It seems like organizations are still in the AI experimentation phase, where evaluating AI products is still the focus.
Most organizations are familiar with products such as ChatGPT, Claude and Gemini and haven't explored AI beyond the chatbot.
Those products are very good at generating text, but when it comes to building agents that take actions, the problem becomes very different.
A chatbot is primarily an interface to an LLM. An agent is a software system that uses an LLM as part of a larger process. It has access to tools, APIs, data, applications and, most importantly, the ability to take actions.
That changes the game.
In my opinion, the two most important things for AI agents to be successful in the enterprise are security and predictability.
We want agents to be secure, but we also want to make sure that they don't go rogue, doing things they are not authorized to do.
The problem is that LLMs are probabilistic by design. Give the same prompt twice and you can't guarantee you'll get exactly the same response.
That isn't necessarily a problem for a chatbot.
It becomes a very different problem when the AI has permission to send an email, modify a database, approve a transaction, create a user, change a configuration or access sensitive information.
The enterprise doesn't necessarily need the AI to behave like a script.
It needs the controls around the AI to be deterministic.
The model can reason probabilistically, but the boundaries around what it is allowed to do need to be predictable, enforceable and auditable.
This is where I think the conversation around AI agents needs to move.
The question shouldn't simply be:
"How capable is the AI?"
It should be:
"How safely can we deploy AI agents in our workflows without worrying about them going rogue?"
And more importantly:
"What happens when they make the wrong decision?"
That's the difference between an AI demo and an enterprise AI system.
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