Behavioral security
for the agentic era.
Agents report what they did. Quint records what actually executed, out of band at the OS level, where the agent cannot reach it. The gap between the two is the alert. Zero code changes. We audit the agent, not the developer.
See everything your agents do.
Fleet-wide visibility into risk scores, behavioral baselines, and anomaly detection. One pane of glass.
The agentic workforce is already here. Security isn't.
Governance built for humans doesn't work for agents. That gap is the market. It closes in 2026.
Every function is becoming an agent operator. Coding agents, support agents, clinical agents, trading bots, procurement bots, sub-agents spawning sub-agents. Every one of them holds credentials. Every credential is a blast radius.
Gateways only see what agents route through them. Most tool calls bypass them entirely. Prompt scanners read conversation, not execution. EDR watches the OS but has no concept of intent. None of it was built for agents.
Continuous agent oversight becomes mandatory. Fines reach 7% of global revenue. The compliance clock started when you weren't looking.
Two dimensions of divergence. One product philosophy.
Every agent claims something. Every agent does something. Every agent has a pattern. Quint watches all three and flags the gap, before it becomes an incident.
Signed .pkg via Jamf, Intune, or Kandji. Zero code changes.
Two layers capture intent + truth. Baselines learned automatically.
Score both divergences per action. Risk propagates instantly.
Block, flag, or allow. Every decision Ed25519-signed.
Noma, Pillar, and Lasso watch agents at the API layer. Quint records what agents actually do on the machine: every file read, every process spawn, every connection they never declared.
Hyperscaler guardrails protect their own models. When an agent calls three providers and touches local files, they see one slice. Quint sees the complete picture.
Observability records what agent frameworks report. It doesn't block anything, doesn't see OS actions, and at 3am when an agent goes rogue, a dashboard doesn't stop the exfiltration.
Governance platforms produce the compliance binder. Quint produces the Ed25519-signed evidence that goes inside it. Proof of what every agent actually did, not what policy said it should.
Declared intent. Observed action. A signed verdict.
- 01Zero LLMs in the critical path
- 02Sub-10ms enforcement at the edge
- 03Every action, every agent
- 04Ed25519-signed into a tamper-evident chain
- rule
- unapproved recipient
- latency
- 4.2ms
Intent. Truth. Baseline.
Other tools pick one signal. Observability platforms watch traces. Gateways watch traffic. Governance tools write docs. Quint captures what the agent claimed, what it actually did, and what is normal at this scope then scores the gap between all three.
“This support agent is behaving unusually for Alice's queue at 2am, and what it just did doesn't match what it said it would do.”
A graph neural network encodes every agent, machine, user, and tool as a node, every call, spawn, and access as an edge. The six baselines are learned embeddings on the same graph, so when one node drifts, Quint already knows which neighbors to watch.
- 01Per agent. what this specific instance normally does: coding, support, or clinical.
- 02Per machine. the host's pattern, whether laptop, VM, or container.
- 03Per user. how one person uses agents across every machine.
- 04Per team. how engineering, data science, and SRE differ from each other.
- 05Per enterprise. your organization's complete agent footprint.
- 06Cross-fleet. opt-in aggregate across customers — not built yet.
Under ten milliseconds. Zero LLMs in the path.
Everything you need to secure AI agents at runtime: interception, scoring, enforcement, audit. Deterministic end-to-end. Same input, same verdict, every time.
Competitors like Lasso use “LLM as a judge” in the enforcement path. Trail of Bits research has shown that puts an attackable, non-deterministic surface inside your security layer. Quint chooses math over models.
Know every agent on your network
Six-layer detection stack identifies 20+ AI agent platforms through independent signals: code signing, process inspection, HTTP headers, system prompt fingerprinting, user-agent patterns, and protocol analysis. Discovers shadow AI and sub-agents. Retroactively reclassifies unknown requests as richer signals arrive.
Score in real time. Enforce at the edge.
Multi-layer composite scoring computed at the edge with no LLM in the critical path. Verdicts evaluate on-device, ahead of the action — running observe-first, so enforcement is measured against your real traffic before anything is blocked.
Declarative rules, fleet-wide control
Rules match on agent and tool-name patterns, argument content, and parameter constraints. Policies push to every endpoint on heartbeat and evaluate on-device, priority-ordered. Observe mode records exactly what a rule would have caught — on your real traffic — before it enforces anything.
Every machine. Your existing stack.
Centralized control plane with machine inventory, health monitoring, and agent census. Deploy fleet-wide via signed .pkg through Jamf, Intune, or Kandji. Structured event export for SIEM integration. Full REST API.
Three frameworks you need. Thirteen more you'll ask about later.
Every agent action scored against sixteen frameworks in real time. But nobody buys based on a grid of logos. Here are the three buyers ask about first.
Trust services criteria mapped to every tool call. Continuous control monitoring, automated evidence collection, exportable audit bundles.
Personal-data access classification on every agent action. Right-to-erasure enforcement. Cross-border transfer detection.
Continuous oversight of high-risk AI systems. Prohibited-practice detection. Fines up to 7% of global revenue, and the clock is running.
Built for CISOs. Built for engineers.
Quint records what an agent actually did at the kernel, next to what it said it would do, and shows you where those two diverge. Same install on every host, same evidence trail.
Every other tool asks the agent what it did. We watch the kernel and compare.
What Quint does
Research & threat analysis
What Is AI Agent Runtime Security? The Complete Definition
AI agent runtime security is the discipline of monitoring, scoring, and controlling what AI agents actually do at execution time, at the operating system, network, and tool-call layers, independent of what they were configured or prompted to do. Here's the full definition, the layers it covers, and why it's the security category that didn't exist two years ago.
MARKET · 12 minBehavioral Security for AI Agents: What It Is, Why It's Different, and Why Static Controls Fail
Behavioral security for AI agents is the practice of building a baseline of normal agent behavior, scoring every action against that baseline, and flagging divergence the moment it happens. Here's how it differs from every other AI security category, and why it's the only approach that catches what the agent actually does vs. what it claims to do.
THREAT REPORT · 8 minThe MCP Security Checklist: 12 Things to Do Before You Trust Any MCP Server
MCP servers are running with your shell access, your API keys, and your data. Here are the 12 specific checks to run before connecting one to any AI agent in your org. Includes a print-friendly version.
Frequently Asked Questions
Your agents are running.
See what they're actually doing.
Deploy fleet-wide via MDM. Start with visibility, enforce when ready. No agent configuration required.