Runtime Governance Layer
Adaptive AI Governance
The rise of autonomous, multi-agent AI systems expands the enterprise threat model beyond prompt-level controls, requiring continuous runtime governance, policy enforcement, execution monitoring, and auditability. This platform classifies every request's risk tier before a model is selected, and routes standard, security-sensitive, regulated, and blocked requests differently — live, not as a policy document.
Part of the Enterprise Capability Lifecycle— this page covers Validation & Approval.
Where this sits
Adaptive governance sits between the model router and the model itself — layer 6 of the platform's 15-layer execution flow. It is a genuinely new layer added to this architecture, not a renamed existing one: standard model routing decides which model based on cost and complexity, this layer decides whether and how the request should proceed at all.
Enforced today vs. the broader threat model
The risk classifier below operates at request/routing time — one decision, made once, before a model is selected. That is one instrument in a larger set enterprises need as agents move from single prompts to long-running, multi-step execution: plan → call tool → read memory → write memory → delegate → call MCP server → repeat. Being precise about the difference matters more than claiming broader coverage than what is actually built.
Enforced today, live
- Prompt injection blocking
- Security-sensitive request restriction
- Regulated-domain policy + audit
- Human approval escalation (HITL)
Broader enterprise threat model
- Tool abuse across a running agent
- Memory poisoning
- MCP misuse
- Cross-agent coordination, workflow escalation, data exfiltration
Industry signal
In August 2026, Uber open-sourced ADR (Agentic Detection and Response), an EDR-style runtime detection system for agentic AI — sensors that capture agent telemetry (prompts, MCP activity, reasoning traces, tool calls) and a detection layer that flags credential exposure, prompt injection, data exfiltration, and policy-violating tool use. Uber's code is not yet public; the project is described in an accompanying paper. It is not a dependency of this platform and nothing here integrates with it — it is cited as external validation that runtime, telemetry-driven detection for agentic AI is becoming a recognized enterprise category, the same category this page's risk classification, prompt-injection blocking, and audit trail already operate in at a smaller, single-request scope.
Runtime Risk Classification
Every request is classified into a risk tier — standard, security-sensitive, regulated, or blocked — before a model is ever selected, not reviewed after the fact.
Why it matters
Static, point-in-time policy review cannot keep pace with agentic systems making thousands of routing decisions a day. Classification has to run at request time.
Risk-Aware Model Routing
Standard requests proceed through normal cost/latency routing. Security-sensitive requests are downgraded to a restricted model tier pending human review. Regulated-domain requests proceed under policy with a mandatory audit entry. Prompt-injection attempts are blocked outright.
Why it matters
A model router that only optimizes for cost and latency is incomplete — enterprise routing has to account for what a request is asking, not just how hard it is.
Human Approval Escalation
When a request is classified as security-sensitive, the runtime marks it as pending human review rather than either silently allowing or silently refusing it.
Why it matters
Binary allow/block governance loses information. An escalation path preserves the request for a human decision instead of forcing a false choice.
Policy-Governed Regulated Requests
Requests touching regulated domains (medical, legal, financial, compliance-sensitive) are not blocked — they proceed under policy, with a mandatory audit trail entry attached.
Why it matters
Over-blocking regulated topics makes an assistant useless for real enterprise workflows; the safer control is auditability, not refusal.
Prompt Injection Blocking
Requests matching known prompt-injection and jailbreak signatures are blocked before reaching any model, and the block is written to the audit trail.
Why it matters
This reuses the same injection-detection logic already enforced on every API route in this platform — one control, applied consistently, not duplicated per demo.
Audit Trail on Every Decision
Every risk classification — standard, restricted, policy-approved, or blocked — is a structured, traceable decision, not a silent pass-through.
Why it matters
Compliance and incident response both depend on being able to answer "why did the system do that" after the fact, for every request, not just the flagged ones.