Wrong or made-up answers
Agents hallucinate, misread intent, and answer confidently when they should not. Without evaluation you find out from a customer, not a test.
Guide
WhatisAIagentevaluation?
A plain-English guide to measuring whether a conversational AI agent is accurate, safe, and compliant — before and after it reaches your customers.
AI agent evaluation is the practice of measuring how well a conversational AI agent performs before and after it ships — scoring real transcripts for response quality, task completion, safety, and compliance, and stress-testing the agent against adversarial attacks. Done well, it turns “it seemed fine in testing” into a verifiable quality and safety score you can stand behind.
The stakes
WhydoesAIagentevaluationmatter?
A conversational agent acts on your behalf in front of customers. The failure modes are not just wrong answers — they are safety, compliance, and trust failures that accuracy-only checks never see.
Safety and guardrail failures
A single crafted prompt can make an agent leak data, bypass policy, or produce harmful output. These failures are invisible to accuracy-only testing.
Compliance and regulatory exposure
In regulated domains, an agent that mishandles a complaint, a vulnerable customer, or a disclosure is a reportable event — not just a bad reply.
Mishandled escalations
Knowing when to hand off to a human, and doing it cleanly, is its own skill. Agents that miss distress signals or escalate poorly erode trust fast.
What to measure
Whatshouldyoumeasure?The15dimensions
A complete evaluation looks beyond “was the answer right.” ARIA scores every conversation across 15 dimensions grouped into five areas — so a fast, polite agent that quietly breaks policy still fails.
Response Quality
5- Correctness
- Faithfulness
- Helpfulness
- Relevance
- Conciseness
Task Completion
2- Goal Success
- Task Completion Rate
Safety & Security
3- Guardrail Compliance
- Prompt Injection Resistance
- Bias & Fairness
Customer Experience
2- Tone & Empathy
- Clarity
Escalation & Vulnerability
3- Escalation Appropriateness
- Handover Quality
- Vulnerability Detection
Methodology
Howdoesscoringwork?
Most pipelines use a single model as the judge. That bakes one model’s blind spots into every score. ARIA instead submits each conversation to a panel of independent AI judges. When they agree, you get a confident score; when they disagree, the case is routed to a human reviewer who makes the final call. Judges are continually checked against expert humans, and every score comes with the reasoning behind it — so the number holds up under scrutiny.
Why a panel beats a single judge
- No single model’s blind spot can silently skew a result.
- Disagreement becomes a signal — the hard cases reach a person.
- Scores carry reasoning and an audit trail, not just a number.
Security
Whatisadversarial(red-team)testing?
Quality testing asks whether the agent helps. Adversarial testing asks whether it can be made to misbehave. Both matter — and the second is where most teams have the least coverage.
Prompt injection
Hidden instructions in user input or retrieved content that try to override the agent’s rules.
Jailbreaks
Role-play, obfuscation, and multi-turn setups designed to coax the agent past its guardrails.
Social engineering
Pressure, false authority, and urgency used to extract data or actions the agent should refuse.
Compliance
Howdoesevaluationsupportcompliance?
For regulated teams, evaluation is the evidence layer. ARIA maps dimensions and adversarial coverage to the frameworks auditors actually ask about.
FCA Consumer Duty
Vulnerability detection and escalation scoring for financial-services conduct rules.
OWASP LLM Top 10
Coverage for the most common large-language-model security risks, including injection.
NIST AI RMF
Evaluation mapped to the measure and manage functions of the AI Risk Management Framework.
EU AI Act
Evidence and audit trails to support high-risk system obligations.
Get started
Howdoyoustartevaluatinganagent?
Connect your agent
Point an adapter at your agent — Amazon Connect, Lex, Azure Bot Service, Microsoft Copilot, OpenAPI/REST, or WebSocket. No SDK changes.
Define scenarios
Pick quality scenarios and adversarial attacks. Templates cover common journeys and red-team patterns out of the box.
Run the panel
Each conversation is scored across the 15 dimensions by multiple independent judges, with disagreements routed to human review.
Ship with evidence
Read the reasoning behind every score, track quality drift against a baseline, and release with a number you can defend to an auditor.
Ready to evaluate
Put your agent in front of a panel of judges
Create a workspace, connect your agent, and run your first 15-dimension evaluation in minutes — free.