The reliability of an AI agent does not come from the LLM, but from the “harness” that frames it. A generic model, without real anchoring, remains subject to hallucinations — unacceptable in network security.
All AI vendors talk about agents: autonomous systems that handle complex tasks without constant human intervention. The promise is very real. However, an essential question is too rarely asked: what makes an agent reliable enough to guarantee the security of your network?
The answer lies not in the model, but in the operating system.
The agents are based on LLMs. Reliability relies on something else.
AI agents derive their reasoning ability from large language models (LLM). These models are impressive: they understand context, reason through complex problems, and plan actions for a wide variety of tasks.
However, taken in isolation, an LLM remains a general system. He has superficial knowledge on a multitude of topics, but ignores the specifics of your environment. Without anchoring in real data, he is prone to hallucinations: he produces erroneous answers with deceptive confidence. While this phenomenon is just an inconvenience for a chatbot, it represents an unacceptable risk in the field of network security.
What distinguishes a successful AI agent from a reliable agent is the way in which it is supervised and structured. Three elements define this framework: skills, tools and context.
If these three elements are correctly mastered, the agent gains in precision and reliability. Conversely, an error in one of them leads back to the problem of hallucinations, simply through a more complex path.
Why generative AI gets the harness wrong
A general-purpose agent applied to network security takes an architecture designed for general purposes and uses it where precision is crucial.
Generic models deal with generic cases. They have never been confronted with the edge cases specific to mature environments: ten-year-old rules that no one knows anymore, or dependencies between multiple suppliers causing failures with unsuspected causes. They rely on static training data, with no concept of what constitutes optimal operation at scale.
The result is automation that works well in demonstrations but fails when deployed at scale.
The essential criteria for a reliable harness
Reliability does not depend on the size of the model. It arises from the way in which the three components of the coaching system (or “harness”) are designed: skills, tools and context.
Develop skills based on real-world experience. These skills should reflect expertise acquired in a production environment, not theoretical knowledge derived from generic training data. The most valuable are those that integrate the management of complex cases — those particularities and borderline situations that only arise in mature environments. It is this depth that allows the agent to understand what defines “good” execution.
Design tools to serve the agent. Not everything can be covered by the LLM; the tools are essential to recover data, synthesize them to make them usable and prepare them for the model. These tools can range from simple API calls to sophisticated solutions incorporating complex machine learning algorithms. In any case, this is also where your expertise must be integrated to give the agent capabilities specific to your field.
Give the agent a real-time view of the environment. The precision of an agent depends directly on the fidelity of the image on which it relies. Don’t rely on static snapshots or stale exports: they become irrelevant the moment they are generated. Tools must read the environment as it exists at the moment T — topology, dependencies, policies, traffic — and act accordingly.
If you master these three elements, it is this framework that will guarantee reliability, not the underlying model.
