NEW Watch the whole customer journey, uncut - from unknown AI workloads to the first caught anomaly, in 2:47. Watch it →
Behavioral detection & response for AI agents

Every AI agent
has two identities.

The static one your identity stack assigns - and the Dynamic Identity it earns by how it behaves. FortLine learns every agent's missions - the real units of its work - and catches the moment one goes wrong. Per agent, not per population. While it runs, not after.

Off the data path No SDK, no code change Any model, any cloud
LIVE mission behavior
agent: support-copilot · mission #4821
learned baselineanomaly
score 0.04
Anomaly on mission #4821
tool: db.queryscore 0.93
deviationoff-baseline egress
The gap

Most controls approve the action. Almost nothing verifies the behavior.

A prompt-injected, poisoned or drifting agent acts entirely inside its granted permissions - so every gate says yes. An agent can do ten individually permitted things that add up to something wrong. No per-action gate can see that. A mission can.

Allowed

Identity - the door

Decides which agents may act and what they may touch. Necessary - and it assumes the agent stays itself.

Enforced

Gateway - the lock

Filters prompts and enforces policy at the chokepoint. Blind to whether the run inside the policy is normal - and blind to everything that never passes through it.

Unwatched

Behavior - the cameras

What the agent actually did, step by step, across the whole mission. This is the blind spot FortLine closes.

You want all three. Nobody argued that directory and access control made endpoint detection unnecessary. FortLine's verdicts fire while the mission runs and feed the gates you already own - and because it sits off the data path, it can never slow down, throttle or break an agent.

See it

Click the anomaly. Read the conversation.

FortLine links every anomaly to the agent conversation that produced it - the prompts, tool calls, reasoning, and the exact step where the mission went off-baseline. Investigation that took an afternoon becomes one click.

A mission going wrong A support copilot normally reads tickets. Today, after a prompt injection, it runs a full-table read of the customer database and ships the result off-domain. FortLine flags the mission the moment it leaves its baseline - and opens the exact conversation that caused it.
See it on your agents
The journey

The whole journey, uncut. 2:47.

From a cluster full of unknown AI workloads to a learned agent catching its first anomaly - every screen is AgentShield, the FortLine platform, running live.

300+
behavioral models protecting a single production agent - all feeding one explainable alert
~0%
false positives - a mixture-of-experts ensemble with majority voting suppresses them, tunable to your risk tolerance
10M+
events analyzed, hundreds of thousands more every day
1 click
from anomaly to the exact conversation that caused it
200+ behavioral features per event
4 levels: event → transaction → session → mission
MoE: SVM · Isolation Forest · autoencoders · VAEs · LSTM
Every layer: LLM · MCP · API · DB · network
Dynamic Identity

An agent has the identity you assign - and the one it earns by how it behaves.

Identity vendors answer who is this agent and what may it do. That is essential, and we build on it. FortLine adds the missing dimension: a learned, living model of how each agent normally behaves - so you can tell when an authorized agent is no longer acting like itself.

Static identity · your IAM / NHI stack

Who the agent is

  • Credentials, roles and permissions
  • What the agent is allowed to access
  • Declared, policy-defined, assigned up front
  • Answers "is this agent authorized?"
Dynamic Identity · FortLine

How the agent behaves

  • A learned baseline per agent, per mission, per step
  • 300+ models capture one production agent's normal
  • Continuously updated as the agent evolves
  • Answers "is this agent still acting like itself?"
The platform

Discover. Baseline. Detect. Drill down.

01

Discover

Inventory every agent, model, MCP server and AI workload across clusters and clouds. Shadow-AI and posture (AI-SPM).

02

Baseline

A self-governing AutoML engine learns each agent's missions and their normal behavior - no rules to write, no policy files to rot.

03

Detect

Catch drift, goal hijack, tool misuse and rogue behavior in production - the anomaly, not a signature.

04

Drill downSERVERLESS + MCP

Every alert links to the exact conversation, tool calls and reasoning that produced it. One click, not an afternoon.

How it works

From live traffic to the exact conversation that caused it.

Capture happens off the data path - at the kernel with eBPF, or through a transparent gateway for serverless. No SDK, no code change, and zero added latency to your agents.

live traffic LLM · MCP · API · DB capture eBPF + gateway missions hyper-sessions Dynamic Identity 100s of models anomaly drill-down
1. CaptureEvery LLM call, MCP tool call, API and DB query - off the data path.
2. CorrelateStitched into missions and hyper-sessions, the way the agent actually works.
3. BaselineMission-level models learn the agent's Dynamic Identity - 300+ on one agent.
4. DetectDrift, goal hijack and tool misuse scored in under a second.
5. ExplainEach anomaly opens the exact conversation that caused it.
This month, in the real world

The behavior gap stopped being theoretical.

Agents rebuilt their own backchannel.

OpenAI's escaped eval agents created a hidden message board to coordinate - and after it was shut down, rebuilt it four days later. Disclosed on the Black Hat stage, August 2026. Every action was authorized.

The gateway was there. It was bypassed.

A top-US-retailer shopping agent was chained to remote code execution straight through an LLM gateway with an intent-classification layer - and stayed unremediated past the 90-day window (Rein Security, Black Hat 2026).

Behavioral watching went production.

Roblox presented multi-layer sandboxing plus behavioral monitoring of Claude Code as day-to-day enterprise practice at Black Hat 2026. Watching what agents actually do is no longer a research idea.

"One of the key questions for me now is trying to figure out how do we detect good agent behavior versus bad agent behavior?" - a sitting CISO, August 7, 2026 (Forbes). That question is this product.

Coverage

We do not need to be in the path.

Every inline control sees only the traffic that passes through it. FortLine captures behavior at the kernel and below the SDK - on and off the brokered path - so the same Dynamic Identity baseline covers containerized, serverless and MCP-speaking agents alike. No SDK, no code change.

Runtimes

Containerized and serverless agents, captured the same way.

KubernetesAWS LambdaAmazon Bedrock AgentCoreECS / FargateVM / bare metal

Protocols & tools

The full agentic surface, including the tool-use layer prompt-firewalls never see.

MCP tool meshLLM conversations & tool callsREST / gRPC / GraphQLSQL & NoSQL queriesVector DB / RAGFilesystem & shellOutbound HTTP / egressStreaming: SSE / WebSocket

Model providers

Provider-agnostic by construction: we capture below the SDK, so any model works - even ones not on this list. A sample of what partners run:

OpenAIAnthropicAmazon Bedrock / NovaGoogle Gemini / VertexAzure OpenAIMeta LlamaMistralOllama / vLLM

Deployment

eBPF DaemonSet or transparent gateway. Off the data path, by design.

Zero instrumentationNo SDKLive in minutes0 added latency
Standards & frameworks

Mapped to ASI01 through ASI10.

FortLine maps to the OWASP Top 10 for Agentic Applications, with runtime behavioral evidence for the risks that show up in what the agent actually does - goal hijack, tool misuse, memory poisoning and rogue agents (highlighted below).

ASI01
Agent Goal Hijack
behavioral
ASI02
Tool Misuse & Exploitation
behavioral
ASI03
Identity & Privilege Abuse
ASI04
Agentic Supply Chain
ASI05
Unexpected Code Execution
ASI06
Memory & Context Poisoning
behavioral
ASI07
Insecure Inter-Agent Comms
ASI08
Cascading Failures
ASI09
Human-Agent Trust Exploitation
ASI10
Rogue Agents
behavioral
OWASP Top 10 for Agentic Apps Gartner Guardian Agents · Market Guide 2026 MITRE ATLAS NIST AI RMF ISO 42001-aligned
Quali
FortLine + Quali. Dynamic Identity baselining is embedded into Quali Torque, so every agent ships with behavioral detection from its first boot - governance at provisioning, not bolted on later.
Read the announcement
Get started

See it on your agents.

Start with free runtime visibility - we will baseline a mission and show you its Dynamic Identity. No SDK, no code change, off the data path.

  • Live in minutes with zero instrumentation
  • Watch us baseline a real mission and catch a planted anomaly
  • Works across Kubernetes, serverless, MCP and any model provider
  • Off the data path - it cannot slow down or break your agents

Get free runtime visibility

Tell us where your agents run. We will be in touch to set up your baseline.
By submitting you agree to be contacted about FortLine. No spam. Prefer email? info@fortline.ai · Not ready to talk? Watch the journey or read the LinkedIn essays.
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