Hiring for Agentic AI: A New Vetting Framework for CTOs

Ask ten IT leaders what “agentic AI” means for their hiring plans, and you’ll get ten different half-answers. Not because the concept is that complicated, but because almost nobody’s actually hired for it yet.

That’s the real field observation, and it’s a more useful starting point than another “AI talent shortage” headline: most of the companies we talk to aren’t competing for agentic AI engineers right now.

They’re still figuring out whether they need one.

The conversations are early, the roles are barely defined, and the resumes coming across recruiters’ desks are, frankly, ahead of the actual hiring activity.

Which is exactly why this is the hiring call most likely to go wrong. When almost nobody’s done it before, almost nobody knows what a good candidate actually looks like and that gap gets filled with buzzwords.

(If you’re not sure yet whether you actually need to hire for this or whether the real gap is somewhere else entirely, it’s worth checking first before you write the job description.)

The short answer: the resume terminology isn’t the filter. What separates a real agentic builder from someone who’s only shipped chatbots is whether they’ve actually built, watched fail, and fixed an autonomous system in production — and whether they have the judgment to be trusted with decisions the system can’t make on its own.

Everything below is how to screen for that, specifically.

What’s Real vs. What’s Hype

Here’s the distinction that matters, and the one most job postings blur completely: there’s a real difference between a Chatbot Developer — someone who wires up an LLM API, builds a conversational interface, gets a bot answering questions and an Agentic Architect — someone who designs autonomous, multi-step systems that plan, use tools, check their own work, and operate with guardrails instead of constant supervision.

Both of those people might list nearly identical technology on a resume. That’s the trap. We call the second person an Agentic Architect when most job postings just call it an agentic AI developer, which is exactly where the confusion starts.

Pulled directly from what’s actually showing up in our own candidate database at Artemis right now, here’s the kind of language doing the blurring:

• Agent and framework terms: “AI agents,” “multi-agent orchestration,” “tool use,” “ReAct,” “function calling,” “human-in-the-loop”

• Named libraries and platforms: LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, Bedrock Agents, Vertex AI Agent Builder

• RAG and knowledge patterns (often labeled “agentic” even when they aren’t): retrieval-augmented generation, vector search, GraphRAG, embeddings

• Memory and state management: conversation memory, episodic memory, state machines, session persistence

• Evaluation, safety, and governance: LLM evals, hallucination reduction, guardrails, prompt injection defense, observability/tracing

• Implementation terms: workflow automation, API integrations, structured outputs, async tool execution, MCP (Model Context Protocol)

None of that terminology is fake or disqualifying. The problem is that almost anyone who’s touched a chatbot project in the last year can legitimately use several of these terms — RAG and vector search especially show up on resumes that have nothing to do with autonomous systems at all.

Terminology tells you what someone’s been exposed to. It doesn’t tell you what they can actually build, or what happens when the system they built encounters something it wasn’t planned for. That’s the judgment gap a resume alone can’t close.

Why This AI Job Profile Is Genuinely Scarce

Here’s what we’re actually seeing in our own client base across Columbus, Cleveland, and Pittsburgh: nobody’s losing a candidate to a competing offer over this yet. No bidding wars, no counteroffers, no ‘we lost them to a faster hiring process’ stories.

At least not yet. But that’s not the same as saying the skill set is common.

“This is the part of the AI talent shortage story that doesn’t always make the trade press,” says Sarah Pervo, Chief Growth Officer at Artemis. “It’s not that the people don’t exist, it’s that almost nobody has hands-on production experience yet.”

Production-grade agentic systems — the kind that plan, execute, and recover from failure without a human catching every mistake are still new enough that “years of experience” isn’t really the filter that matters. What matters is whether someone has actually shipped one, watched it fail in production, and fixed it.

That’s a much smaller pool than the resume terminology would suggest, regardless of how competitive the local hiring market currently is.

The Vetting Framework

We’ll say this plainly: nobody has years of agentic-AI placement history to point to yet, Artemis included. The category is too new.

What we do have is a discipline we’ve built placing IT talent across enterprise applications, cloud, and development for years — backed by a 95% consultant retention rate. It’s the same practice every time: separating real technical judgment from resume terminology, no matter what the terminology of the moment happens to be.

That discipline doesn’t change just because the skill category is new. It’s the same filter, pointed at a newer target.

Here’s what that filter looks for with agentic AI specifically, in roughly the order these signals tend to reveal themselves in a conversation:

Autonomy Design	How much independent decision-making does the system actually make, and where did the candidate draw the line between “the agent decides” and “the agent asks first”? A real builder has an opinion here, grounded in a specific project. Someone who’s only shipped chatbots usually doesn’t, because they’ve never had to make that call.
Tool Orchestration	Can they describe how the system chooses between multiple available tools, and what happens when the wrong tool gets picked? This is the difference between “I integrated an API” and “I built a system that decides which API to call and recovers when it calls the wrong one.”
Failure and Eval. Handling	What happens when the agent gets something wrong? A candidate who’s actually built one of these systems will have a specific, sometimes uncomfortable story about a failure mode they didn’t anticipate. Someone who hasn’t will describe failure handling in the abstract.
Observability	Can they explain how they’d know the system was drifting or misbehaving before a human noticed? Tracing and logging aren’t glamorous, but they’re where real agentic engineering shows up in practice.
Cost and Control	Multi-step, tool-using systems can burn through API calls and compute fast if they’re not bounded properly. Has the candidate actually had to design for that constraint, or are they assuming infinite budget?
Security	Especially relevant given how often these systems touch internal tools and data — has the candidate thought about prompt injection, scope of tool access, and what an agent should never be allowed to do on its own?

One approach we’re actively developing for this: rather than relying on resume claims alone, having candidates work through a live, hands-on exercise — prompting a system in real time and walking through how they’d catch a hallucination or a bad tool call as it happens.

“We’re still refining exactly how this looks in practice,” Sarah said. “But the instinct behind it is sound: watching someone reason through a failure in real time tells you more in ten minutes than a resume tells you in ten years.”

What a CTO Almost Never Asks (And Should) When it Comes to AI Hiring

Take the “AI” out of this question for a second, because the answer isn’t AI-specific. It’s a gap we see across almost every senior technical hire: most CTOs are thorough on technical depth and surprisingly light on how a candidate handles conflict, ambiguity, and pressure.

“It’s more broad than just agentic AI talent specifically,” says Sarah. “It’s how someone handles conflict resolution, how they’ve handled genuinely difficult projects, mergers and acquisitions, large implementations, the stressful ones, and whether they’re actually the right cultural fit for how your team works, not just technically qualified for the role.”

That matters more for agentic systems than most roles, not less. These are systems that will encounter situations nobody explicitly planned for, which means the person building them needs the same judgment under ambiguity that the system itself is supposed to have.

A brilliant technical builder who falls apart the first time a stakeholder pushes back on scope is a real risk on a project like this — arguably a bigger one than a slightly less polished resume.

This is also where we’d point out something worth being upfront about: differentiating a firm in a crowded field of competitors is genuinely hard when the technology is this new.

One thing that helps: we don’t only place single contracted hires. When nobody, including the client, fully knows what the finished org chart should look like yet, we can bring in a full contract team — a project manager, a prompt engineer, and an agentic builder together — instead of asking one company to guess at which single role solves the problem.

That’s IT staff augmentation built for exactly this kind of ambiguity, not a future capability we’re still building toward.

Practical Takeaways

Resume terminology in this space moves faster than actual experience does, which means the vetting has to work harder than usual to tell the two apart.

Ask about specific failures, not just tools used. Weigh judgment and communication as heavily as technical depth. And don’t assume the person who talks about AI most confidently is the one who’s actually built something that had to work.

If you’re trying to figure out what this role actually needs to look like for your team, that’s the kind of conversation worth having with an AI recruitment agency before you write the job description, not after.