How to Build High-Performing AI Agents: Using Evals to Deliver Excellence in Self-Service

There's a pattern to AI self-service projects. The demo is dazzling. The pilot is promising. Then it hits real traffic and the cracks appear — a confidently wrong answer here, an off-policy response there, a regression nobody noticed after a prompt tweak. The bot that wowed everyone in the boardroom quietly erodes trust in production.

The difference between the bots that stay excellent and the ones that drift isn't a better model or a cleverer prompt. It's that the high performers have a systematic way of measuring quality — and they use it as a gate, not an afterthought. That mechanism is evals.

Why "Vibes" Don't Scale to Excellence

Traditional software is deterministic — the same input gives the same output, so a fixed test suite catches regressions. Generative AI isn't like that. The same question can produce six different, equally valid phrasings; temperature adds randomness; a model or prompt update can shift behaviour in ways no one intended. Checking output against a fixed expected string fails constantly, even when the agent is doing the right thing.

So most teams fall back on "vibes" — someone reads a handful of transcripts, decides it looks good, and ships. That works for a demo. It does not scale to the thousands of real, messy conversations production throws at you. A bot can be factually correct but wrong in tone, safe but useless, concise but incomplete, or helpful in a way that quietly invents unsupported details. Spot-checking won't catch that reliably, and it certainly won't catch a slow regression over time.

Evals replace opinion with measurement. They turn "I think it's good" into "it scores 94% on faithfulness across 300 real scenarios, up 2 points since last week."

What an Eval Actually Is

An eval is simply a repeatable test for an AI system: give it an input, then apply grading logic to its output to measure success. Where a unit test checks a code path, an eval checks judgement — did the agent answer correctly, ground its claims in approved content, complete the task, and stay in policy?

Every eval has three parts:

• An input or scenario — a customer question or a whole multi-turn conversation ("I want to dispute a £47 charge I don't recognise").
• The agent's output — what it said and did (including any tools it called).
• A grader — the logic that scores the output. This might be an exact/rule check, a similarity check against a reference, or an LLM acting as a judge.

Run a set of these consistently and you have the AI equivalent of a test suite — one that makes the agent's variability visible and manageable instead of a mystery.

The Eval Types That Matter for Self-Service

No single eval catches everything — each type finds a different class of bug, and you layer them. For contact centre self-service, these are the ones that earn their place:

The three ways to grade

Across all those types, graders fall into three buckets, and good eval suites use all three:

• Rule-based / deterministic — exact matches, regex, "did it call this API with an amount under the limit?" Cheap, fast, unambiguous. Use for anything with a clear right answer.
• LLM-as-a-judge — a model grades the output against a rubric (faithful? on-policy? right tone?). This is what makes evaluating open-ended language affordable at scale. It agrees with human judgement roughly 85% of the time in reported studies — useful, but it carries known biases (position, verbosity, self-preference) you have to design around.
• Human review — sampled, not exhaustive. Humans label a slice to calibrate the LLM judge and catch the subtle domain issues automation misses. This is your source of truth.

How to Build Your Eval Set

The eval set — your collection of scenarios and expected outcomes — is the real asset. The model will change; this is what protects your quality through every change. Build it like this:

1. Start from real intents and real failures

Don't invent scenarios in a vacuum. Pull them from your actual call drivers and transcripts: the top intents, the awkward edge cases, and especially the conversations where the bot (or a human) got it wrong. Every production failure should become a permanent eval so it can never silently regress again.

2. Build a "golden set" with known-good outcomes

For a core set of scenarios, agree the ideal outcome with your business and compliance stakeholders — the gold-standard answer, the correct action, the right escalation. This is what you grade against and the thing you defend "excellence" with.

3. Cover the whole spread, not just the happy path

A strong eval set deliberately includes: common happy-path intents, messy/ambiguous phrasing, multi-intent requests, out-of-scope asks, adversarial inputs, and vulnerable-customer situations. If it's a risk in production, it belongs in the eval set.

4. Example: an eval entry

# One eval scenario for a banking disputes agent
scenario_id: dispute-unrecognised-charge-001
input: "There's a £47 charge from TXN-DIGITAL I don't recognise."
context: { customer_verified: true, txn_exists: true, amount: 47.00 }
expected:
  task_completed: "dispute_logged"
  tools_called: [get_recent_transactions, flag_dispute, provisional_refund]
  must_include: ["case reference", "provisional refund"]
  must_not: ["full card number", "guaranteed refund"]
graders:
  - type: rule        # tool sequence + forbidden strings
  - type: llm_judge   # faithfulness, tone, policy adherence

Notice it checks behaviour and boundaries, not an exact wording — because there's no single correct phrasing, only a correct outcome.

5. Keep it alive

An eval set is never finished. Every new feature adds scenarios; every production incident adds a regression test; every policy change updates the expected outcomes. Version it alongside your prompts and flows so you can see exactly what "good" meant at any point in time.

Where Evals Run: Dev, CI, and Production

Evals aren't a one-time pre-launch check. They deliver excellence precisely because they run continuously, at three points in the lifecycle:

  ┌───────────────────────────────────────────────────────────────┐
  │  1. DEV LOOP   Change a prompt / flow / tool → run evals locally │
  │     → see the score move before you commit                      │
  └───────────────────────────────┬───────────────────────────────┘
                                  ▼
  ┌───────────────────────────────────────────────────────────────┐
  │  2. CI GATE   On every PR → run the full eval set               │
  │     → block the merge if scores regress below threshold         │
  └───────────────────────────────┬───────────────────────────────┘
                                  ▼
  ┌───────────────────────────────────────────────────────────────┐
  │  3. PRODUCTION   Sample live conversations → grade continuously │
  │     → catch drift, feed new failures back into the eval set     │
  └───────────────────────────────────────────────────────────────┘
                                  │
                                  └──────────▶ back to step 1 (improve)

• Dev loop — the fast feedback that lets a designer tweak a prompt and immediately see whether faithfulness went up or down. This is what turns prompt engineering from guesswork into engineering.
• CI gate — the quality bar made automatic. Wire your eval set into the pipeline so no change reaches production if it drops task completion, faithfulness, or policy adherence below an agreed threshold. This is the single most powerful control for sustained excellence — it makes regressions impossible to ship by accident.
• Production monitoring — because offline evals can't anticipate everything, sample real conversations and grade them live (an LLM judge makes this affordable at volume). Drift, new edge cases, and model-update regressions surface here — and every new failure becomes a fresh eval.

That closed loop — change, gate, monitor, improve — is exactly the self-service flywheel, with evals as the measurement engine that powers it. It also pairs directly with the AI agent metrics you track on your observability dashboards: the metrics tell you what moved; the evals tell you whether a change made it better or worse.

Applying This to ACXD and AI Agents

None of this is theoretical for Amazon Connect. The platform is building the hooks you need:

• Amazon Connect ACXD packages flows, guardrails, and knowledge bases into immutable, versioned builds with a TestGuardrail operation and build diffs. That means you can wire an eval stage into an ACXD CI/CD pipeline: build in a non-prod workspace, run your eval set against it, and only promote the build if it clears the bar.
• Amazon Connect AI Agents and Lex-based bots can be driven through test conversations and scored the same way — task completion, faithfulness against the knowledge base, and policy adherence.
• Any AI self-service project — regardless of platform — follows the identical pattern: a golden eval set, layered graders, and the three-point lifecycle. The tooling differs; the discipline doesn't.

The through-line is that evals give you a defensible definition of "good" that survives model swaps, prompt changes, and vendor updates. When your foundation model is updated under you — which will happen — your eval set is what tells you in minutes whether quality held or slipped, rather than finding out from an angry customer.

Where to Start

You don't need a platform or a big budget to begin — you need the discipline. Start small and compound:

1. Write 20 scenarios from your top intents and your worst known failures, with the ideal outcome for each. That's your first golden set.
2. Grade with a mix — rules for the clear-cut checks (forbidden strings, tool sequence), an LLM judge for faithfulness, tone, and policy.
3. Calibrate the judge against a handful of human-labelled examples so you trust its scores.
4. Run them on every change — even manually at first — and record the score. Make "did the eval score go up?" the question before anything ships.
5. Automate the gate once it's proving its worth, and add production sampling to close the loop.

Do that, and you move from launching a bot and hoping, to running a bot you can prove is excellent — and keep excellent.

Designing the self-service journey these evals will test? Map the intents, tools, and guardrail checks first with the free IVR Design Tool — its generated test plan is a natural starting point for your eval scenarios. And if you're defining what "excellent" means for your project, pair this with defining success criteria by role.

Note: the LLM-as-a-judge accuracy figure (~85% agreement with humans) reflects commonly reported industry findings and varies by task, model, and rubric — treat it as directional, and calibrate against your own human-labelled data. Content on industry practice was summarised and rephrased from publicly available sources.