Decision Governance
for Autonomous Agents and AI

Why governing AI models isn't enough when agents make business decisions, access systems of record, and take actions.

Decision Governance for Autonomous Agents brings together four disciplines:

• Agent Decision Tool to enable agents to make decisions deterministically with full explainability.
• Agent Action Tool to enable agents to take actions within an admissible boundary at runtime.
• Data, Context, and Semantic Control classifies content, blocks injection, and governs what flows into every decision.
• LLM and Prompt Management controls prompt construction, output scanning, and sensitive data leakage.
• Agent and Action Control enforces admissibility, boundaries, RBAC, and tool-level governance.

Formalizing a decision is only the first step. The real shift happens when a formalized decision is registered as a tool inside an autonomous agent. That is when all four disciplines activate together. No rule engine, no AI governance framework, and no agent builder covers this today.

• EU AI Act enforcement is already underway. NIST AI RMF and ISO 42001 adoption is accelerating. If your agents are making business decisions and your governance stops at the model, you have a compliance gap today, not tomorrow.
• Every AI governance framework today stops at the model. None of them governs the business decisions an agent makes, the data it accesses, the systems of record it writes to, or the actions it takes.
• A formalized decision without the other three disciplines is just a rule. With them, it is accountable, auditable, and secure.
• You will walk away with a practical architecture from decision formalization to governed agent execution.

• Security Alignment OWASP Top 10 and OWASP Agentic Top 10 coverage as an architectural property, not a checklist.
• The Gap in AI Governance Frameworks
• Real-World Scenarios
• From Decision Support to Decision Making