2. How do you give an agent context about a codebase it has never seen?
3. Tell me about a time an agent produced code that worked but was wrong. How did you notice?
4. What do you never delegate to an agent, and why?
5. How do you check agent output before it reaches code review?
6. What rules, skills or automations have you built around your tools?
7. When do you run several agents in parallel, and how do you keep their work from colliding?
8. How has AI changed the way you split your time between coding, reviewing and talking to the business?

Green flags and red flags

These are patterns we see again and again in real interviews.

Green flags	Red flags
Writes a structured prompt with file paths, the objective and constraints, such as keeping a schema config-driven instead of hardcoded	Pastes the whole exercise into a chat window and copies code back and forth
Plans with the agent, then reviews its fixes for code smell against acceptance criteria	Treats the agent as a hands-off delegate and stops supervising once it starts
Has context/rules files, subagents and parallel tasks, and picks the model by task complexity	No setup at all; switches to a browser chat when the editor has no AI configured
Chooses not to use AI on a step and can explain why	Cannot explain parts of the code the agent wrote
Catches the planted bug in the agent’s output	Accepts the first answer that compiles
Talks about outcomes: the user, the business rule, the production risk	Talks only about tools and prompts

One nuance: a candidate who skips AI in the exercise is not automatically a weak hire. Some make a deliberate choice and implement and debug the solution comfortably on their own. What matters is whether they understand where AI fits in the workflow, and whether they could work at the team’s pace.

What AI-first engineers deliver: the Aloha case

Aloha is a healthcare platform serving more than 5,000 practitioners. BEON engineers on the Aloha team built AI into their whole workflow, not just the coding step:

• Scoping and planning the work with AI before implementation
• Generating and reviewing code, including AI-assisted pull request reviews
• Debugging production issues with AI support
• Custom agents and automations that catch problems before they reach users

Metric	Before	After
Projects each engineer manages in parallel	1	3–4
Full mobile app rebuild	—	2 weeks
New web app launch	—	1 day
Post-release defects	Baseline	Lower
Code review scores	Baseline	Higher

The last two rows matter most. Speed went up and quality went up with it. That only happens when validation is part of the workflow, which is exactly what the framework above is designed to detect in an interview.

Read the full Aloha success story for how the team was built and scaled.

Agentic coding scorecard template

Copy this into your interview kit. Rate each practice No / Usually / Always, and write one line of evidence from the session.

Dimension	No	Usually	Always	Evidence from the session
Planning: clarifies and plans before prompting
Prompting and context: gives files, constraints, acceptance criteria
Review and verification: tests, reads diffs, catches the planted issue
Ownership: explains every change, owns trade-offs
Workflow maturity: context files, skills, MCP, subagents, tests, hooks
Fundamentals: design and debugging hold up without the agent
Domain orientation: reasons about users, business rules, production risk

Overall AI coding level: Native / Assisted / Non-adopter

How to read it: hire for fundamentals and ownership first, then weigh the AI level against how your team works. Ask for “Always” on review and ownership for roles with production responsibility.

FAQ

What exactly is agentic coding? It is software development where AI agents plan, write, test and fix code across multiple steps, using tools like the terminal and the test suite. The engineer sets the goal and the constraints, and validates the result.

What is the difference between agentic coding and vibe coding? Vibe coding accepts AI output if it seems to work, which suits prototypes. Agentic coding adds planning, context and verification so the output is production-ready. The tools can be the same; the discipline is different.

What are some examples of agentic coding? An agent that takes a ticket, reads the relevant files, drafts the change and runs the tests. Parallel agents fixing separate bugs in isolated git worktrees. Automated agents that review pull requests or flag risky changes before release.

Is agentic coding a trap? It can be when output is not verified. The 2025 DORA report links AI adoption to lower delivery stability when testing and feedback loops are weak. With strong validation, the same tools raise both throughput and quality.

Should candidates be allowed to use AI in coding interviews? For most roles, yes. Banning AI tests a way of working the engineer will rarely use. Companies like Canva now expect candidates to use AI and evaluate how well they direct and review it.

How do you measure developer productivity with AI? Measure how fast validated work reaches production, and how much of it has to be reworked. Lines of code, PR counts and self-reported time savings are unreliable in agentic workflows.