The first agent demo is always charming. Someone shows you a bot that drafts a reply, summarizes a meeting, or pulls an answer from a labyrinth of documents that normally takes three people and two days to navigate. Everyone nods. A few people smile. For a brief moment, you can almost hear the organization exhale.
Then I ask the question that ruins the mood. What happens when it’s wrong?
Not wrong in the abstract. Wrong in the way production is wrong. Wrong in the way an email goes to the wrong recipient, a ticket gets closed too early, a policy gets bypassed, a number ends up in a deck and starts living its own life. Wrong at scale, on a Tuesday morning, when nobody has time for a philosophical debate about “probabilistic systems”.
That’s usually when we stop talking about innovation and start talking about accountability. Agentic AI changes the nature of the conversation because it changes the nature of the actor. A copilot helps someone do a task. An agent can do the task, take intermediate decisions, use tools, and escalate only when it decides to. The moment it can touch systems of record, trigger workflows, or communicate externally, you have created an operational entity. It is not a toy anymore, even if the interface still looks playful.
This is why the “pilot era” ends faster than people expect. Early pilots live in a protected bubble. Limited scope, friendly users, lots of supervision, and very forgiving success criteria. The enterprise does not break because the enterprise is not really involved yet.
Scaling is when the enterprise shows up. And the enterprise has a way of asking unfashionable questions. Who owns the outcome. Who is on the hook when the agent’s action creates damage. What evidence exists when someone challenges a decision. What controls prevent the obvious abuse. What the unit economics look like when usage is multiplied by ten, then by a hundred.When those questions arrive, the right response is not to slow down and create a committee for the sake of optics. It is to industrialize. To build an Agent Factory. I like the word “factory” because it removes the romance. Factories exist to produce reliable outcomes repeatedly, with known quality, known costs, and known safety mechanisms. They also include something we tend to forget in tech: retirement. A factory knows how to stop producing a model when it no longer makes sense. A factory doesn’t like bullshit.
An Agent Factory is an operating model that makes agents boring in the best possible way. It starts with a decision that sounds simple and is surprisingly hard to enforce: agents are not bespoke. You build them on a paved road. Shared identity patterns, shared access control, shared logging, shared deployment discipline, shared monitoring, shared lifecycle rules. Not because people enjoy standardization, but because standardization is what allows speed without fragility.
Without that paved road, the organization doesn’t scale agents. It multiplies exceptions. Teams ship their own versions, each one slightly different, each one harder to secure, each one harder to observe, each one harder to support. After a few months you have a bot zoo. It feels like progress until the first incident, and then it feels like debt. If you want a simple test for whether you are doing this responsibly, I look for three capabilities that should exist before you call anything “production”.
First, containment.
In a mature environment, you can stop an agent the way you can stop an industrial line: immediately, cleanly, and without waiting for a meeting. That means a real kill capability, not a policy statement. You can disable the agent, revoke its credentials, and freeze its tool access fast. You can also narrow the scope when needed, because “stop everything” is rarely an acceptable option in a global business that runs across time zones.
People sometimes hear this and assume pessimism. I see it as respect. Respect for operations, respect for risk, respect for the fact that mistakes happen. Safety mechanisms are not an admission of failure. They are what allows confidence.
Second, traceability.
At some point, someone will challenge an outcome. It might be an auditor, a regulator, a business leader, a customer, or simply a colleague who needs to understand why an action happened. “Because the model decided” is not an acceptable explanation in an enterprise setting. You need a flight recorder. What the agent received as input, what context it retrieved, which tools it called and in what order, what it produced, and what approvals happened along the way. Versions matter too. Prompts evolve, policies evolve, connectors evolve. If you cannot reconstruct a decision path with reasonable clarity, you cannot defend your system, and you will eventually lose trust internally. Trust, once lost, does not come back because you published a new guideline.
Third, unit economics.
Pilots often look cheap because they are small, supervised, and funded through budgets that are designed to tolerate ambiguity. Production is different. Production requires cost discipline that survives scale.
An agent’s cost is not only model usage. It includes tool calls, orchestration overhead, monitoring, human supervision, quality correction, incident handling, and the hidden tax of rework when outputs are “almost right” but still require someone to clean up. If you cannot express the economics as a unit cost per case handled, per transaction completed, per hour saved, or per risk avoided, you will not manage it. You will argue about it.
That argument tends to get unpleasant when Finance is in the room. For this reason, it is often more permanent to launch an agent that responds to an existing use case and allows you to improve it and, above all, measure that improvement, than to launch an agent that responds to a new need whose business value will be difficult to measure. Once those fundamentals exist, the next move is portfolio discipline. Not every workflow deserves an agent. Not because the technology cannot do it, but because accountability cannot be diluted infinitely. The moment you cannot name the business owner, the KPI, and the risk boundary, you are not launching a capability. You are launching a question mark.
The best portfolios I’ve seen stay surprisingly small at the beginning. They focus on high-frequency friction, clear ownership, and measurable outcomes. They treat agents like products, not like demos. They accept that retirement is part of maturity. If an agent stops delivering value, you either fix it or you remove it. Keeping an agent alive because it was once celebrated is how “innovation” becomes organizational clutter.
Service levels matter here, but not in the usual IT sense. Uptime is table stakes. An agent can be available and still be wrong, unsafe, slow, or too expensive. The more meaningful conversation is about reliability, output quality, safety behavior, latency within the workflow, and cost boundaries that don’t drift silently. A particularly honest metric is escalation rate: how often the agent hands work back to a human, and why. When that rate starts climbing, something is deteriorating. Data quality, workflow clarity, permissions, tooling stability, or the agent itself.
This is where the factory becomes valuable. It turns drift into signals, not surprises. If you want a practical way to start without building a cathedral, I recommend a disciplined small bet. Pick two workflows with real owners and real consequences. Build both through the same factory path, using the same platform components and the same governance gates. Refuse exceptions, even when someone says “just this once, we need speed”. You are not proving that agents are possible. Everyone knows they are possible. You are proving that they are operable.
Scale actually matters. In 2026, most organizations will have agents. That fact alone will not impress anyone for long, especially not internationally, where complexity and scrutiny are higher. The differentiator will be who can run agentic systems without losing control, without losing trust, and without turning every new use case into a negotiation with risk and security.
The ambition should be simple: build a machine room, not a magic show.