By Sanjoy Ghosh, AI & Digital Engineering Leader
The last wave of “AI for the contact center” concentrated on assist: call summaries, auto-notes, knowledge snippets. Those are real wins when deployed with guardrails; one insurer’s direct brand reported ~3 minutes shorter calls after productionizing call summarization, freeing advisors to focus on moments that matter. At the strategic level, large operators are signaling material savings from AI in service; Microsoft publicly attributed >$500M in annualized savings to AI across functions, with a majority coming from call centers.
But two practical constraints have slowed transformation: (1) handoffs when the bot is unsure, customers fall into generic human queues; (2) governance controls live in meetings and documents, not in code that the system can obey. Agentic AI addresses both: agents execute multi-step processes and only escalate with evidence; policies and gates are enforced automatically in a control plane.
What Agentic AI Is (and Isn’t)
Agentic AI (for service) is the capability for software agents to:
- Perceive & plan: Detect intent, break a request into steps, and choose tools or sub-agents.
- Act under policy: Call tools (CRM, billing, KMS), update records, schedule callbacks only within scoped permissions and signed requests.
- Coordinate: Hand off sub-goals to specialized agents (e.g., identity verification, refund calculation) and reconcile results.
- Escalate with evidence: Route edge cases to humans with a case brief (confidence, steps attempted, artifacts, policy checks).
- Learn safely: Feed structured outcomes (labels, rationales, corrections) into evaluation sets and enterprise memory for measured improvement.
It is not just a chat UI, a single “copilot,” or a rules engine with new branding. Agentic AI requires a governed runtime(control plane) and clarity about autonomy levels:
- L0: AI suggests; human performs.
- L1: AI drafts; human approves.
- L2: AI executes low-risk steps under policy with full logging.
- L3: AI executes multi-tool workflows with mandatory gated approvals.
(Choose the lowest level that meets risk/benefit; move up only with evidence.)
Business Impact & Use-Case Map (AHT, FCR, CSAT)
1) End-to-End Case Resolution for Top Contact Reasons
Today: Bots answer FAQs; complex cases bounce between channels, agents re-collect info, and back-office tasks lag.
Agentic: A case coordinator agent plans the flow: authenticate → retrieve account context → verify policy → propose options → execute the chosen path (e.g., credit, replacement, technician dispatch) → confirm resolution escalating to a human approver only where required.
Outcomes:
- AHT ↓ (less rediscovery and swivel-chairing).
- FCR ↑ (fewer handoffs; agent completes the loop).
- CSAT ↑ (coherent journey, clearer commitments).
Leading indicators: average steps to resolution; rate of human approvals without rework; breach rate per 1k actions.
2) Regulated Journeys (Identity, Disputes, Claims)
Today: Agents manually check policy clauses, chase documents, and discover errors late.
Agentic: A compliance agent runs policy checks as code (KYC docs present? thresholds met?) and compiles a case brief; a human reviewer approves at gated steps with rationale stored for audit.
Outcomes:
- AHT ↓ (fewer loops to fix missing artifacts),
- FCR ↑ (required checks pass on first attempt),
- CSAT ↑ (predictable timelines, transparent criteria).
Leading indicators: percent of approvals with compliant artifacts; time-to-decision; audit exceptions.
3) Knowledge Replies with Confidence & Citations
Today: Agents search multiple systems; answer quality varies.
Agentic: A knowledge agent drafts responses grounded in retrieval with citations; below threshold or sensitive topics auto-route to a human reviewer. Reviewer edits are structured and feed the memory/evaluation harness.
Outcomes: AHT ↓ (less searching), FCR ↑ (consistency), CSAT ↑ (clear, evidence-backed answers).
Leading indicators: citation coverage; edit distance vs. draft; recontact within 7 days.
Metric context: AHT includes talk, hold wrap-time visible to see LLM summarization effects; cross-industry FCR is often reported near ~70% using survey methods, with wide variance by complexity; calibrate locally. [NICE, Sep 2025; SQM Group, Jul 2025]
Control plane responsibilities:
- Identity & entitlements: Least-privilege scopes per agent and per tool; deny by default.
- Policy-as-code: Approval gates (e.g., refunds >$X need human sign-off), compliance checks (PII, consent), data retention.
- Safety filters & provenance: Prompt/response logging, source citations for grounded answers, redaction at ingest.
- Signed tool calls: Every action against CRM/billing/OMS is cryptographically signed and attributed to a service identity.
- Cost metering: Record model calls, tool API time, storage, and human minutes to price the task, not the token.
- Rollout flags: Canary and rollback for prompts, retrieval configs, and tool scopes, like any reliability-sensitive service.
Required fields: intent, steps attempted, artifacts gathered, confidence bands, policy checks passed/failed, decision proposal, audit link.
Human actions: approve, revise, request evidence, override. Each action stores rationale and labels (root cause, policy clause). These structured artifacts feed the evaluation harness and memory.
What moves the pricing economics:
Most centers have a short list of contact reasons that dominate volume and valuebilling disputes, order changes, plan upgrades, warranty claims, reinstatements, account unlocks. Improving FCR on these top reasons does more for unit economics than shaving a few seconds from dozens of long-tail topics. The practical move is to pick one high-value reason, map its current path end to end, and then let an agentic workflow coordinate the steps under policy. When the customer gets a complete resolution in a single interaction on a high-volume reason, your cost curve bends in a way Finance can see.
Next, make human time compound. Early on, you’ll need human reviewers to correct drafts, approve actions, or request missing evidence. That’s fine if those interventions are captured and reused. Treat each edit as an asset: store the rationale, tag the root cause, and fold it back into prompts, retrieval configuration, and tool policies. Over a few cycles, you should see the minutes per escalation drop because the system stops repeating the same mistakes. This is how agentic systems learn in production by turning live corrections into structured patterns and reducing edit distance over time without heroics.
Govern with numbers that matter to both Risk and Finance. Publish cost-per-interaction right next to AHT, FCR, and CSAT so stakeholders can weigh quality and cost together. Add a simple breaches per 1k actions metric that counts policy violations, unsafe responses caught by filters, and gated approvals that were required but skipped. When those breach rates stay flat or fall while cost-per-interaction trends down, you’ve earned the right to scale. If breach rates rise or costs drift up you have the signal to pause, fix, and try again before expanding scope.
Keep external case studies in their place useful as context, not as a business case. It’s fine to say, “Others have seen time savings when they deployed summarization” or “Large programs reported material service savings.” But resist turning someone else’s outcome into your forecast. Your baseline, your contact mix, your approval gates, and your data quality will determine your economics. Run your own 30/60/90 evaluation, disclose the assumptions up front, and let your weekly trends not anecdotes decide whether you scale.
Two more quiet levers round out the picture. First, seasonality and channel mix: if your peak volumes cluster at known times (renewals, holidays, billing cycles), front-load agentic automation where it can absorb those spikes without hiring sprints. Second, tool scopes and case briefs: the fastest way to cut rediscovery time is to send humans a compact brief (what the agent saw, tried, and why it paused) and make sure the software agent’s tool permissions match the job no more, no less. Tight scopes reduce risk and latency; good briefs reduce handle time. Neither shows up as a flashy feature, but both move pricing economics in a durable way.
Risks, Constraints & Mitigations
Privacy & compliance drift
- Risk: Generated summaries or agent actions can surface or move sensitive data across contexts; transcript exports can leak personal data.
- Mitigations: Redaction at ingest, purpose limitation, consent checks, role-based retrieval, storage limits aligned to GDPR principles
Hallucinations & decision quality
- Risk: Ungrounded outputs degrade decisions and trust.
- Mitigations: Retrieval-grounding with citations, confidence thresholds, human approval for sensitive/low-confidence flows, golden-set evaluation with regression alerts, breach-rate KPI per 1k actions.
Cost sprawl
- Risk: Premium model tiers and unmetered API use inflate spend.
- Mitigations: Per-interaction metering; budget envelopes; scale gates (expand only when AHT/FCR/CSAT and cost curves clear thresholds); quarterly vendor reviews. [No reliable public data]
Integration complexity & latency/SLA
- Risk: Mixing on-prem systems with cloud AI introduces latency and security gaps.
- Mitigations: Zero-trust brokering for each model/tool call, mutual authN/authZ, least-privilege scopes, per-call encryption, local caches (where lawful), and SLOs monitored by the control plane.
Auditability & third-party assurance
- Risk: Inability to demonstrate controls to clients/regulators at scale.
- Mitigations: Map logs, approvals, and retention to ISO/IEC 27001 (ISMS) and SOC 2 Trust Services Criteria; generate evidence from the control plane, not spreadsheets.
Adoption & talent
- Risk: Agents and supervisors perceive agents as surveillance or added overhead.
- Mitigations: Incentivize teaching (edits that reduce rework), promote Reviewer/Approver and Pattern Curator roles, and show agent contributions on the weekly quality dashboard.
Buy vs. Build vs. Partner
| Criterion | Buy (platform/app) | Partner (co-build) | Build (in-house) |
| Time-to-Value | Fast for standard assist & summaries | Fast for first high-value lane | Slow initially |
| Customization | Limited by roadmap | High with reusable patterns | Highest (if you invest) |
| TCO | Subscription and change management | Services and run cost | Talent , infra and guardrails |
| Governance | Vendor and your policies | Joint RACI; code hooks | Full control; more effort |
| Exit Risk | Higher | Medium | Lower (if standards-based) |
| Roadmap Influence | Low–Med | Med–High | High (internal) |
Rule of thumb: Buy for commodity assist; partner to stand up the control plane, escalation contracts, and evaluation harness fast; build where domain, data, or compliance posture is your moat. Switch paths if quality plateaus for three cycles or unit economics won’t beat baseline.
Implementation Roadmap (30/60/90 and beyond)
Days 0–30 Prove the spine
- Pick one top contact reason (volume × value).
- Baseline AHT, FCR, CSAT and document your methods
- Deploy a thin slice: intent , retrieval, drafting/summarization, enterprise memory, reviewer console, policy gates for low-confidence/regulated intents.
- Stand up telemetry: weekly quality graph (edit distance vs. golden set), breaches per 1k actions, cost-per-interaction, escalation rate.
Days 31–60 Make it safer and cheaper
- Add the control plane: policy tests, tool scopes, signed calls, approvals, audit logs; turn on cost metering.
- Launch escalation contracts (triggers, assignments, actions, artifacts).
- Start skill routing (tags: product, authority, language).
- Introduce incentives for teaching: recognition/comp tied to reduced edit distance and sustained FCR lift.
Days 61–90 Gate to scale
- Replace one manual/vendor step with the agentic flow for a defined subset.
- Scale only if all pass:
- AHT down vs. baseline;
- FCR up against control;
- CSAT steady or better;
- Breaches/1k actions stable or falling;
- Cost-per-interaction within budget envelope.
- If gates fail, stop and reroute the factory; keep evaluation and control scaffolding.
Beyond 90
- Expand to adjacent contact reasons; grow autonomy cautiously (L1→L2) where telemetry justifies.
- Publish a weekly exec page with AHT, FCR, CSAT; breaches/1k; cost-per-interaction; agent contribution index.
- Formalize Pattern Curator ownership of reusable replies, workflows, and prompt patterns.
Conclusion & Call to Action
Agentic AI is not about flashier chat; it is about closing the loopsoftware agents that plan, act under policy, coordinate, and escalate with evidence. With a neutral control plane and an evaluation harness, you can improve FCR, bend AHT in the right direction, and safeguard CSAT with unit economics and audit evidence the board understands. The research and standards are available; the difference is operational discipline.