How do AI security solutions differ for LLM apps versus agentic AI systems?

LLM application security has traditionally focused on the input and output boundary of a single model call, addressing risks like prompt injection, jailbreak attempts, and data leakage that occurs through what the model generates in response to a given prompt. This scope made sense when most LLM deployments were simple chat interfaces where the model's output was the end of the interaction.

Agentic AI systems change the risk profile substantially because the model's output is often just the first step in a longer chain of actions. An agent might use that output to decide which tool to call, what data to retrieve, or what action to take next, and a single successful manipulation early in that chain can cascade into consequences well beyond what the original model output looked like on its own. This might include sending data to an external system, triggering a transaction, or taking an action that affects other connected agents.

Because of this, security for agentic systems requires runtime behavioral monitoring that tracks the full chain of decisions and tool calls an agent makes, not just input filtering applied at the boundary of a single model interaction. A filter that catches a harmful prompt but has no visibility into what the agent subsequently does with its tools misses the majority of the actual risk surface in an agentic deployment.

Akto's Argus is purpose built for this agentic case, continuously testing and monitoring the full chain of tool calls an agent makes rather than evaluating its text output in isolation, which reflects where the real risk in agentic systems tends to concentrate.