Representative deployment patterns for regulated estates. Each one carries real compliance requirements, real audit obligations, and real consequences for failure — which is what decides how the agent has to be governed, not just what it has to do.
An agent reviews every proposed trade against MNPI policy, market-abuse heuristics, and the firm's restricted-list state. Every denial is logged with the policy that fired and the human approver, if any. The trade either crosses the boundary cleanly or never moves at all.
A clinical agent summarizes patient charts into a structured intake brief. PHI access is gated by clearance and care-team membership. Low-confidence outputs route to the attending clinician via QDL gates. No partial state ever reaches Epic.
A fleet of agents reviews pull requests for security regressions, broken contracts, and missing tests. Findings post inline; merges block until policy gates clear. Reviewers retain authority; agents augment, never override.
An agent reads each first notice of loss, pulls the policy and prior claims, flags coverage questions and possible fraud indicators, and drafts a triage recommendation for the adjuster. It can route and draft, but it can never approve or deny a claim; anything above the payment threshold goes to a named adjuster. Every recommendation traces back to the documents and the model that produced it.
A constituent-services agent triages incoming casework, routes time-sensitive items, and drafts initial responses for caseworker review. Runs fully air-gapped. No outbound model calls. EDT exports to the agency's existing records system. Citizens get faster responses; the audit trail is sovereign-controlled.
A fleet of import-pipeline agents converts a backlog of LangGraph and custom-Python KYC scripts into governed Operon agents. Each imported agent is born into the governance envelope with policies bound at compile time. The remediation backlog clears in weeks, not quarters.
A company's existing API surface, dozens of internal services, is imported into Operon and becomes a catalog of governed, agent-callable tools. AI-assisted mapping drafts each tool from the spec; policy binds at the boundary, so pre-execution checks fire before any call and every invocation lands in the audit trail. Agents reason over one catalog, the APIs stay free to call, and a quarter of integration work ships in days.
Most production AI stacks today are LangChain pipelines, ad-hoc orchestration, and un-auditable LLM workflows. They ship faster than they govern. Operon doesn't replace the model providers your teams already use, it replaces the duct tape between them.