High-Level Design for a Conversational AI Evaluation Framework (Production Implementation)

TL;DR: Treat evaluation like a product. Build a versioned, reproducible pipeline that scores conversational agents on three layers—Task Outcomes, Conversation Quality, and System Reliability—with dataset governance, evidence-aware judges, slice dashboards, and release gates wired into CI/CD and canary rollout.

1) Architecture at a Glance

 1            ┌─────────────────────────────────────────────────────────────────┐
 2            │                         Data Sources                             │
 3            │  • Shadow/Prod traffic  • Synthetic suites  • Human audits      │
 4            └──────────────┬──────────────────────┬───────────────────────────┘
 5                           │                      │
 6                     (A) Ingest/Log         (B) Offline Suites
 7                           │                      │
 8┌──────────────────────────▼──────────────────────▼───────────────────────────┐
 9│                           DATA MANAGEMENT LAYER                             │
10│  Event Store (immutable)  •  KB Snapshot Store  •  Test Case Registry       │
11│  PII Redaction/Tokenization • Schema Registry • Dataset Versioning          │
12└───────────────┬─────────────────────┬───────────────────────────┬──────────┘
13                │                     │                           │
14         ┌──────▼───────┐       ┌─────▼─────┐               ┌─────▼─────┐
15         │ Metrics Calc │       │ LLM Judges│               │ Rule Check │
16         │  (batch/rt)  │       │  (evidence)              │  (hard req)│
17         └──────┬───────┘       └─────┬─────┘               └─────┬─────┘
18                │                     │                           │
19                └──────────┬──────────┴──────────┬────────────────┘
20                           │                     │
21                   ┌───────▼────────┐    ┌──────▼─────────┐
22                   │ Hybrid Scoring │    │ Slice Analyzer │
23                   │  (weights+CI)  │    │ (intent/persona│
24                   └───────┬────────┘    │  /channel/age) │
25                           │             └──────┬─────────┘
26                     ┌─────▼─────┐             │
27                     │ Leaderboard│             │
28                     └─────┬─────┘             │
29                           │                    │
30      ┌────────────────────▼────────────────────▼────────────────────┐
31      │             DECISION & DELIVERY LAYER (Gates)                │
32      │  • CI gates  • Canary policy  • Rollback  • Weekly reports   │
33      └──────────────────────────────────────────────────────────────┘
34

2) Data Model (make it boring, make it last)

2.1 Interaction Event (immutable, PII-safe)
A single turn or tool call with strict schemas.

 1{
 2  "event_id": "uuid",
 3  "ts": "2025-08-31T14:07:01Z",
 4  "session_id": "hash",
 5  "user_id": "pseudonymized",
 6  "channel": "web|whatsapp|voice",
 7  "turn": 6,
 8  "role": "user|agent|tool",
 9  "payload": { "text": "...", "tool": "orders.get", "args": {...}, "result": {...} },
10  "latency_ms": 412,
11  "tokens_in": 612,
12  "tokens_out": 184,
13  "policy_flags": ["redacted_pii"],
14  "trace_id": "otel-trace",
15  "model_version": "gpt-x.y",
16  "kb_snapshot_id": "kb-2025-08-31"
17}
18

2.2 Test Case (task-centric, evidence-bound)
Ensures acceptance rules and evidence exist per case.

 1{
 2  "case_id": "returns-01",
 3  "goal": "Start a return for order #A123",
 4  "persona": {"tier":"gold","locale":"en-IN"},
 5  "constraints": {"policy_days": 7},
 6  "required_tools": ["orders.get","returns.create"],
 7  "evidence": [{"id":"kb-returns-v3","url":"kb://..."}],
 8  "acceptance": {
 9    "must_include": ["return_id","timeline","policy_excerpt"],
10    "must_cite": true,
11    "max_words": 180
12  },
13  "slices": ["intent:returns","tier:gold","locale:en-IN"]
14}
15

2.3 Judgment (rule+judge hybrid)

 1{
 2  "run_id": "eval-2025-09-01",
 3  "case_id": "returns-01",
 4  "rules": {"pass": true, "violations": []},
 5  "judge_scores": {"grounded": 2, "success": 2, "safety": 1, "format": 1},
 6  "citations": ["kb-returns-v3"],
 7  "latency_ms": 5210,
 8  "cost_cents": 0.9,
 9  "tool_metrics": {"schema_err": 0, "retries": 1},
10  "version": {"model":"gpt-x.y","orchestrator":"1.6.2","kb":"kb-2025-08-31"}
11}
12

3) Metric Suite (mapped to what the business cares about)

Layer 1 — Task Outcomes & Safety

• TSR (Task Success Rate) — acceptance rules satisfied.

• GF (Grounded Factuality) — evidence-aligned claims.

• SVR (Safety Violation Rate) — PII, policy, tool misuse (must be 0).

• P50/P95 Latency, Cost/Task, Escalation Rate.

Layer 2 — Conversation Quality & Control

• DME (Dialogue Management Effectiveness) — intent/slots, turn discipline, constraint adherence.

• CR (Context Retention) — slot-wise precision/recall across turns/sessions.

• CE (Clarification Efficiency) — (# clarifications)/(total turns) and usefulness.

• PEE (Plan Execution Efficiency) — actual vs optimal steps; Dead-end Rate.

• TUA (Tool Use Accuracy) — schema errors, precondition failures, idempotency.

Layer 3 — System Reliability & Integration

• R@K/MRR (retrieval), Memory Integrity (staleness/contamination),

• SIH (System Integration Health): edge error rates, circuit-breaker triggers, partial recovery,

• SLO Adherence, Drift/Regression Index vs prior release.

Release Gates (example):

• TSR ≥ 0.85, GF ≥ 0.88, SVR = 0, P95 ≤ 6s, Cost/Task ≤ ₹X

• No top-5 intent slice below 0.75 TSR

• TUA schema error ≤ 1%, Dead-end ≤ 3%

4) Scoring Pipeline

1. Rule Checks: enforce acceptance (citations present, JSON valid, ≤ max words).

2. Evidence-Aware Judges (LLM-as-Judge ensemble):

   • Provide only whitelisted snippets; require quoted evidence IDs in rationale.

   • Majority vote across 2–3 diverse judges; calibrate monthly vs human gold.

3. Hybrid Scoring: final = α·rules + β·judge + γ·system_signals (weights with confidence bands).

4. Slice Analysis: intent/persona/locale/channel/knowledge-freshness.

5. Leaderboard: compare run_id vs previous; compute Regression Index.

5) Online + Offline: one system, two tempos

• Offline (pre-prod): full suite against frozen KB snapshot, deterministic seeds, model+orchestrator+tools pinned.

• Online (prod):

  • Shadow: new version answers silently on mirrored traffic; log deltas.

  • Canary: 1–5% traffic; auto-rollback on gate breach.

  • Continuous signals: TSR proxy, P95, Cost, policy blocks, tool error taxonomy.

6) Observability & Governance (non-negotiable)

• OpenTelemetry traces around every LLM/tool call; correlation IDs per session.

• Structured logs (JSON) with token/latency/cost counters; no raw PII.

• Redaction/tokenization at ingest; reversible only under privileged workflows.

• Model/KB registry: every run binds to {model, prompt pack, orchestrator, tool versions, kb_snapshot}.

• Reproducibility: “click-to-replay” any regression with the exact artifacts.

7) Implementation Plan (8–10 weeks)

Week 1–2 — Foundations

• Event schemas + redaction; versioned KB snapshots.

• Seed 30–50 task-centric cases per top 5 intents; define acceptance rules.

Week 3–4 — Scoring

• Rule engine; evidence-aware judge prompts + 2-judge ensemble;

• First leaderboard + slice views; CI gate for TSR/SVR.

Week 5–6 — System Metrics

• Tool contract validation (pydantic), schema error counters, retries with jitter;

• Retrieval diagnostics (R@K) and chunking audits; memory integrity checks.

Week 7–8 — Online Wiring

• Shadow + canary playbooks; auto-rollback; weekly eval report to owners;

• Drift monitor (model, KB, traffic mix).

Week 9–10 — Hardening

• Human calibration panel; adversarial pack (injunction, jailbreak, flaky tools);

• Budget/SLO alerts; documentation and runbooks.

8) Technology Choices (opinionated, swappable)

• Ingest/Log: Kafka / Kinesis; structured JSON.

• Storage: Object store (S3/GCS) for artifacts; columnar warehouse (BigQuery/Snowflake/DuckDB) for analytics.

• Processing: dbt/SQL + Python (Pandas/Polars) for batch; Flink/Spark for streams.

• Tracing/Metrics: OpenTelemetry → Tempo/Jaeger; Prometheus + Grafana.

• Eval Harness: Python service with Hydra configs; PyTest for CI integration.

• Labeling/Review: Light human tooling (Label Studio/GDocs forms) with strict PII policy.

• Access Control: IAM per environment; audit trails for artifact changes.

(Swap vendors freely; keep the interfaces stable.)

9) Example: Runner Skeleton (pseudo-Python)

 1def run_suite(agent, cases, kb_snapshot, model_pin):
 2    results = []
 3    with pinned(kb=kb_snapshot, model=model_pin):
 4        for c in cases:
 5            out, sys = agent.run(c["goal"], persona=c["persona"], constraints=c["constraints"])
 6            rules = check_rules(out, c["acceptance"])
 7            judge = judge_with_evidence(out, load_snippets(c["evidence"]))
 8            results.append(aggregate(c, out, sys, rules, judge))
 9    return leaderboard(results)
10

Wire this to CI so each PR that changes prompts/models/tools/KB re-computes the leaderboard and applies gates.

10) Mapping to Your Earlier Metrics

• DME → part of Layer-2 (intent/slot accuracy, turn budgets, constraint adherence).

• CMC → Context Retention with slot-wise precision/recall and drift audits.

• CPA → Plan Execution Efficiency (+ Dead-end Rate) with step optimality.

• CS → System Integration Health (edge error taxonomy, circuit-breakers, recovery).

You keep the spirit, but anchor it in operational signals that unblock decisions.

11) Anti-Patterns to Avoid

• Optimizing judge prompts instead of fixing agent behavior.

• Comparing runs with different KB snapshots or model pins (eval drift).

• One vanity number (global TSR) that hides slice failures.

• Unbounded loops in orchestrator or eval harness.

• Logging PII “just for debugging.”

12) What “Good” Looks Like (12-week snapshot)

• TSR 0.86, GF 0.90, SVR 0, P95 5.2s, Cost ₹X/task

• No top-5 intent slice below 0.78 TSR

• TUA schema error <0.8%, Dead-end <2%

• Canary → GA in 72h with zero rollbacks, all artifacts versioned & replayable.

Closing

An evaluation framework is not a report—it’s a control surface for your agentic system. When outcomes, behaviors, and reliability are observable, versioned, and enforceable via gates, you ship faster, safer, and cheaper. The rest is iteration.