With Agentic AI, these will have a comeback in slightly different form: as Agentic Control.
The simple RAG Reporting Model
Let's take a look first how I would define a RAG report by giving you a minimal example:
| Status | Condition | Action |
|---|---|---|
| Red | In a condition that the reporting party can not resolve. | Informed party needs to take Action X. |
| Amber | Unusual activity required to remain in control. | Reporting party plans to take Action X, informed party should contradict if that's not desired. |
| Green | Business as Usual. | Reporting party will continue as planned. |
What this means for Agents
You can already guess that a Red status means that the Agent can or should no longer continue what they are doing, and a Green status means that no human action is required - the Agent is doing its job just as intended.
But now comes the tricky part:
What is "Red?"
The way LLM components work, is that unless you violate one of their internal guardrails (such as trying to get it to produce nuclear weapons) - it will always try to find a solution.
So you need to define its stopping conditions. You have to tell it exactly where it is not allowed to proceed, and how to recognize that point.
If no "Red" is defined, the Agent will always proceed - even to the point where it might agree to sell your company for a measly $1, if that's within what it could technically do.
If "Red" is defined only in technical terms (e.g. "do not proceed without database access") - then there's still no guarantee that it won't sell your company for a dollar.
Many companies are myopic in defining "Red" and think "We tested the positive path, it does what we expect - so we're good to go."
Until they get a note from a lawyer who asks when the company will be handed over.
Who defines "Red?"
As you might have seen from the context above, what are hard stop conditions for the Agent depends on whom you ask.
There are business rules, legal rules, social norms - so many things the Agent could violate, and depending on whom you ask, you get a different answer.
But then, ultimately, there is someone who must own the consequences of what the Agent does. Ultimately, that's the CEO. But because you can't have everything any Agent inside your company does monitored by the CEO (that's not their job) - you must assign ownership of consequence to the Agent. And that person must say, "This is what the agent is allowed to do, that not."
The liability: Amber
The biggest problem with Amber: it creates organizational drag, human cognitive load, and stress. It is convenient for developers, and undesirable for operators.
Amber status is typical Human-in-the-Loop (HITL) terrain. That is where the agent may already know the next step, but it may be better to get a human to approve - lest a wrong move creates irreversible consequences.
For example, if you have an agent to manage inbound customer service requests - most of these can be handled by standard procedure, but as soon as anything is unusal, you may want to have a human check the agent's answer before letting it go out.
Amber is really difficult, because a lot of Amber terrain is discovered by trial and error, as the Agent simply proceeds, and someone discovers in a Review, "It shouldn't have done that."
The big problem with Amber is: an overuse dulls it down. If the Agent reports Amber on every single move, people stop caring for the details and simply ask the Agent to "proceed."
Another problem with Amber is attention economy: every Amber status is an interrupt in whatever the responsible human is doing. The more interrupts, the more counterproductive the agent becomes.
So you need agreed guidelines on what is really Amber, and where Green is still OK.
The threat: Green
We treat Green as "just information, a journal of events" - but it masks the big problem of False Negatives, that is: the Agent says, "No problems encountered" when every human would jumps off the chair and shout "Don't. STOP!"
The biggest threat to Agentic AI is the assumption that "as long as the Agent doesn't report errors, we are okay." This is simply untrue.
An Agent reporting Green while being in Red state is a nightmare - both for AI security, and for business owners.
The stories on the Internet of Agents announcing a state safe to proceed while informing users about the impending doom as if it was redecorating shelves, are numerous.
You need to monitor what is hiding behind "Green" - and find out which of these would actually have been Yellow or Red.
The Agent Traffic Light Report
Given everything we wrote, only the first part of an agents is to produce a detailed, auditable RAG report, in a format somewhat to the one proposed above.
An Agent requires governance enforcement. A RAG Report creates the basis.
The Agent's actions needs to be owned, screened and evaluated - what do the records say? What does that means in consequence? And also: how can we improve the agent to reduce both False Positives and False Negatives?
Once we have a good classification with few False Positives and False Negatives, we can optimize the Agent to do as much within Status: Green as possible.
And never forget: it's better to deal with a Red, than to deal with the consequences of a False Green.
