Agentic AI

AI agents don't just answer questions. They take actions: calling tools, querying data, and connecting to services through protocols such as MCP (Model Context Protocol). This page explains how WitnessAI helps you discover the agents in your environment, attribute their activity to a real identity, and control what they're permitted to do.

The problem

Autonomous and semi-autonomous agents introduce risks that prompt-only controls don't cover:

Invisible actors. Agents and the MCP servers they connect to can proliferate without anyone tracking them.
No identity attribution. When an agent acts, it's hard to tie that action back to the human or service it acts on behalf of.
Unbounded tool access. An agent with broad tool or MCP access can reach data and systems far beyond what its task requires.

The agentic features on this page are turned on per tenant. The Agents and MCP Servers tabs in Discovery are enabled separately, so a tenant may see one without the other.

How WitnessAI solves the problem

WitnessAI extends the same observe-control-protect model to agentic AI. It discovers the agents and MCP servers operating in your environment, brings their activity into the same classified, auditable record as the rest of your AI traffic, and lets you govern which tools and MCP servers an agent can use through policy, so agent actions stay scoped to what you intend.

What capabilities WitnessAI provides

Discover agents and MCP servers. Agents and MCP Servers, both tabs in Discovery, show the agents operating in your environment, the MCP servers they reach, the tools they use and the data they transfer, so you can assess their risk.
Govern tool and MCP access. Policies can control agent tool use and MCP access, restricting an agent to an approved set of capabilities. See Policy types for the policy controls available and Policy creation for how to build them.
Apply behavioral guardrails. The Behavioral Activity guardrail identifies what a requester is trying to do and can Allow, Warn, Block or Route the request to another model. It can also generate its rules from a stated purpose, so you don't have to write each one by hand.
Audit agent activity. Agent prompts and responses appear in Conversations alongside the rest of your AI traffic. Each conversation also has an Actions tab that lists the tool calls made during that session.

How to get started

Find your agents. Open Agents under Discovery to see the agents and MCP servers in your environment.
Decide what's allowed. Review Policy types to understand how to scope agent tool and MCP access.
Build the guardrail. Use the Behavioral Activity guardrail to define permitted behaviors, then attach it to a policy via Policy creation.
Monitor. Track agent activity in Conversations and refine as your agent footprint grows.

Agentic AI security The problem How WitnessAI solves the problem What capabilities WitnessAI provides How to get started