Agentic AI Risk Is Real.
Mere Cleanup: Not a Good Plan.

A recent Economist ebook shows how fast agentic AI risk has grown. Read it closely and it makes a strong case for building AI a smarter way.

Agentic AI risk is no longer a future worry. It is here now. A recent Economist ebook puts hard numbers on it. Companies are rolling out AI agents faster than they can control them. The report makes a strong case for a smarter way to build AI. Probabilistic AI simply isn’t enough to control the risks. We need a better alternative.

What the report found about agentic AI risk

The findings are blunt. Agent failures are already causing damage across whole organizations.

Here is the sharpest point: The threat has moved inside. The danger is no longer just outside attackers. It is the agent itself, acting outside the limits that it was originally meant to stay within. All told, the report emphasizes the real need for deterministic AI technology that can help manage the probabilistic AI agents. Hence the need for Responsible AI technologies, as good solutions.

Where the report stops short

The ebook makes one assumption worth a second look. It treats AI agents as unpredictable by nature. This is true due to the probabilistic nature of LLM (Large Language Model) technologies. It assumes incidents are unavoidable. So it says the smart move is to watch closely and roll back fast after something breaks. In other words, governance after-the-fact. 

This is cleanup thinking. It has value. But it only asks how fast you can undo the damage. It never asks how to avoid the damage altogether. And it skips one big option. You do not have to accept an AI agent that is out of control, in the first place!

Two very different things share one name

Most of the agentic AI risk in the report comes from one design. A guessing model is given wide access and set loose to act on its own. Not and ideal or stable business process, by any means. In fact, at kama.ai we don’t at all believe this to be a viable, intelligent, or wise approach to building an AI agent that can impact real-world consequence.

A governed, Responsible AI agent is built to be accountable from day one. It answers from approved knowledge. It follows rules set in advance. It sends anything uncertain to a human agent for review or troubleshooting. Same word. Very different risk profile. The report warns you about the first kind. It is not an argument against the second.

Govern the Probabilistic side. Do not surrender to it.

This is what kama.ai was built to do. We do not remove the power of generative AI. We put a deterministic brain in charge of it.

A Knowledge Graph AI acts as the control layer. Generative AI works as a resource that is strictly guard-railed. It is not the decision maker. The deterministic AI side governs the probabilistic AI side. Answers come only from Trusted Collections, which means content which a trusted knowledge manager approves and provides from branded resources. The system does not invent its own facts. Ask the same question twice and you get the EXACT same governed answer. That makes the behavior predictable, auditable, and easy to defend.

This is what governed by design means. The rules are set in advance, not bolted on after a crisis. You are not chasing an agent to learn what it did. You decided what it could do before it did anything.

You do not need to recover faster from a mistake when you build on an architecture where that mistake does not happen.

The two gaps in the report, closed by design

The report names two things most companies lack. They cannot see what their agents are doing. They cannot reverse a harmful action. A Responsible AI platform that is both governed-in-advance, and deterministically orchestrated answers both of these challenges.

Every answer traces back to an approved source. So, a clear audit trail is a natural result of how the system runs. It is not a separate tool you have to buy and stitch together. Human in the Loop reviews puts a real checkpoint into the flow. Oversight is built in, not hoped for.

An honest limit

Not every AI use case needs to be locked down. Open, exploratory work has its place, and there, recovery planning matters. The point here is narrower and stronger. When correctness is not optional. When the risks are high. Then agentic AI risk becomes a REAL threat. A wrong answer to a customer can carry real costs. In those cases, do not lean on how fast you can undo a mistake. Build on an architecture that prevents it in the first place.

The takeaway

The Economist ebook makes agentic AI risk impossible to ignore. Most vendors answer it by helping you recover faster after the agent goes wrong. At kama.ai – we think that is the wrong end of the problem. Use a deterministic AI to govern and orchestrate the probabilistic side (see GenAI’s Sober Second Mind). Set the rules in advance. Keep a person in the loop. And most importantly – bypass most of these incidents entirely. Speak with us at kama.ai to figure out how. 

If it’s got to be right, it’s got to be kama.ai.

kama.ai is a Responsible AI platform that combines Knowledge Graph AI, governed generative AI, and Intelligent Automation. Purpose-built and governed by design.