Feature · Agentic AI governance
SentinelAI extends governance to agentic systems by registering each agent with its declared tool permissions, autonomy level, and memory scope, then tracing the full chain of custody across multi-agent executions so oversight keeps pace with what agents actually do.
What this area covers
Most platforms govern static models, but autonomous agents act, call tools, and chain decisions across steps. The agentic governance workspace gives teams a record of every agent, its permitted tools, its autonomy boundary, and its execution history so accountability is maintained as agents operate.
Related product areas
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
Monitor live LLM outputs for toxicity, PII leakage, prompt injection, and hallucination against per-model thresholds, using privacy-preserving request hashes.
Bring live assurance signals, telemetry connector management, trigger rules, and evidence-ready monitoring context into AI governance workflows.
Coordinate alerts, findings, remediation, evidence posture, SLA deadlines, and closure outcomes in one shared case workspace.
Generate a content-hashed AI Bill of Materials capturing model, dataset, and framework provenance, flag license conflicts, and export CycloneDX, SPDX, and PDF.
Core capabilities
Register each agent with its declared tool permissions, autonomy level (supervised, human-in-the-loop, or fully autonomous), and memory scope so its intended operating boundary is documented.
Trace the full chain of custody across multi-agent executions, capturing which agent acted, which tools it invoked, and how decisions passed between agents.
Apply agentic-specific readiness gates so an agent must satisfy the relevant governance checks before it is cleared to operate at its declared autonomy level.
Raise governance findings when an agent exceeds its declared tool permissions or action depth, surfacing the deviation for review instead of letting it pass unnoticed.
Give governance teams a shared inventory of agents alongside models so autonomy, permissions, and execution history are reviewed in the same operating context.
Target users
Governance value
How teams use it
Step 1
Capture each agent's tool permissions, autonomy level, and memory scope as the foundation for later review and tracing.
Step 2
Apply agentic readiness gates before clearing an agent, then trace its executions and tool calls as it operates.
Step 3
Use boundary findings to review and resolve cases where an agent exceeds its declared permissions or action depth.
Continue exploring
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
Monitor live LLM outputs for toxicity, PII leakage, prompt injection, and hallucination against per-model thresholds, using privacy-preserving request hashes.
Bring live assurance signals, telemetry connector management, trigger rules, and evidence-ready monitoring context into AI governance workflows.
Coordinate alerts, findings, remediation, evidence posture, SLA deadlines, and closure outcomes in one shared case workspace.
Generate a content-hashed AI Bill of Materials capturing model, dataset, and framework provenance, flag license conflicts, and export CycloneDX, SPDX, and PDF.