WHERE AGENTIC AI BREAKS HERE
Tool-chaining drift
The agent assembles a chain of actions where no individual step would fail review, but the chain as a whole breaches policy.
Reflection-loop prompt injection
Nudged agentic AI enters a reflection loop and drives itself toward continuous unauthorised actions across the enterprise.
Multi-agent collusion patterns
In agent-to-agent workflows, behaviours emerge that no single-agent test would catch. Risk compounds as the agent estate grows.
Agentic AI plans and
executes the multi-step
workflow
Autonomous planning, tool calls, and chained actions across enterprise systems all run inside the autonomous loop.
Disseqt enforces tool-call gates, identity checks, and scope-limited tokens
84+ jailbreak techniques & 67+ input validators applied across multi-step runs, including tool-chaining and reflection-loop probes.
Out-of-envelope actions blocked inline with plan-action-outcome trace
Risk, security, and compliance read what the agent planned, what it did, and the step that needs human override or escalation.
EU AI Act, NIST AI RMF, and sector-specific audit trail across runs
Plan-action-outcome evidence assembled across full agent runs, mapped to the regulatory framework the workflow touches.
Per-tool-call policy enforcement
Every tool call gated against identity, scope, and policy before the agent acts on the enterprise's behalf.
Measurable agentic-risk posture
Tool-chaining drift, reflection-loop injection, and multi-agent collusion caught release-over-release, with the evidence to defend production.
Plan-action-outcome audit trail
EU AI Act, NIST AI RMF, and sector-specific artefacts generated across full agent runs, traceable end-to-end.
One pattern, every workflow
The same assurance shape applied as the enterprise's agent estate scales, instead of one tool per agent.
