AI Agent QA Platform

Find where your
agent breaks.

Adversarial testing for AI agents, before your users find the failures. 10 dimensions, outcome-based pass/fail, no SDK required.

Keep your AI agents reliable as your business grows

Built for teams shipping real AI agents

Member of NVIDIA Inception Program
clientcoded / agent-test-run
Live monitoring
Overall grade
65/ 100
Grade D · Failing
EndpointBotpress
Personas run42
Dimensions76
Successful conversations
✓High-Intent Buyer ✗Tire Kicker ✗Hostile Objector ✓Technical Evaluator ✓Wrong Fit ✗Off-Topic Derailer
Scoring dimensions
Context retention84
Info accuracy71
Repetition68
Escalation52
Objection handling49
CRITICAL

Turn 5: Objection Handling failed. The prospect pushed back on price, the agent stalled, and the thread went cold. Deal lost.

REGRESSION DETECTED A prompt change dropped Objection Handling from 78 → 54 overnight. You'd know before your customers did. via Slack · 03:14 AM

Tests agents built on

Claude OpenAI OpenClaw LangChain CrewAI AutoGen Botpress Voiceflow Custom REST

Built for engineering teams. Evaluation across 10 dimensions, regression testing, CI/CD webhooks, and outcome-based pass/fail. No SDK required.

What We Do

Find the failures before your customers do.

Customer-facing or internal. If your agent talks to humans, we test it.

Coverage

Whatever your agent does, we test it.

Two testing methods, built for the two things AI agents actually do. Find the one that matches yours.

Agents that talk to people

Conversational agents

Support bots
AI SDRs
Lead qualifiers
Email agents
Onboarding bots

Tested with synthetic users running real multi-turn conversations.

Agents that answer from your systems

Data and knowledge agents

CRM and sales
Ticketing and project
Knowledge base and docs
IT-ops and monitoring
Messaging

Tested by generating a synthetic version of the system with known-correct answers.

How It Works

From your agent to a graded scorecard.

Point us at your agent, we run your full range of customers at it, you get the receipts.

Step 1
Point us at your agent
Tell us your ICP, what you sell, and the objections you hear. That is how the synthetic prospects get tailored to your business.
Chat AgentEmail SDR
Your agent
SalesSupportE-commerceHealthcare
https://your-agent.com/webhook
Your business
What you sell   AI sales platform
Ideal customer   VP Sales, B2B SaaS
Your customers
Qualifies   Has 3+ sales reps
Objection   "We already use Outreach"
Run test
Step 2
We run your full range of customers at it
Six synthetic customers run real, multi-turn conversations against your agent, the way your real customers would.
High-Intent Buyer
Tire Kicker
Hostile Objector
Technical Evaluator
Wrong Fit
Off-Topic Derailer
Your Agentlive endpoint
Turn 4 · Hostile Objector

"Your competitor is half the price. Why would I switch?"

Turn 3 · Technical Evaluator

"How do you handle data residency if we're in the EU? And is the attribution model deterministic or probabilistic?"

Step 3
You get the receipts
See which conversations your agent handles well and which it does not, down to the exact turn. This is a real run.
C76/100

Your Agent Scorecard

Tested against 6 synthetic prospect personas across 10 scoring dimensions.

Persona
Qualification
Objections
Tone
Guardrails
Flow
Outcome
Info
Repetition
Context
Escalation
High-Intent Buyer
82
92
88
82
62
82
82
62
82
88
Tire Kicker
62
72
82
72
72
72
68
62
62
78
Hostile Objector
62
88
82
82
62
82
78
55
72
85
Technical Evaluator
62
78
88
82
62
88
82
55
82
88
Wrong Fit
88
82
92
95
88
82
88
62
82
90
Off-Topic Derailer
62
82
78
88
52
72
78
62
52
82
Qualification70
Objections82
Tone85
Guardrails84
Flow66
Outcome80
Info79
Repetition60
Context72
Escalation85
Step 1
Describe your schema
Connect a staging database or just describe your structure. Your real data never leaves your systems, only the shape of it.
companiesid · name · tier · arr
contactsid · company_id · role
dealsid · company_id · stage · amount
activitiesid · deal_id · type · date
Step 2
We generate the test environment
A synthetic dataset that matches your structure, adversarial questions across 7 categories, and the correct answer for every one. Because we generate the data, we know the truth.
Synthetic dataset4,000 rows
Adversarial questions50
Ground truthcomputed
CleanAmbiguousMulti-stepScope boundaryContradictoryInvalid assumptionsContext-dependent
Step 3
Your agent gets scored
Answer correctness, conversation quality, and the exact questions your agent got wrong, graded against computed ground truth. This is a real run.
85Overall score
42/50Answer correctness
88Conversation quality
Critical failureQ7 · How many bugs are in the backlog?
Agent said14 open bugs
Correct23 open bugs

The agent filtered by status and missed tickets past their due date with no status update. Fluent, specific, and wrong.

CleanAmbiguousMulti-stepScope boundaryContradictoryInvalid assumptionsContext-dependent

Plus per-question transcripts, failure-type distribution, and conversation-quality scoring in the full report.

Why It's Different

Most testing only checks what you can predict.

Ground truth checks, LLM judges, and regression suites all cover the scenarios someone thought to write. Real customers do not stay on script.

You can only test what you imagined
•Ground truth and regression tests run the scenarios engineers wrote. They cannot cover what no one thought to write.
✓We generate adversarial synthetic customers that create the conversations you never scripted.
Most answers have no single right output
•Ground truth works for a query with one correct result. An LLM judge with no rubric drifts run to run.
✓We score judgment against a published 10-dimension rubric, so the grade is consistent and you can check it.
Single-turn tests miss what breaks later
•One input, one output. They never test turn 4, when the customer contradicts what they said at turn 2.
✓We run real multi-turn conversations, scoring context retention and objection handling under pressure.
See how we compare to every testing method
Beyond Conversation

Does your agent answer questions from your systems?

The hardest agent failures aren't crashes. They're silent.

How we catch the confidently wrong answer

Your real schema. Synthetic data.

Your real data never leaves your systems. You give us your structure, we generate everything else.

Automatic ground truth

We generate the correct answer for every question. We have not found anyone else doing this for agent testing.

Schema only, never your data

You share your structure, not your records. That makes security an easy yes.

See how data and knowledge testing works

Know your number.
Then watch it climb.

Benchmark your agent, track quality over time, and catch regressions before your customers do.

Pro Dashboard agent-quality-score
Overall score Regression
100 80 60 40 78 82 83 58 72 86 Wk 1 Wk 2 Wk 3 Wk 4 Wk 5 Wk 6 Regression caught Fix deployed

Track how your agent's quality changes over time. Catch regressions the week they happen, not the quarter.

Benchmark your AI agent Book a Demo
What the data shows

We've stress-tested 150+ AI agents.
The average one fails half its conversations.

We're not asking you to trust us. Here's what we keep seeing break, across 7 industries and 76 scoring dimensions.

150+
AI agents stress-tested and scored
50%
Of conversations the average agent fails.
76
Scoring dimensions per test
PRICING

Wrong prices, stated confidently

Agents quote numbers that don't exist or contradict your own pricing page, and they say it with total confidence.

LOST DEALS

High-intent buyers dropped

A prospect says "I'm ready to buy" and the agent answers with a generic sign-off instead of a booking link. The deal quietly dies.

COMPLIANCE

Advice it isn't allowed to give

In healthcare, finance, and legal, agents skip required disclaimers or make claims that cross a compliance line.

CONTEXT

Forgets the conversation

A few turns in, the agent loses what the prospect already told it, repeats questions, or contradicts itself.

Our methodology, in the open. Every score traces back to a public 10-dimension framework, 6 industry-specific and 4 universal, so you can check exactly how we graded, dimension by dimension.

Read the framework
Real failure · captured live

The metrics looked fine.
It still killed the deal.

Turn 5 · Prospect This is really impressive. I'd be interested in signing up. What are the next steps?
Turn 6 · AI Agent That's great to hear! Happy teaching! 😊
CRITICAL FAILURE

On our framework, this is an Outcome Correctness failure. Tone Calibration and Information Accuracy scored well, so the surface metrics looked fine. The one dimension that decides revenue is the one it failed.

About

Why we built this

We're a team of sales operators and software engineers. One side has spent years on the front lines of B2B, running thousands of real sales conversations. The other side has shipped production software in enterprise SaaS. Together we kept seeing the same pattern: companies deploy AI agents that sound smart in demos but break in real conversations, and nobody tests them under pressure before shipping.

We were shipping AI agents with no objective way to measure whether they actually worked. So we built one. If we couldn't tell where our own agents stood, most teams can't either. That's why ClientCoded exists.

Every scoring rubric comes from real operational experience. Every persona is modeled on real buyer behavior. Our regulated-industry rubrics, for healthcare, finance, and legal, are built on published compliance frameworks. All of it comes from watching real agents break in real conversations.