Production AI agent programs for enterprise leaders

Problem. Prior auth is payer-specific, multi-step work that consumes clinical time and delays both care and reimbursement.

Agent design. Multi-agent system: an intake agent assembles the clinical packet, a payer-rules agent applies the right criteria, a submission agent files it, and a follow-up agent tracks status — clinicians approve before submission.

Outcome. Submission and chasing run autonomously; clinical staff review exceptions only.

Acts in. EHR · Payer portals · Utilization management

Problem. Periodic refresh across a large customer book is a fixed regulatory cost that scales only by adding analysts.

Agent design. Agents pull entity and ownership data, screen adverse media and sanctions, reconcile discrepancies and produce a refresh packet with a reasoning trace for analyst approval.

Outcome. Analysts approve evidence-backed packets rather than assembling them, and refresh cycles stay inside regulatory windows.

Acts in. CRM · KYC / AML platform · Screening providers

Problem. SMB submissions are too low-margin to staff properly, so carriers decline business or lose it to slower turnaround.

Agent design. Underwriting agent extracts submission data, runs appetite and eligibility checks, prices tiers against the rating engine and drafts the quote for underwriter approval.

Outcome. Straight-through submissions are quoted same day with the underwriter retaining the bind decision.

Acts in. Policy admin · Rating engine · Submission inbox

Problem. Markdown and replenishment decisions across thousands of SKUs are analyst-bound, so margin leaks on the long tail nobody has time to review.

Agent design. Agents monitor sell-through and inventory position, propose markdown and replenishment actions with rationale, and execute in the planning system once the buyer approves.

Outcome. Every SKU gets a decision, not just the top sellers, with buyers holding the approval.

Acts in. Merchandising / planning · Inventory · Pricing

Problem. Team leader time — the scarcest supervisory capacity in a BPO — is consumed by scheduling, adherence and shrinkage triage.

Agent design. Agent reads WFM, CCaaS and attendance data, proposes coverage actions against service targets and drafts the team communications for the leader to send.

Outcome. Team leaders manage exceptions and coaching instead of routine reallocation. Team leaders approve every reallocation before it takes effect.

Acts in. WFM · CCaaS · HR / attendance

Problem. Regulated appeal SLAs are missed because reviewers spend their time assembling case history and citing policy rather than deciding.

Agent design. Agent builds the case timeline from claims and correspondence, cites the governing policy and drafts the decision letter to template for reviewer approval.

Outcome. Reviewers edit and approve, and regulatory turnaround holds under volume spikes.

Acts in. Appeals case system · Claims platform · Policy library

Problem. Idle block time is among the most expensive unused capacity in a health system, and manual rebooking cannot keep up with cancellations.

Agent design. Agent forecasts utilization, proposes block releases and fill lists, predicts likely bumps and executes rebooking once the scheduler approves.

Outcome. Higher utilization of high-cost capacity with less manual scheduling overhead.

Acts in. EHR scheduling · Block management · Capacity analytics

Problem. Service-desk capacity is consumed by password, access and device requests while employee downtime accumulates across the business.

Agent design. Kore.ai employee agent authenticates the requester, executes resets, access grants and device actions across IAM, ITSM and MDM, with approval gates on privileged changes.

Outcome. Common IT requests are completed in chat, and analysts work complex incidents.

Acts in. IAM · ITSM · Device management

Problem. Repetitive policy, benefits and onboarding questions absorb HR capacity and slow new-hire time to productivity.

Agent design. Kore.ai agent answers from approved HR policy, executes onboarding checklist steps in HRIS and escalates sensitive matters to an HR business partner with context attached.

Outcome. New hires ramp faster and HR specialists handle the cases that need judgement.

Acts in. HRIS · Benefits platform · Onboarding workflow

Problem. Procurement requests and status chasing run through fragmented email and ticketing, extending cycle time on every purchase.

Agent design. Kore.ai process agent captures structured intake, checks PO status in ERP, routes approvals against spend thresholds and pushes vendor updates back conversationally.

Outcome. Requests complete without stakeholders chasing teams across systems.

Acts in. ERP / PO · Approval workflow · Vendor master

Problem. Multi-form intake with document uploads and coverage checks produces incomplete submissions and avoidable rework.

Agent design. Kore.ai agent orchestrates conversational intake, collects and validates documents, verifies coverage in policy admin and assembles the adjuster-ready package.

Outcome. Complete, validated claims reach adjusters without back-and-forth. Incomplete or high-severity submissions escalate to an adjuster with the gaps listed.

Acts in. Claims system · Policy admin · Document capture

Problem. Authorization delays hold up care and reimbursement, and payer-specific rules make the work impossible to standardize manually.

Agent design. Kore.ai agent assembles submissions, applies payer rules, attaches required clinical documentation and tracks status, escalating exceptions to a clinical reviewer.

Outcome. Authorizations submit faster and fewer are denied for missing information.

Acts in. EHR · Payer portals · Clinical documentation