Stuck in Demo Land—How Trust Can Get Agentic AI Into Production

When you buy a new car, you want to take it for a spin right away to see what it can do, right? 

I see a similar impulse keeping us focused on all the amazing things agentic AI will make possible. Not a bad impulse, but it’s symptomatic of tool-thinking, a paradigm we’ve enjoyed for most of human history. We like to see new tools in action. 

With agentic AI, we’re moving beyond tools into the world of a digital workforce, where agents are teammates and collaborators and enjoy a measure of independence. To make that work in the real world at scale, you need a solid tech stack as a foundation. 

But foundations must seem boring, because so much of the conversation I hear seems stuck on the agents and getting lots of pilots going to see what they can do. The result? Impressive demos that never quite make it into production.  

The solution: pay attention to what lies beneath. 

The iceberg below the agents 

I think about operational AI as relying on five distinct layers: data, knowledge, orchestration, trust, and action. The agents we’re so eager to see in action may be the most visible part of the stack, but they are still only the tip of the iceberg. 

Below the waterline are the things that make that action useful and safe: clean, current data; a knowledge layer that supplies real context; orchestration that coordinates dynamic workflows; and a trust layer that governs what can happen in the first place. 

And trust turns out to be, arguably, the most critical layer. 

For a long time, trust meant usernames, passwords, access controls, and human judgment. Agentic AI changes that model. As digital teammates take on real slices of work, they retrieve information, coordinate with other agents, invoke tools, trigger workflows, make recommendations, and, in most cases, take action across multiple systems. 

This autonomous action changes the stakes. 

Why trust matters most 

Would you hand the keys of that new car we mentioned earlier to an unlicensed fourteen-year-old stranger? Probably not.  

Turning agents loose in your business raises similar concerns: can they be trusted? Especially since their impact rises dramatically as they work independently and in tandem across workflows.  

All the organizations I work with have identity and access controls. They can answer familiar questions: Can this user log in? Can this role access this application? Does this account have permission to view or modify this data? While necessary, they’re not enough.  

In an agentic environment, the critical question becomes: “Is this specific action appropriate for this agent, on behalf of this person, at this moment?” 

That’s a governance question, not an access-control question.  

An agent may technically be able to access a customer record. That doesn’t mean it should use that information in this interaction. An agent may have the ability to issue a credit, change a network configuration, contact a customer, invoke a tool, or trigger another workflow. That doesn’t mean the action is appropriate given the customer, the event, the business policy, or the objective at hand. 

If you don’t resolve that question before an agent acts, you create a problem that is very difficult to manage after the fact. You could expose sensitive information or violate policy and have a workflow problem moving at machine speed across systems before anyone realizes something’s amiss. 

This keeps many organizations stuck in demo land. The demonstration is interesting. The value proposition may even be clear. But when the conversation turns to whether an agent should be allowed to perform consequential work in production, leaders rightly become less comfortable. 

I recommend beginning with a zero-trust mindset. I like to think of the trust layer as a traffic cop at the start of every interaction: Who are you? What are you trying to do? Why are you trying to do it? Is this appropriate? 

If the answer is no, the interaction stops. If the answer is yes, you move to the next level of questions: What guardrails apply? What needs to be observable? What needs to be auditable? Is there a point where a human needs to be in the loop? If something changes, can we explain the decision and, where necessary, reverse it? 

That requires guardrails around the information entering an agentic workflow as well as around the actions and outputs leaving it that can detect attempts to manipulate a model, misuse an API, or push a request outside policy. It means full observability so the organization can understand what happened and audit the decision path later. 

None of that slows the system down. It creates the confidence required to let the system move.  

LLMs help establish trust 

We are beginning to see three distinct LLM roles emerge.   

• Worker LLMs to carry out tasks 

• Orchestration LLMs to coordinate work 

• Judge LLMs to assess Does this make sense? Is this accurate? Is this appropriate? Should this action be allowed to continue?

It’s not unlike how a good human team operates, and humans are still an essential part of the team. They define the policies, design the guardrails, decide where accountability belongs, and step in when empathy, experience, or consequential judgment is required. 

Trust and Orchestration enables dynamic workflows  

For decades, we’ve built enterprise software around static workflows: A leads to B, which leads to C, which gets passed to D. When the real world does not cooperate, we build exception handling, additional approvals, escalations, and workarounds.  

Trusted agentic workflows offer a different possibility. They work with dynamic data, coordinate specialized agents and systems, and determine the most appropriate path toward a defined outcome. Trust and orchestration keep that flexibility from becoming a free-for-all. 

An agentic system can quietly handle routine follow-through, data reconciliation, low-risk tasks, and the thousands of small obligations that consume attention across an organization. It can elevate the moments that genuinely need a human: the decision requiring judgment, the difficult tradeoff, the customer conversation, the exception that cannot be resolved through policy alone. 

Build what lies beneath 

The temptation is to keep adding agents and better LLM models. There will always be another model, another demonstration, another point solution promising to deliver value quickly.  

My advice to leaders is not to ignore those opportunities. Take the car for a spin. See what it can do. But don’t mistake the test drive for the work of building a production-ready system. 

Pay attention to what lies beneath. 

Build a knowledge layer that converts data into shared, usable context. Design orchestration around real workflows and business outcomes. And establish a trust layer that can determine—dynamically and at the level of each action—what an agent should and should not be allowed to do. 

Then agents can become trusted collaborators in the real work of running a business. 

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