Steve Nouri

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The "AI for everything" era is ending.

The "AI for specific execution" era is just beginning.

For the last two years, the goal was just to get the model to answer.

Now, the goal is to get the model to act—safely, within bounds, and with the right context.

That’s a much harder problem to solve.

When I talk to CTOs about agentic workflows, the concern isn't the LLM.

How do we handle permissions?

How do we track the audit log?

How do we stop an agent from deleting a production database?

This is why I’ve been watching the shift toward governed execution layers.

It’s not enough to have a smart agent.

You need an agent that understands your company’s SOPs.

An agent that knows when to ask for a human-in-the-loop.

And an agent that operates inside your VPC, not in the wild.

I found a new framework that handles exactly this.

→ They get specific permissions.

→ They follow a set of "if-then" policies.

→ They report every action to a central dashboard.

Give the AI a prompt and hope for the best.

Give the AI a sandbox and a set of rules.

That is how you move from "cool demo" to "production value."

Check out the full breakdown here: https://lnkd.in/gXyZ892

Are you letting agents touch your production data yet? Or is it still strictly for research?

#enterpriseai #aiagents #automation

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