Governance for AI agents

Understand your agents.
Account for their decisions.

See what your AI agents are doing, measure recorded outcomes and investigate the decisions that matter. Bring governance reporting and independently verifiable evidence into one workspace.

Explore the governance demo ↗Discover the platform →

Synthetic demo · Private testnet pilot · Production access in development

◒ ORVESSIAN / GOVERNANCE WORKSPACEIllustrative data
Recorded decisions1,28012 reporting agents · illustrative 7-day window
Reporting activity by agent · synthetic example
Agent / deploymentDecisionsLatest reported action
Purchasing assistant procurement-v2480Approval requested
Support triage support-v3640Case routed
10 other reporting agents160Decision recorded
Investigate purchasing decision PO-1042 +
Recorded action
Approval requested
Submitted policy context
Requested spend above the configured limit
Reported outcome
Human review pending

This example shows submitted context, not an independently detected policy breach. The customer's application enforces the limit.

Open the interactive synthetic demo ↗
Activity you can understandDecisions you can investigateEvidence you can independently check

The questions behind responsible AI

What are your agents doing?
How well is it working?

Give engineering, operations and governance teams a shared view of recorded AI activity, from the daily overview to the detail of an individual decision.

01 / ACTIVITY

See the work taking place.

Explore reporting agents, recorded decisions and activity over time. Filter the view by project and model deployment.

Explore this workflow →

02 / OUTCOMES

Make performance understandable.

Compare labelled outcomes across model cohorts. See sample sizes and missing labels alongside the results.

Explore this workflow →

03 / INVESTIGATION

Get from a trend to a decision.

Investigate submitted incidents and the structured context behind individual decisions. Bring the relevant evidence into the review.

Explore this workflow →

Agent governance, in practice

From delegated work
to accountable decisions.

Bring the people responsible for AI around a shared record of activity, outcomes and exceptions. Build a governance process that keeps pace with the work.

01

Understand the activity

Which agents are reporting, and what decisions are they recording? Start with the activity you receive and make gaps visible.

Pilot: scoped activity reporting
02

Review the decisions

Follow recorded actions, model versions, policy context and labelled outcomes. Investigate exceptions with the evidence in view.

Pilot: decisions, outcomes & submitted incidents
03

Organise the response

Give incidents an owner. Set tolerances, deliver alerts and retain a resolution history so reviews become repeatable work.

Planned: rules, alerts & incident workflow
04

Substantiate the account

Export selected records with signatures and proofs. Check their integrity against a blockchain anchor outside the portal.

Pilot: independent evidence verification

Our roadmap adds agent owners, purpose and heartbeat status. Reporting coverage is not automatic discovery; immediate permissions and enforcement remain in your agent runtime.

Our evidence foundation

Governance reporting.
Cryptographic accountability.

Signed records and cryptographic hashes anchored to the Base blockchain give authorised reviewers an independent reference for checking the evidence.

A change to an anchored record can be detected by checking it against its original commitment. Export the record and proof to verify the checks outside the portal.

Explore the verification layer →
01

Structured record

Selected context stays private; source conversations remain in your systems.

02

Signature & proof

A recording-service signature and batch membership proof connect the record to its commitment.

03

Blockchain anchor

A cryptographic batch commitment is published on Base. The pilot uses Base Sepolia.

04

Independent check

Check the record, signature, proof and canonical anchor with an authorised evidence export.

Tamper-evident records do not prove complete capture, source truth or AI correctness. Private storage and access controls remain essential.

Built around the review

From “what changed?”
to a record you can inspect.

Start with the overview. Compare outcomes. Open a decision. Follow its evidence. Keep the business question and verification in the same workflow.

Try the synthetic workspace →

Illustrative purchasing workflow

A decision needs a closer look.

  1. Find the activityA purchasing agent records a request above its policy limit.
  2. Inspect the contextReview the submitted policy reference and approval outcome.
  3. Check the evidenceInspect the signature, proof and anchor state in the pilot workspace.

A platform with a clear direction

Visibility today.
Deeper governance next.

The synthetic platform demonstrates activity reporting, model comparisons, submitted incidents and evidence investigation. Agent heartbeats, configurable alerts, incident ownership and scheduled reports are on the roadmap.

Submit selected structured data through the API. Your source conversations, documents and media stay in your systems.

Explore proposed plans →View readiness