The endpoint is where agents act. Origin observes what they do, traces why they do it, and maps how their behavior propagates across your organization - in real time.
Endpoint detection was designed around a simple assumption: a human sits at a keyboard, and malicious activity looks different from normal activity. AI agents break both of those assumptions simultaneously.
EDR records that a process spawned a child process. It does not record that an AI agent chose to refactor authentication middleware, read .env files, and triggered a network call to an unfamiliar endpoint. The causal chain, the why, is missing.
When an AI agent reads files, writes code, spawns processes, and opens connections as part of normal work, EDR heuristics turn into noise. The signal-to-noise ratio collapses. Legitimate agent behavior starts to look like lateral movement.
Injected instructions, context-window poisoning, and tool-call hijacking happen inside the agent’s reasoning layer. They leave no binary signature, suspicious file hash, or obvious network anomaly. Most endpoint tools do not even recognize this as a surface to monitor.
Origin captures the full semantic trace of every AI agent operating on every endpoint: the prompt that started it, the reasoning chain that drove it, every file read, process spawned, and connection opened along the way.
Then it automatically clusters that behavior, so normal patterns emerge as recognizable topology - and anything anomalous stands out by contrast, not by signature.
User prompt intercepted and extracted from API call
Agent decomposes task into 4 sub-operations - file reads, dependency install, code modification, test execution
Agent reads .env and config/secrets.yaml - access attributed to auth refactor task
Outbound connection to unfamiliar endpoint - not part of declared task scope
Session classified as atypical - credential access + undeclared network call deviates from “Auth Refactoring” cluster baseline
Without semantic observability at the endpoint, none of this is possible. With it, security teams gain an entirely new operational surface - one that matches the speed and complexity of the agent workforce itself.
Follow the semantic thread backward from system effect to tool call to reasoning step to the exact prompt that initiated the chain.
Every prompt, every reasoning step, every file access, and every connection opened is captured, attributed, and searchable.
Discover every AI agent running across the endpoint fleet, including ones nobody explicitly deployed or approved.
Topic clustering establishes what typical agent behavior looks like for each team, workflow, and role.
Move beyond binary allowlists to policies that understand intent, scope, and side effects rather than just process names.
Prompt injection, context poisoning, and tool-call hijacking become visible before they resolve into system effects.
