Agent Evaluation & QA
Scores every AI interaction and blocks regressions before they reach production.
The problem. Most enterprises cannot answer a simple board question: is the AI getting better or worse? Without evaluation, every prompt change is a gamble taken in production.
The agent maintains graded evaluation sets from real traffic, scores every production interaction, and reports quality by intent, cohort and release.
It gates deployment: a prompt, model or tool change that regresses the evaluation set does not ship.
Drift, refusal spikes and grounding failures raise alerts with example transcripts attached.
Quality is judged by spot checks and complaints, and nobody can prove whether last week's change helped.
Quality is a tracked metric with a release gate, and regressions are caught before customers see them.
Where this agent sits in the stack.
From sampled manual review to scored coverage of every interaction, with calibration and appeal built in.
- 01
Quality & coaching workspace
Scorecards, coaching queues, calibration sessions and agent-visible feedback with dispute handling.
- 02
Scoring & evaluation
LLM scoring against your rubric, confidence thresholds, sampling for human calibration and drift detection per model version.
- 03
Transcription & redaction
Speech-to-text across languages and channels, with PII/PCI redaction before evaluation and storage.
- 04
Interaction capture
Recordings, transcripts, screen and metadata drawn from the CCaaS platform and recording estate.
- 05
CCaaS, WFM & systems of record
Quality outcomes flowing into WFM, performance management and reporting.
Integration surface
- Agent runtime and prompt/version registry
- CI/CD pipeline
- Interaction and transcript analytics
- Human QA tooling for calibration
Guardrails & human oversight
- Human calibration sets the standard the automated scorer is measured against.
- Release gates are enforced in the pipeline, not by convention.
- Every action outside policy stops at a reviewer queue with the agent's reasoning, evidence and proposed change attached.
- Evaluation sets are refreshed from real traffic on a schedule so they do not go stale.
What has to be true first
- Access to production transcripts or traces.
- A human QA baseline to calibrate against.
- A CI/CD pipeline the gate can hook into.
Security, data & compliance
- Runs under a dedicated service identity with least-privilege, per-tool scopes — never a shared admin account.
- Customer and employee data stays inside your tenancy and region; no training on your data by default.
- PII is redacted before it reaches a model, and prompts, responses and tool calls are retained under your retention policy.
- Every tool call, input, decision and system write is logged and replayable for audit and model-risk review.
How this agent reaches production.
Weeks 1–2 · Scope
Quality dimensions defined, calibration baseline, evaluation-set design.
Weeks 3–6 · Build
Scoring, dashboards, drift alerting and the CI release gate.
Weeks 7–10 · Production pilot
One agent under full evaluation with weekly calibration.
Quarter 2+ · Scale & run
All production agents under one quality regime with monthly reporting.
What we agree to be measured on.
Ranges drawn from comparable production engagements. Your baseline is agreed before build starts, and the same numbers are reported after go-live.
| Metric | Expected range |
|---|---|
| Production interactions scored | 100% |
| Regressions caught before release | Most quality regressions blocked in CI |
| Agreement with calibrated human QA | 90%+ |
| Time to detect a quality drift event | Weeks to hours |
Model the business case: Agentic AI ROI calculator →
One production agent under full evaluation with a working release gate and calibration cadence.
Fixed-price scope · milestone billing · price on request.
Portfolio-wide quality regime across every agent, reported monthly to the AI governance forum.
Retained pod · quarterly outcome review · price on request.
The full Agent Evaluation & QA specification, as a PDF.
A multi-page specification your architecture, security and procurement reviewers can read without a call: what the agent does, the architecture, the integration surface, autonomy and guardrails, security posture, rollout plan, measurement plan and engagement shape.
- Process before and after, with the decision that stays with a human
- Layered architecture diagram and named integration surface
- Guardrails, approval gates, escalation and audit trail
- Security, data handling and compliance posture
- Phase-by-phase rollout and the measurement plan
Building your first production-grade agentic workflow
A field-tested blueprint for shipping your first agentic AI workflow into production — the same one we use with Fortune 500 clients. Covers intent boundaries, tool design, memory, guardrails, human-in-the-loop patterns and evaluation harnesses so your first agent survives real users, real data and real audits.
Read the playbook →- Quality management agent that scores every interaction and routes on it
100% scored interactions · quality signals applied to routing within the shift · compliance risk reduced
- Quality agent scoring 100% of client interactions
100% interaction coverage · 40% faster coaching cycle · audit-ready client reporting
- Audit-grade agent observability and tracing
100% of cases reconstructable step by step · drift detected before member impact · 30% shorter compliance approval cycle
Get a written estimate for the Agent Evaluation & QA.
Tell us the process, the systems it touches and the compliance scope. We come back with a scope, a measurement plan and a written estimate — no published band that would not apply to you.
solutionAgent Evaluation & QA — routed to this team
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