From uniform rules to proportional governance
Not every agent needs the same checkpoint. Controls scale with what each agent can do, so people stay in the loop where decisions carry real risk, and simple agents aren't left waiting in the same queue.
Complimentary Gartner® report
Classifying AI agents by autonomy level: does every agent need the same human oversight?
Access your complimentary copy of this Gartner® report to learn how matching oversight to each AI agent's autonomy level helps organizations keep humans in control where it counts, without slowing everything else down.
Access the full report now and learn:
The governance mistake Gartner discusses to be the primary driver of AI agent failure in the enterprise.
The four autonomy levels, from observing to acting autonomously, and the controls each one requires.
Why autonomy and scope are separate risks, and why a read-only agent isn't automatically low risk.
The hidden cost of over-governing AI agents, and how it pushes development into the shadows.
“By 2027, 40% of enterprises will demote or decommission autonomous AI agents due to governance gaps identified only after production incidents occur.”
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Inside the research
Three ways autonomy levels redefine AI agent governance
From maximum autonomy to the right autonomy
You decide how far each agent goes: observe, advise, act with approval, or act autonomously. Many agents deliver their best value without ever acting on their own, and some should never be allowed to.
From blind trust to earned trust
Agents earn more responsibility on evidence: proven reliability, the right controls and a clear business case. Audit trails show what each agent did and why, and an agent can be moved back down if it stops earning that trust.
antaivo by Cora
How antaivo governs every AI agent through a single governed AI layer
Gartner framework makes clear that how far an AI agent can safely act depends on the governance around it. Many vendors are retrofitting AI into legacy platforms. We believe that approach struggles to govern consistently, because every part of an older stack brings its own architecture and its own rules. antaivo took a different route. It was rebuilt from the foundations, with a new data model, a new orchestration layer and intelligence built in throughout.
antaivo: built in from the ground up
Consistent, auditable AI that's ready for production.
AI bolted on, feature by feature
- Workforce AIOwn rulesOwn audit logOwn model
- Finance AIOwn rulesOwn audit logOwn model
- Reporting AIOwn rulesOwn audit logOwn model
Inconsistent rules, scattered audit trails, and AI that stalls at proof of concept.
One governed AI layer
At its core is a single governed AI layer. Every AI request passes through it, so every action is audited, every AI call is cited and every output is explained.
Scoped, deterministic, model agnostic
Agents are scoped to the same permissions as the person using them. All calculations run in antaivo's deterministic engine rather than in the language model. The platform is model agnostic, so organizations can use the LLM of their choice, including a private one.
A person in the loop, every time
Every antaivo agent keeps a person in the loop: the agent proposes, a person applies, and the action is logged against their name. Because the safety is in the architecture rather than the policy, the same controls hold for every agent and every model, however your use of AI grows.
Gartner, Avoid Governance Mismatch: Classify AI Agents by Autonomy Level, Shiva Varma, 2 March 2026. GARTNER is a trademark of Gartner, Inc. and/or its affiliates.