Deterministic AI Governance: Enforce ‘Cannot’ Policies, Not ‘Will Not’ Behaviors
Decision Infrastructure compiles policy, authority models, and compliance constraints into the decision path itself. Non-compliant outputs aren't blocked after the fact — they're removed from the possibility space. The model doesn't refuse. It structurally cannot
The Problem
Guardrails Are Probabilistic. Production Requires Determinism
Today's AI governance relies on prompt-based instructions that hope the model complies. This "will not" approach is probabilistic, silent in failure, and impossible to audit. Enterprise production requires a "cannot" approach where unauthorized actions are structurally impossible
Weak Enforcement
Prompts may suggest rules but don’t actually enforce them. Policy violations can still occur even when instructions exist
Instructions, not controls
No hard enforcement layer
Violations still possible
Relies on model compliance
No deterministic prevention
Outcome: Policies can be ignored without technical prevention
Post-hoc Detection
Monitoring only detects violations after they’ve happened. Logs and alerts cannot prevent unauthorized actions beforehand
Reactive detection
Unauthorized action already executed
Logs reviewed post-event
Alerts after damage
No pre-action blocking
Outcome: Compliance becomes reactive instead of preventive
Model Drift
Model updates can silently change how guardrails behave. Fine-tunes edits can degrade enforcement without notice
Version changes alter outputs
Fine-tuning shifts interpretation
Prompt edits impact enforcement
Guardrails degrade unnoticed
No stability guarantees
Outcome: Behavioral controls become unreliable over time
How It Works
Policy Compiled Into the Decision Path
Decision Infrastructure operates at the architectural level, not the behavioral level. Rules are compiled into the execution environment itself
Policy Gates
Deterministic checkpoints that evaluate every agent action against policy before execution. Same input + same policy = same enforcement, every time
Authority Models
Structured authority as data — not buried in prompts. Every agent, every user, every role has an explicit authority level that determines what actions are possible
Decision Boundaries
Validity enforcement that scales with risk and evolves as the organization evolves. Not permanent rules — adaptive boundaries
Core Capabilities
What Decision Infrastructure Delivers
Structured decision infrastructure enforces policies, authority, and context for reliable, auditable actions.
Policy Gates
Every agent action passes through a deterministic gate before execution. The gate evaluates context, authority, and policy — then allows, modifies, escalates, or blocks
Authority as Structured Data
Authority models define who can do what, up to what threshold, in what context. Agents inherit authority from their principal — never exceed it
Threshold Matrices
Multi-dimensional approval matrices that consider amount, risk level, category, and urgency. A $5K vendor payment and a $5K employee reimbursement can require different authority
Contextual Escalation
When an action exceeds an agent's authority, it doesn't fail silently — it escalates to the appropriate authority level with full context
Version-Controlled Policies
Every policy change is versioned, timestamped, and attributed. Decision Replay can re-evaluate past decisions under any historical policy version
Typed Action Space
Every possible agent action has a type, a required authority level, and a set of preconditions. Actions outside the typed space don't exist
Use Cases
Where Decision Infrastructure Operates
Enforce policies, approvals, and access dynamically across agents, ensuring compliance and structured authority at every step.
Integrations
Connects to Your Policy Stack
Decision Infrastructure integrates with existing policy, identity, and compliance systems
Identity & Access
GRC Platforms
Policy Engines
Audit & Compliance
FAQ
Frequently Asked Questions
"Will not" is behavioral, relying on prompts and can fail silently. "Cannot" is structural, removing unauthorized actions from the output space — the same principle behind constrained decoding in structured generation
Every agent action passes through a Policy Gate that evaluates context, authority, and version-controlled policy, producing one of four deterministic outcomes: Allow, Modify, Escalate, or Block — same input plus same policy always yields the same result
Authority is structured: every agent and user has roles, scopes, and thresholds. Multi-dimensional matrices differentiate approvals, and delegated authority is always scoped to the intersection of delegator and delegate permissions
Policies are version-controlled and hot-swappable: updates take effect immediately for future decisions, while past decisions retain their original policy version, enabling Decision Replay for impact analysis
Ready to See Decision Infrastructure in Action?
See how Decision Infrastructure deploys over your existing infrastructure in 4 weeks — with structural governance from day one