Detection Model

The analytic core of the AgenticSafe framework. It moves beyond watching individual actions to continuously characterize each agent's intent, behavior, and role, catching drift before it becomes damage.

Overview

The Detection Model continuously characterizes and evaluates the intent, behavior, and roles of discovered agents to identify drift from their assigned tasks. By building a complete picture of behavioral context, it lets you map clear agent ownership across business units, individual users, and independent owners. It proactively detects misuse, manipulation, and emerging risk before business impact, and enforces enterprise policy and regulatory compliance through adaptive, identity-driven action and real-time audit trails. A multi-agent architecture moves past signature-based security toward deep behavioral identity and anomaly detection.

Capabilities

Behavioral identity profiling

Builds a unique fingerprint of each agent from how it actually behaves, interacts, and accesses data over time, rather than relying on static labels or code.

Deception and manipulation detection

Continuously analyzes agent outputs to catch hidden risk, data manipulation, and cases where an agent misleads users or systems about what it is really doing.

Multi-agent workflow monitoring

Tracks complex AI chains, such as text-to-SQL or automated analytics, where sub-agents hand off tasks, so the original intent is not altered or hidden along the way.