Governance infrastructure for autonomous AI.
Your AI agents are making consequential decisions in production — approving loans, routing claims, escalating support tickets, executing trades. ADJUDON provides the runtime governance layer that scores, enforces, and proves every decision before it reaches the end user. Not documentation. Not monitoring. Enforcement.
Monitoring tells you what happened. Governance prevents what shouldn't.
AI observability tools generate dashboards. They show you token counts, latency distributions, and error rates. They tell you what your agents did — after the fact. When a model hallucinates a medical recommendation, approves a fraudulent transaction, or leaks customer PII, a dashboard shows you the event. It does not prevent it.
AI governance is fundamentally different from AI monitoring. Governance means the system has the architectural authority to block a decision before it executes, route an uncertain decision to a human before it ships, and produce a cryptographic proof that both of those things happened. The gap between "we saw it" and "we stopped it" is the gap most organizations have not closed.
Runtime governance — not pre-deployment documentation
Most AI governance platforms focus on model registries, risk assessments, and compliance documentation — activities that happen before deployment and are never revisited at runtime. ADJUDON operates at the opposite end: every decision is evaluated, enforced, and logged at the moment it occurs.
Score independently
The Confidence Engine evaluates every decision using three independent signals — base probability, decision entropy, and historical vector similarity — producing a single CPI score. This is not the model's self-reported confidence. It is an external, calibrated assessment of how much you should trust this particular output.
Enforce deterministically
The Policy Engine applies your rules to every decision at ingest. Conditions, thresholds, and actions are deterministic — no probabilistic reasoning, no LLM interpretation. A decision that violates a policy is blocked (403) or flagged for review (202) before your application can act on it.
Prove cryptographically
The Audit Trail logs every decision with a SHA-256 hash chain. Every trace — input, output, score, policy match, human override — is immutable and verifiable. When a regulator asks for proof, you provide a cryptographic verification — not a spreadsheet someone assembled last week.
Built for any architecture that makes decisions autonomously
ADJUDON is agent-agnostic and model-agnostic. If your system produces a decision that affects a user, a customer, or a regulated process, ADJUDON governs it.
Autonomous Agents
Single-agent systems that take actions without human confirmation — approving applications, executing transactions, generating recommendations. ADJUDON scores and enforces every action before execution and logs the full decision context.
LLM-Powered Workflows
Pipelines where an LLM generates content, summaries, or classifications that feed into downstream business logic. ADJUDON evaluates the LLM output at the integration boundary — before it enters your production database or reaches the end user.
Decision Automation
Rule-based or hybrid systems where AI handles the majority of cases and exceptions are escalated. ADJUDON adds the governance layer that distinguishes confident automation from uncertain guesswork — and routes the uncertain cases to humans automatically.
Multi-Agent Systems
Architectures where multiple agents collaborate, delegate, or compete. ADJUDON can evaluate each agent's output independently, enforce policies per agent, and maintain a unified audit trail across the entire agent graph.
Regulatory alignment built into the runtime
ADJUDON's architecture maps directly to specific regulatory requirements — not as a documentation exercise, but as enforced technical controls.
EU AI Act — Article 13 (Transparency)
Every decision trace is logged with full context, scored independently, and stored in a cryptographic hash chain. The audit trail is exportable on demand for regulatory review. Article 13's requirement for traceable, explainable AI system behavior is satisfied at the infrastructure level.
EU AI Act — Article 14 (Human Oversight)
The Policy Engine enforces deterministic routing to the human review queue when confidence is low or policies are triggered. Oversight is not optional or advisory — it is architecturally enforced. Reviewers see full decision context, score breakdowns, and policy matches before approving or rejecting.
GDPR Article 28 & BaFin Requirements
ADJUDON operates as a Data Processor under GDPR Art. 28. Data residency is EU-only (Frankfurt). PII masking is applied at ingestion. Zero Training Policy is contractual. BaFin requirements for algorithmic decision transparency are addressed by the export-ready audit trail and the deterministic policy enforcement layer.
Governance that runs at the speed of your agents.
Deploy runtime governance in under 10 minutes. No model changes. No retraining. No batch jobs.