Enterprise Agent Governance Early Access — Q2 2026
Agentic AI without governance
is a liability, not a capability.
Governance-first infrastructure for the agentic enterprise. Centralized agent registry, human-oversight circuit breakers, policy-as-code enforcement, and full decision traceability — built before the agents go live, not scrambled after the first incident.
40%
of agentic AI projects cancelled by 2027 — Gartner
91%
of enterprises deploying agents without mature governance
35%
of enterprises cannot shut down a rogue agent
Six structural failures agentic AI exposes
These are not edge cases. They are the default state for enterprises deploying autonomous agents without a governance layer. Gartner's prediction is not pessimism — it is pattern recognition.
01
No centralised control over deployed agents
55% of enterprises describe their agentic AI use as "chaotic." Agents are deployed by individual teams, tracked in spreadsheets if tracked at all, and owned by no one when something goes wrong. You cannot govern what you cannot inventory.
02
Governance bolted on after the product ships
Building governance into an agent after it is in production requires redesigning it from scratch. Audit logging, access controls, human-oversight hooks, and policy enforcement are architectural decisions — not features you can add in a sprint after launch. Late governance means expensive re-architecture.
03
No decision traceability for regulatory audit
When a regulated enterprise's agent makes a consequential decision — approves a transaction, denies a claim, initiates a communication — regulators want to know: who authorised the agent to make that decision, on what data, under what policy, and who was responsible? Without a decision trace store, you cannot answer those questions.
04
Policy in documents, not in code
79% of agentic AI incidents occur in ungoverned areas — not because the policy did not exist in a document, but because the policy was never encoded as a technical constraint the agent operates within. Acceptable-use policies that live in PDFs do not constrain agents. Policy-as-code does.
05
No human-oversight thresholds or circuit breakers
35% of enterprises that have deployed autonomous agents cannot shut them down quickly if something goes wrong. There is no circuit breaker, no escalation threshold, no kill switch. The agent that was approved for one task scope expands its own authority until a human notices — if a human notices.
06
The wrong vendors, confidently deployed
Of the thousands of AI vendors in the market, fewer than 130 offer genuine agentic capability with enterprise governance. Enterprises are deploying tools with impressive demos and no governance architecture — discovering the gap when they try to satisfy their first compliance questionnaire or audit request.
What ships in Q2 2026
Four committed capabilities that give enterprises the foundation of agentic governance — before they need it the hard way.
01
Centralized Agent Registry
A single inventory of every autonomous agent deployed across your enterprise: identity, owner, scope, permissions, current status, and deployment history. No agent operates without a registry entry. Shadow AI reduction target: 90% coverage of deployed agents within 30 days of activation.
02
Governance-First Onboarding
New agents are onboarded through a structured workflow: scope declaration, risk classification, human-oversight thresholds defined, policy constraints agreed, approval granted. Governance is a precondition of deployment — not a retrospective exercise. Target: time-to-governance under 2 hours for standard agent types.
03
Agent Circuit Breaker
Every registered agent has configurable circuit breaker thresholds: anomalous action rates, scope boundary violations, escalating error patterns, or explicit human-override triggers. When a threshold fires, the agent is suspended within 30 seconds, the owner is notified, and no further actions are taken until human review. The circuit breaker you cannot shut down fast is the one you will regret deploying.
04
Role-Based Skill Library
Agent capabilities are defined as governed skills: discrete, auditable units of authority that an agent can be granted. Skills are scoped by role, approved by owners, and revocable without redeploying the agent. This separates what an agent can technically do from what it is currently authorised to do.
What comes next — Q3 2026 and beyond
The Q2 foundation enables the Q3 governance layer. These capabilities build on the registry, circuit breaker, and skill library that ship first.
Policy-as-Code Engine
Define governance constraints in code that agents operate within at runtime — not in documents that agents never read. Policies are versioned, auditable, and enforced at the skill execution layer.
Multi-Framework Compliance Pack
Pre-built governance templates for GDPR, HIPAA, SOC 2, EU AI Act, and SOX. Map your agent inventory to framework requirements and generate evidence packages for auditors.
Decision Trace Store
An immutable, queryable log of every consequential agent decision: what data was used, which policy applied, what action was taken, and who was responsible. Designed for regulatory audit, not just internal review.
Skill Approval Workflow
Structured approval workflow for granting agents access to new skills: risk assessment, owner sign-off, compliance review, time-bounded grants. Approval decisions are logged and auditable.
Get early access before Q2 2026.
Early access organisations shape the product. Tell us your agentic AI deployment today — how many agents, what they do, and what governance gap you are trying to close. We will prioritise your use case in Q2 delivery and give you first access when the product ships.