AgentKey · Security
AI agent security is the set of controls, policies, and observability practices that keep autonomous AI agents from taking harmful, unauthorized, or unintended actions when they call tools and APIs on a user's behalf.
By AgentKey · Last updated 2026-09-01
An LLM agent is powerful because it can choose and call tools. That same property is the risk. Unlike a traditional application with fixed code paths, an agent decides at runtime which action to take and with which arguments. A misread instruction, a hostile email, or a tool that changed behavior can turn an assistant into something that moves money or deletes data.
Securing an agent is not about locking down the model. It is about controlling the actions the agent can take against real systems, observing what it actually did, and being able to prove it.
Application security hardens code. Agent security hardens decisions made by code at runtime. The threat is not only a vulnerability in a library; it is a legitimate tool used for an illegitimate purpose because the agent was told to. Controls must therefore be on the action, evaluated in real time, not only on the codebase.
AgentKey implements these layers together: a policy engine for authorization, parameter conditions for least privilege, a tool trust registry, input scanning, human approval, and a hash-chained evidence trail. See the docs to enable it with one line of code.
The runtime controls, policies, and observability that keep autonomous AI agents from taking harmful or unauthorized actions when they call tools and APIs. It covers authorization, least privilege, tool trust, input integrity, human approval, and audit.
Because an agent decides at runtime which action to take and with which arguments. The risk is a legitimate tool used for an unintended purpose, so controls must sit on the action and be evaluated in real time, not only on the codebase.