Control How Agents Behave in Production

AI agents make decisions on your infrastructure and inside your application. Impart watches what they do at runtime — identifies adaptive attacks, blocks deceptive behavior, and lets you patch zero-days in minutes instead of sprints.

What it Does

Agent protection at runtime that enforces, not just observes.

Agents authenticate like users, call APIs like services, and improvise like nothing your stack was designed to handle. The behavior is the attack surface.

Evaluate intent at runtime to control what your agents actually do, not just what they're allowed to.

Inspect

Every agent request in full context. Identity, role, prior actions, the tools the agent has access to, and the sequence of calls leading up to this one.

Decide

What the request is actually trying to accomplish. Privilege escalation, data exfiltration, unauthorized tool use, executing on injected instructions. Intent gets classified, not just the request shape.

Enforce

Malicious behavior stops before the action lands. The decision is made inline, in milliseconds. No second-guessing required.

How it Works

Impart detects adaptive attacks.

Agentic attacks rarely show up as a single bad request. They adapt, probe, and escalate.

Agent protection runs on the same ingress-layer enforcement engine that inspects your full attack surface. Impart reads the whole arc using a four-phase runtime defense:

• Discover
• Detect
• Protect
• Govern

Discover

Every agent making requests through the enforcement layer, whether sanctioned or not, to establish full visibility across your attack surface.

Detect

Malicious agent attacks by evaluating the full action sequence, instead of just single anomalous requests, to spot patterns like privilege escalation or data exfiltration.

Protect

Against abuse and exfiltration by classifying the agent's intent and blocking or modifying malicious behavior in milliseconds, before the action executes.

Govern

Enforce agent behavior using policy-as-code rules, which are written against live traffic and continuously refined to guide safer operation and stop unauthorized tool use.

One runtime engine for all your agents.

It sits between the agent and your systems, enforcing security before any action executes.

device:: known fingerprint
location:: Houston TX
rate:: normal
endpoint:: /users · always
timing:: business hours

device:: new fingerprint
location:: Paris FR
rate:: 47/min
endpoint:: /pays · first time
timing:: 02:14 AM
BLOCK if request.headers.match(agent_fingerprint_db) AND request.path ~ /export\|dump\|bulk → block

Stateful

Full behavioral history maintained across sessions, identities, and tool chains lets Impart make high-quality decisions

Live RULE if normalize(request.body).contains_sqli() → block

Unified

Whatever path the agent takes through your stack, the runtime knows who it is.

What a blocked attack looks like in Impart.

An adaptive attack unfolds in phases. Impart sees it as one sequence and stops it in real time.

Recon

The agent is identified and fingerprinted at first contact. Behavioral context begins building immediately across every surface it touches.

Probing

Suspicious patterns emerge. Tool calls outside the agent's normal scope, unusual access velocity, requests that don't match the role. Risk is tracked across the entire system in real-time.

Adaptation

The attacker shifts tactics. New prompts, new request shapes, deceptive framing. Intent classification reads through the surface change. The behavior is what's being evaluated, not the disguise.

Rapid Patch

A new exploit pattern is observed. Your team writes a rule against live traffic and pushes it in minutes. The window closes before the next request lands.

Enforcement

A rule is generated from the observed behavior, blocking the actor going forward. What started as a sequence becomes a denial.

Running in production. Enforcing in real time.

FAQ

Does this cover agents using MCP and tool calls?

Yes. MCP traffic, function calls, and tool invocations are exactly where Impart enforces. Every call gets evaluated for whether it fits the agent's role and the trajectory of the session. Tool poisoning, unauthorized tool use, and chained tool calls that only become attacks in aggregate are all in scope.

What about shadow agents we don't know about?

Impart sees every agent making requests through the enforcement layer, sanctioned or not. If an agent is calling your APIs, hitting your data, or invoking tools through your infrastructure, it shows up in the runtime. Visibility is the first thing the system gives you before any policy is written.

How does AI Analysis avoid blocking legitimate agent behavior?

Intent gets classified against the agent's role, history, and the full action sequence. A request that looks anomalous in isolation often makes sense in context, and AI Analysis reads the context. Most teams run in observe mode first, watch the decisions Impart would make against real traffic, then turn on enforcement when they're ready. False positives are the failure mode the system is tuned against.

How is this different from agent governance and posture management tools?

Posture tools tell you what your agents are configured to do. Governance tools tell you what they're allowed to do. Impart enforces what they actually do, in the moment they try to do it. Posture and governance run in advance. Impart runs at execution. Most production teams need both layers.

## Let the attack start.
 It won’t finish.