AI agent governance
Give every AI action an identity, boundary, approval path, and record.
Governance becomes operational when AI moves from answering questions to changing real systems. Dialogo controls what starts a workflow, which agent acts, what it may access, which actions require approval, and what evidence remains afterward.
The operating problem
An answer can be reviewed. An action changes the business.
When an agent updates an ERP record, prepares an external communication, changes a commitment, or initiates a payment process, companies need more than a prompt history. They need clear authority, permissions, decision ownership, technical guardrails, and durable evidence.
A strong fit for
- Regulated and security-conscious organizations
- IT teams evaluating agent access to production systems
- Operations teams automating sensitive cross-system processes
- Companies using frontier models that require a separate control layer
From event to evidence
One controlled execution chain.
Verified requester
Associate the workflow with the person, team, service, or event authorized to start it.
Delegated mandate
Define which agent is acting, for whom, for what purpose, and for how long.
Scoped permissions
Limit the systems, records, tools, and actions available to that specific workflow.
Guardrail checks
Apply policy and runtime controls before tools and frontier models can perform an action.
Human approval
Pause at consequential steps and record the responsible person’s decision.
Verifiable execution
Preserve the complete chain from request and context through action and outcome.
Platform capabilities
Control the work, not only the model.
Approval Gates
Require human control before sending, deleting, publishing, committing, or changing sensitive records.
Guardrail engine
Constrain frontier-model execution with workflow policy, permitted tools, validation, and action checks.
Least-privilege access
Expose only the accounts, records, and actions necessary for the assigned workflow.
Model independence
Apply a consistent governance layer across commercial, private, and local models.
Run Ledger
Record inputs, tool calls, returned data, errors, retries, approvals, changes, and outcomes.
Controlled escalation
Surface ambiguity and missing authority instead of allowing the agent to improvise beyond its mandate.
Example workflow
A real event, handled inside a defined boundary.
An agent prepares a response to a delayed supplier delivery.
- 1Verify the requesting workflow and agent mandate
- 2Read the permitted ERP and CRM records
- 3Prepare the operational response
- 4Stop before changing the customer commitment
- 5Record the approver and authorized execution
Outcome
The routine coordination is automated, the consequential decision remains human, and every step can be reviewed afterward.
How success is measured: Governance is demonstrated through the actual run: what the agent could access, what it attempted, where it stopped, who approved, and what changed.
Questions teams ask before they deploy
What is AI agent governance?+
AI agent governance is the set of identity, authority, permission, policy, approval, monitoring, and evidence controls that determine how an agent may act in a real operating environment.
Do guardrails replace human approval?+
No. Guardrails constrain execution and detect defined risks. Approval gates keep consequential business decisions with accountable people when policy requires them.
Can the same controls apply to different AI models?+
Yes. Dialogo’s execution and governance layer is model-independent, so workflow permissions, approvals, and evidence do not depend on one model provider.
Start with one workflow
Define the outcome, controls, and proof before you scale.
20 minutes · one operational process · no obligation