How to compare agentic AI governance platforms

Agentic AI governance platforms do not all govern the same layer. Some inventory models, some filter prompts, some secure model gateways, some monitor applications, and some control end-to-end actions. A useful comparison starts with the workflow and consequence, then asks where each control is enforced.

This page compares platform approaches and evaluation dimensions rather than publishing unsupported vendor grades. Product capabilities change, contract scope varies, and a logo matrix ages quickly. Use the framework to collect dated evidence from every candidate.

Which platform categories are commonly confused?

Platform category	Primary job	Typical strength	Common gap for agents
Model gateway	Route and observe model requests	Central endpoint policy and usage visibility	May not control downstream tools or durable actions
AI security scanner	Detect prompt, data or model vulnerabilities	Testing and attack detection	May not own runtime authorization or approval
Governance registry	Inventory systems, risks, models and obligations	Documentation and accountability	Records intended controls but may not enforce them
Observability platform	Trace model and application behavior	Debugging, quality and operational visibility	A trace after execution cannot stop the action
Agent framework	Build plans, tools and multi-agent workflows	Developer velocity and orchestration	Governance may remain application-specific
Vertical application	Deliver one domain workflow	Strong task context and user experience	Control reuse and evidence portability may be limited
Agentic control plane	Apply policy, approval and evidence across workflows	Runtime authority and consistent execution records	Requires integration depth and release discipline

An organization may need more than one category. The decision is about ownership and integration, not finding one label that claims everything.

Which dimensions should the comparison table include?

Dimension	Strong evidence	Warning sign
Governance model	Named policy authority and action catalog	Principles without enforceable decisions
Authorization	Contextual checks at each tool and data boundary	Session-level role or prompt-only restrictions
Human approval	Parameter-bound, expiring, single-use state	Generic confirmation dialog
Audit integrity	Public schema, verifier and mutation tests	Searchable logs with no integrity proof
Provenance	Identity, sources, versions and receipts linked	Model output stored without origin
Deployment envelope	Cloud, on-premise or disconnected dependencies stated	"On-premise" that still requires hosted models
Model portability	Policy and audit remain stable across endpoints	Provider list with no evaluation continuity
Tool control	Typed operations, least privilege and denial tests	Generic arbitrary-code or HTTP tool
Failure behavior	Deny, hold, retry and reconcile states documented	Silent best-effort continuation
Tenancy	Query-shaped isolation and cross-tenant negatives	Post-query filtering
Release evidence	Tests tied to exact versions and environment	Evergreen architecture claims
Export and review	Authorized, documented evidence package	Vendor-only dashboard access

Weight the dimensions by the consequences in your workflow. A drafting assistant and an agent that grants access should not share one scorecard.

How do you evaluate runtime enforcement?

Trace one action from request to side effect. Identify the enforcement points for data retrieval, model routing, approval, tool invocation, target-system write, and audit commitment.

Then attempt to bypass the normal path:

• call a tool operation the policy does not recognize;
• change a material parameter after approval;
• use an expired or revoked grant;
• cross a tenant boundary;
• select an unapproved model endpoint;
• repeat a single-use action;
• cause a timeout after the target accepts the request;
• make the audit store unavailable.

The platform should produce a deterministic state and an evidence trail. A model refusing in natural language is not runtime enforcement.

How do you compare audit integrity?

Ask five separate questions.

What is recorded?

Review event coverage for identity, task, policy, approval, tool request, downstream receipt, result and release context.

How is change detected?

Inspect canonicalization, hash linkage, checkpoints, signatures, or transparency proofs. "Immutable" without a construction and verifier is a marketing word.

How is completeness tested?

Request a map from every state-changing boundary to required events and negative tests. A valid chain cannot reveal an action omitted before hashing.

Who can read or export it?

Audit data needs contextual authorization, tenant separation, retention, redaction and export controls.

Can another party verify it?

Prefer a documented format and supported export. A badge visible only inside the vendor dashboard is weak diligence evidence.

How do you compare human decision authority?

Review the action taxonomy and ask which actions are automatic, reviewed, dual-approved or prohibited. Examine what the reviewer sees, how the exact action is bound, how long approval remains valid, how edits are handled, and how duplicate execution is prevented.

Measure reviewer workload and queue age. A theoretically perfect gate can fail operationally when every low-risk action requires the same manual review.

How do you compare deployment envelopes?

"Cloud," "private," "on-premise," and "air-gapped" are often used loosely. For each candidate, list every runtime dependency and update path.

Question	Cloud	On-premise connected	Air-gapped
Where do models run?	Provider or private cloud	Local, provider, or mixed	Inside enclave
Can policy depend on vendor control plane?	Often	Only if permitted	No
How do updates arrive?	Online release path	Online or managed transfer	Controlled transfer
Where are audit records retained?	Contract-defined	Customer or mixed	Inside enclave
How is identity supplied?	Cloud identity or federation	Enterprise identity	In-boundary identity
What happens without internet?	Degraded or unavailable	Configuration-specific	Normal operating condition

Ask the vendor to run the workflow under the proposed topology. A deployment diagram is not proof that the product functions without hidden external dependencies.

How do you compare model independence?

Model agnosticism is operational when the workflow can change endpoints without rewriting policy, losing audit continuity, or silently accepting different behavior.

Collect evidence for:

• a stable endpoint manifest or capability contract;
• task-specific evaluation across candidate models;
• data-location and provider-term constraints;
• versioned model selection policy;
• fallback behavior and stop conditions;
• continuous audit identity across a route change;
• cost and quality measurement tied to the task;
• exit procedures for stored prompts, embeddings, and logs.

If every provider integration has different control semantics, the platform may be multi-provider without being model agnostic.

How do you compare standards and compliance claims?

Ask whether the claim is a mapping, assessment, certification, authorization, or contractual requirement. Those are different things.

A strong answer identifies the control, implementation, evidence, owner, review date, known limitation, and customer responsibility. A weak answer lists framework logos without saying which outcome or release is supported.

What should a proof-of-value exercise contain?

Use one real but non-sensitive workflow with a meaningful side effect. Define acceptance criteria before the exercise.

1. Connect a representative identity and resource boundary.
2. Configure one allowed, one reviewed and one prohibited action.
3. Run the ideal path and retain the evidence.
4. Change approved parameters and confirm denial.
5. Attempt cross-tenant or cross-classification access.
6. Interrupt a dependency and inspect recovery.
7. Change the model endpoint and rerun evaluation.
8. Export the audit package and verify it independently.
9. Measure operator work, reviewer burden and time to investigate.
10. Document gaps and who owns closure.

A polished demo is not a proof when the vendor chose every input and no negative path ran.

Where does Trunnion fit in the comparison?

Trunnion positions its company-level architecture around governed agentic workflows: contextual policy, human decision authority, model and tool orchestration, and reviewable execution evidence across cloud, on-premise and disconnected designs. The portfolio combines a control-plane thesis with vertical applications and a custom-build lane.

Those public design objectives are not a substitute for release evidence. Buyers should evaluate the exact Trunnion offering, control configuration, deployment and written scope using the same tests applied to every other candidate.