Production AI agent programs for enterprise leaders

Problem. Sampling 1–3% of interactions leaves client risk unmeasured and makes quality claims impossible to defend in a QBR.

Agent design. Scoring agent evaluates every voice and digital interaction against client-specific scorecards, with calibration sets, confidence thresholds and a dispute workflow the client can audit.

Outcome. Defensible per-client quality reporting on full volume, and the scoring foundation quality-aware routing builds on. Low-confidence scores route to a human reviewer, and clients can dispute any score.

Acts in. Interaction recording · QA scorecards · Coaching workflow

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. 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

Problem. Labor-arbitrage back-office contracts run on thin margin, and headcount-based pricing has no more room in it.

Agent design. Pod model combining document agents, process agents and human-in-the-loop review under per-client SLAs, with volume and quality reported to the client.

Outcome. Programs deliver on SLA at a lower unit cost, so margin comes from automation rather than wage arbitrage.

Acts in. Client systems of record · Workflow / case management · SLA reporting

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. Onboarding spans HRIS, IAM, procurement, facilities and training, so day-one readiness slips and paid ramp time is wasted.

Agent design. Agentic workflow provisions accounts and equipment, sequences training, nudges managers and reports exceptions on a single readiness view.

Outcome. New hires start productive on day one, and coordinators stop tracking checklists by email. HR and hiring-manager approvals gate every provisioning and access step.

Acts in. HRIS · IAM · Procurement and facilities

Problem. Slow ramp and sampled coaching drive attrition and inconsistent quality, both of which hit program margin directly.

Agent design. Assist and automated quality signals feed a coaching agent that serves in-the-moment guidance during live handling and gives supervisors ranked coaching themes per person.

Outcome. Faster ramp and coaching grounded in every interaction rather than a sample. Team leaders review each coaching plan before it is delivered.

Acts in. CCaaS · Quality management · Learning content

Problem. AI agents are deployed program by program with no shared view of what they handle, what they cost or when they must hand back to humans.

Agent design. A control layer registers every AI agent, sets routing and containment policy per client program, monitors live performance and enforces handback thresholds alongside human routing.

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

Acts in. CCaaS routing · WFM · Client reporting

Problem. Years of interaction and case history sit unused while each new client program starts agent design from a blank page.

Agent design. Mine historical interactions and case notes to rank automatable intents, generate candidate flows and evaluation sets, then promote only what passes offline scoring.

Outcome. New client programs launch agents from evidence in their own data instead of workshop guesswork. Program owners approve every intent before an agent ships to production.

Acts in. Interaction archive · Case history · Evaluation harness

Problem. Back-office processors work queues with uneven quality, and coaching depends on small manual samples reviewed weeks later.

Agent design. Agent reviews completed work items against process rules, gives processors in-the-moment guidance on the next case and gives team leads ranked coaching themes per person.

Outcome. Quality improves during the shift rather than in retrospective review, and coaching targets real error patterns.

Acts in. Case / workflow management · Process rules · Quality management