Agents Are Not Just Chatbots
The AI agent landscape has shifted dramatically. Modern agents don't just answer questions — they execute multi-step workflows, access databases, call external APIs, manage infrastructure, and make decisions with real consequences. An AI agent managing your deployment pipeline can push code to production. One handling customer support can issue refunds and modify accounts. A compliance agent can flag or block transactions. The blast radius of an unchecked agent is enormous.
Why Traditional Controls Fail
Role-Based Access Control (RBAC) was designed for humans with predictable behavior patterns. Agents break every assumption RBAC makes: they operate at machine speed (thousands of actions per minute), they exhibit emergent behavior (the same prompt can produce different actions), they chain actions autonomously (step 3 depends on step 2's output, which depends on step 1), and they interact with each other (agent A calls agent B which calls agent C). You can't govern this with static role assignments. You need dynamic, context-aware policy evaluation.
DRD's Three-Tier Enforcement Model
DRD implements a graduated enforcement model: Warn (log the violation, notify operators, let the action proceed — for low-severity policy breaches), Block (prevent the action from executing, notify operators, log the attempt — for medium-severity breaches), and Kill (terminate the agent's session entirely, revoke credentials, trigger incident response — for critical violations). This graduated approach avoids the all-or-nothing problem of traditional access controls. Most violations are learning opportunities, not emergencies.
Policy Engine: Declarative Rules
DRD policies are declarative — you define what agents can and cannot do, not how to enforce it. A policy might state: 'Agent customer-support-ai may issue refunds up to $500 per transaction and $2,000 per day. Refunds above $500 require human approval. Any action affecting more than 100 accounts in a single batch is blocked.' The policy engine evaluates every agent action against active policies in real-time. Sub-50ms latency means governance doesn't slow your agents down.
Multi-Agent Coordination
When agents interact with each other, governance gets complicated. Agent A's output becomes Agent B's input — a policy violation can cascade through the chain. DRD tracks agent-to-agent interactions and evaluates policies at every handoff point. If Agent A produces output that would cause Agent B to violate its policies, DRD catches it at the boundary — before Agent B ever acts on it.
The Governance Stack
A complete agent governance stack needs four layers: Identity (who is this agent?), Authorization (what can it do?), Monitoring (what is it doing?), and Enforcement (what happens when it violates policy?). DRD provides all four. W3C Verifiable Credentials for identity, declarative policies for authorization, real-time event streaming for monitoring, and three-tier graduated enforcement. The result: autonomous agents that operate within defined boundaries, with full auditability and real-time control.
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