Intellicor AI

Transforms execution into structured meaning

Raw agent execution — tool calls, prompts, payloads, responses — is parsed into structured behavioral events. Each event is enriched with intent, target, scope, and contextual metadata, producing a continuous semantic stream the rest of the system can reason over.

• ·Intent and action classification
• ·Cross-step context linking
• ·Behavioral event normalization

Defines hard constraints

A declarative layer for absolute boundaries — what an agent must never do, regardless of context. Policies are versioned, auditable, and evaluated deterministically. The kernel separates non-negotiable rules from probabilistic judgement.

• ·Declarative policy definitions
• ·Deterministic evaluation
• ·Versioning and audit trail

Estimates forward risk and uncertainty

Models agent behavior as a trajectory under uncertainty. Instead of evaluating a single action in isolation, it projects likely next states and estimates the probability of failure, drift, or violation — with explicit confidence bounds.

• ·Forward trajectory modeling
• ·Confidence and uncertainty quantification
• ·Drift and anomaly scoring

Identifies boundary proximity before failure

Detects when behavior is approaching a policy boundary — not just when it crosses one. By measuring distance to constraints across the trajectory, the system can intervene early, before a violation becomes inevitable.

• ·Boundary-proximity scoring
• ·Pre-violation signaling
• ·Trajectory-aware thresholds

Determines when and how to act

Combines policy state, risk estimates, and uncertainty into a single decision: allow, require approval, restrict, or block. The decision policy balances operational throughput against risk tolerance and routes interventions to the Control Layer for enforcement.

• ·Allow / approve / restrict / block
• ·Risk- and uncertainty-aware routing
• ·Human-in-the-loop escalation