Use cases / Finance and operations
AI Operations
Turn AI into operational execution, inside defined limits.
01
Problem
Operations teams spend time on routine exceptions, status chasing and hand-offs between systems.
02
AI capability
Monitor process signals, diagnose exceptions, update systems of record and notify owners.
03
Data
The agent works from the context you connect, not from the open internet by default.
- Operational systems and queues
- Runbooks and SLAs
- Asset and inventory data
- Incident history
04
Agent
Operations Agent
Handles routine exceptions and escalates the rest with context.
05
Workflow
- 1. Signal detected
- 2. Diagnose
- 3. Match runbook
- 4. Act within limits
- 5. Notify owner
- 6. Escalate if outside limits
- 7. Log outcome
06
Governance
Governance runs inside the agent at runtime: policy changes what it can actually do.
Can
- Read operational systems
- Run approved runbook steps
- Update tickets and statuses
- Notify owners
Cannot
- Change production configuration outside runbooks
- Cancel customer orders
- Bypass change windows
- Grant itself access
Requires approval
- Steps outside a runbook
- Actions affecting customers
- Changes during a freeze
Records
Agent identity, Data accessed, Model used, Output, Tools called, Policy applied, Decision, Approval, Action, Outcome.
Suggested starting autonomy: L3 Supervise
L3 fits runbook steps with clear limits and monitoring; new runbooks begin at L2.
07
Outcome
What you measure, so the agent earns more autonomy on evidence:
- Exception handling time
- Share of exceptions resolved without escalation
- Runbook adherence
- Actions with complete records
Questions
What stops it acting outside its remit?
Allowed and restricted actions are enforced at runtime by policy, not left to the prompt.
Can it learn new procedures?
New runbooks are added by people and start at a lower autonomy level.
Build it on the platform
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