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Illustrative, anonymized engagements · Results vary by scope
2026 priorities

The agent programs enterprise leaders are funding right now.

Six agent use cases reflecting where 2026 budgets are moving: agents that take action in systems of record, supervision of an AI workforce, and governance you can put in front of compliance.

A financial services operations team reviewing account servicing and dispute cases at their workstations.
Customer-Facing AI AgentsFinancial ServicesAutonomousAmazon ConnectAmazon Lex

Action-taking voice agent for billing and payments

Payments, disputes in progress and balance actions complete in the voice channel, with clean escalation when policy or risk rules trip.

Workflow
  1. Intake
  2. Act in Core banking
  3. Update Payments
  4. Escalate exceptions
  5. Measured

Transactions completed in channel · 15–25 pt containment lift over answer-only bots

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A team lead coaching an agent on a BPO contact center floor while reviewing a quality dashboard.
Customer-Facing AI AgentsBPOSupervisedGenesys CloudNICE CXone

Control plane for a hybrid human and AI agent workforce

Operations manages AI capacity the way it manages headcount, with per-client reporting clients can audit.

Workflow
  1. Intake
  2. Act in CCaaS routing
  3. Approval gate
  4. Update WFM
  5. Measured

Single capacity view across human and AI agents · cost per contact reported by agent type within 1 quarter

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A health payer operations team reviewing claims and member eligibility on screen together.
Customer-Facing AI AgentsHealth PayersSupervisedFive9NICE CXone

Quality management agent that scores every interaction and routes on it

Sensitive member calls land with agents who score well on that behaviour, and coaching lands while the gap is fresh. Disputed scores and routing changes require a quality-leader review.

Workflow
  1. Intake
  2. Act in Quality management
  3. Approval gate
  4. Update CCaaS routing
  5. Measured

100% scored interactions · quality signals applied to routing within the shift · compliance risk reduced

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Insurance claims handlers reviewing first-notice-of-loss photos and claim files while one adjuster takes a call.
Process & Operations AI AgentsInsuranceSupervisedAWS BedrockKore.ai

RPA replacement agents that call APIs instead of scraping screens

Automations survive vendor releases, and the exception queue becomes the only thing operations watches.

Workflow
  1. Intake
  2. Act in Policy and claims APIs
  3. Approval gate
  4. Update Workflow orchestration
  5. Measured

40–60% lower automation maintenance cost per process · 50%+ fewer break-fix incidents per release

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A nurse coordinator and a patient-access specialist working scheduling and prior authorization queues at a hospital operations desk.
Process & Operations AI AgentsHealthcare ProvidersSupervisedAWS BedrockAzure OpenAI

Audit-grade agent observability and tracing

Agents clear compliance review because each action can be reconstructed, and regressions surface before they reach patients or payers.

Workflow
  1. Intake
  2. Act in Agent runtime
  3. Approval gate
  4. Update Trace store
  5. Measured

100% of cases reconstructable step by step · drift detected before member impact · 30% shorter compliance approval cycle

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A financial services operations team reviewing account servicing and dispute cases at their workstations.
Employee-Facing AI AgentsFinancial ServicesSupervisedSalesforce AgentforceCopilot Studio

Converged IT and HR employee service agent

Employees ask once, and joint journeys such as onboarding or role change complete without manual hand-offs. Pay, leave and privileged-access actions run behind manager or HR approval.

Workflow
  1. Intake
  2. Act in ITSM
  3. Approval gate
  4. Update HRIS
  5. Measured

Single intake for 100% of internal requests · 40–60% fewer cross-team reassignments · 20–30% lower cost per internal case

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Agent pattern: Assistive — drafts and recommends, a person sends. Supervised — acts in your systems behind approval gates. Autonomous — completes the work end to end within policy and escalates exceptions.

6 of 86 agent use cases ·

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A financial services operations team reviewing account servicing and dispute cases at their workstations.
AI Agent FoundationsFinancial ServicesAssistive

Knowledge foundation for wealth advisory agents

Problem. Advisory agents stall in pilot because supervision cannot sign off on answers that carry no citation or audit trail.

Agent design. Governed retrieval platform with content connectors, permission-aware indexing, an evaluation harness and citation enforcement — the grounding layer every advisory agent calls.

Outcome. Advisers get cited, supervision-ready answers inside the workstation, so advisory agents clear review and reach production.

Acts in. Research library · Product and policy content · Advisor workstation

Workflow
  1. Intake
  2. Draft in Research library
  3. Human sends
  4. Measured
Cited answers in under 10 seconds · 90%+ of responses carry an approved source
Works with
Azure OpenAI
A nurse coordinator and a patient-access specialist working scheduling and prior authorization queues at a hospital operations desk.
AI Agent FoundationsHealthcare ProvidersAssistive

Clinical knowledge foundation for patient-facing agents

Problem. Clinical, policy and coding content is scattered across intranets, PDFs and legacy systems, so patient-facing agents cannot be trusted with an answer.

Agent design. Unified semantic index with PHI-aware access controls, freshness signals and evaluation guardrails, exposed as a retrieval tool to clinical and operational agents.

Outcome. Every clinical and operational agent answers from one governed source, so care teams stop verifying answers by hand.

Acts in. Intranet and document stores · Legacy knowledge bases · Access control

Workflow
  1. Intake
  2. Draft in Intranet and document stores
  3. Human sends
  4. Measured
20–35% higher answer accuracy vs. keyword search · one governed source behind every agent
Works with
AWS Bedrock
Insurance claims handlers reviewing first-notice-of-loss photos and claim files while one adjuster takes a call.
AI Agent FoundationsInsuranceAssistive

Policy knowledge foundation for underwriting agents

Problem. Form and state-variation errors carry direct loss cost, and no agent can be trusted with issuance until retrieval is form-aware.

Agent design. Retrieval over the policy corpus with form-aware chunking, state and line-of-business filters and a regression evaluation set run on every content update.

Outcome. Underwriting agents cite the right form and state variation inside the workflow. Underwriters review cited passages before an issuance decision is made.

Acts in. Policy forms library · State filings · Underwriting workbench

Workflow
  1. Intake
  2. Draft in Policy forms library
  3. Human sends
  4. Measured
30–40% faster issuance · 50%+ fewer form and endorsement errors
Works with
Azure OpenAI
A retail customer care and fulfilment team reviewing order tracking and returns on screen.
AI Agent FoundationsRetail & EcommerceAutonomous

Product knowledge foundation for commerce agents

Problem. Keyword retrieval misses intent on long-tail demand, capping conversion for both site search and any shopping agent built on it.

Agent design. Semantic index over the catalog with LLM re-ranking, merchandising rule injection and an evaluation loop tuned on conversion signal.

Outcome. Shopping agents and site search resolve intent rather than matching strings. Merchandising rules cap what the agent can promote, and pricing exceptions escalate to a buyer.

Acts in. Product catalog · Merchandising rules · Search and ranking

Workflow
  1. Intake
  2. Act in Product catalog
  3. Update Merchandising rules
  4. Escalate exceptions
  5. Measured
3–8% search-driven conversion lift · 10–20% revenue recovered on long-tail queries
Works with
AWS Bedrock
A health payer operations team reviewing claims and member eligibility on screen together.
AI Agent FoundationsHealth PayersSupervised

Agent governance program with evaluation gates and audit trail

Problem. Each new agent negotiates its own controls with risk and compliance, so approved agents sit months behind the business case.

Agent design. Central evaluation harness, agent and model registry, red-teaming, PHI-aware guardrails and release gates that every team ships through.

Outcome. Agents reach production on a standard, defensible control baseline instead of a bespoke approval each time.

Acts in. Model registry · Evaluation harness · Risk and compliance

Workflow
  1. Intake
  2. Act in Model registry
  3. Approval gate
  4. Update Evaluation harness
  5. Measured
100% of deployed agents pass a standard evaluation gate · 30–50% shorter path from pilot to production
Works with
Azure OpenAI
A team lead coaching an agent on a BPO contact center floor while reviewing a quality dashboard.
AI Agent FoundationsBPOAssistive

Multi-client knowledge foundation for BPO agents

Problem. Multi-client programs run on inconsistent knowledge bases, which blocks agent reuse and drags first-contact resolution.

Agent design. Governed multi-tenant retrieval with strict per-client isolation, shared quality tooling and surfacing inside the agent desktop.

Outcome. New client programs inherit a proven knowledge layer instead of building one. Client-specific content changes pass program-owner approval before agents use them.

Acts in. Per-client knowledge bases · Agent desktop · Tenant isolation

Workflow
  1. Intake
  2. Draft in Per-client knowledge bases
  3. Human sends
  4. Measured
5–10 point first-contact resolution lift across programs · 30–50% faster program launch
Works with
AWS BedrockNICE CXone
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