The Enterprise AI Agent Readiness Gap
I'll be honest, I got AI agents wrong at first.
When I first started working with agentic AI, I treated it like the next version of what we'd already been doing with chatbots. Better prompts, more tools connected, maybe some memory across sessions. An upgrade, not a different thing.
Then I watched an agent execute a transaction. Not suggest one. Not draft one for review. Execute it.
That's when it clicked. With chatbots, we were looking at text. We could read the output, catch the hallucination, and fix it before anything happened. Agents don't work that way. They run the transaction. They sent the email.
They update the record. They act like a human colleague would, except they don't pause to double-check with you first.
That changes everything about how you need to think about deploying them.
What I'm Actually Seeing
Most organizations I talk to right now fall into one of two camps.
The first group is stuck evaluating. They've seen the demos, they've got a shortlist of frameworks, and they've maybe done an internal presentation. But nothing has shipped because nobody has answered the uncomfortable questions, if this agent processes the wrong invoice, whose problem is it? If it emails a client something incorrect, who knew it had that access?
The second group built something. They connected an LLM to a few tools, got a prototype running, and showed it to leadership. It looked great. But it's been sitting in that demo state for months because moving it to production means answering all those same uncomfortable questions, and nobody wants to own that conversation.
The technical part, connecting models to tools, building the workflow, that's honestly the easier bit now. The frameworks are there. The hard part is everything around it.
The Questions Nobody Wants to Answer
Here's what I keep asking teams, and where I usually get silence:
Where does the agent stop and the human start? Most teams haven't drawn this line. So they either lock the agent down so much that it can barely do anything useful, or they give it broad access and hope for the best. Neither works.
What happens when it gets step 6 wrong in a 10-step workflow? Does it roll back? Retry? Keep going? Escalate?
Most prototypes don't handle this at all. The agent just... continues. MIT just released a framework called EnCompass that addresses exactly this, letting agents backtrack and retry failed paths. The fact that this is a research problem tells you how early we still are.
Can you actually trace what it did? Not what it was supposed to do, what it actually did. Which data it read, which systems it touched, and what reasoning path it followed. If your compliance or security team can't get a clear answer on this, the project isn't going to production. And they're right to block it.
Do you even know what the agent has access to? MCP is becoming the standard for connecting agents to tools and data, which is great. But most organizations haven't mapped their own tool and data surfaces. You can't set permissions on systems you haven't inventoried.
What I Think About Differently Now
A year ago, my GenAI maturity conversations with organizations were about chatbot guardrails, content filters, prompt safety, and making sure the output was accurate and appropriate.
With agents, the governance question is fundamentally different. It's not about what the AI is allowed to say. It's about what it's allowed to do.
I used to evaluate agent initiatives by asking, "What can this build?"
Now the first question I ask is "what happens when it does the wrong thing?"
Because every team can show me an impressive demo. Very few can tell me their plan for when the agent makes a mistake in production at 2 am on a Saturday.
That answer, or the lack of one, tells me more about whether the project will actually ship than any technical architecture diagram.
So Here's the Question
If you're working on AI agents right now, ask yourself:
Do we know who's accountable when the agent gets it wrong, or are we just hoping it won't?
Because the gap between a demo agent and a production agent isn't about picking a better model or a better framework. It's about deciding who owns the workflow, who reviews the agent's decisions, and what the fallback is when things go sideways.
Most teams haven't started that work yet. And until they do, the agents stay in demo mode.
