QA Platform for AI Agents

Find where your
agent breaks.
Automatically.

We generate adversarial test scenarios, synthetic data environments, and prompt fix suggestions. When your team pushes a change, we detect it, test it, and tell you what broke. Works with any agent.

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Member of NVIDIA Inception Program
clientcoded / agent-test-run
Live monitoring
Overall grade
65/ 100
Grade D · Failing
EndpointCustom
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

Adversarial Testing

Generate adversarial personas that push your agent off-script. Score every conversation pass/fail across 10 dimensions. Detect prompt changes, retest, flag regressions.

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Production Monitoring

Every conversation scored in real time. Slack alerts when quality drops. One webhook to set up.

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Test Environments

40 pre-built environments for Salesforce, Jira, Stripe, and more. 200 adversarial queries each with computed ground truth.

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90%
of repetitive QA work, automated*
2 weeks
saved per release cycle*
35+
synthetic environments, plus adversarial testing for conversational agents

*Based on typical automatable QA workload; estimates vary by team and agent.

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.

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
Setup

Set up in 2 minutes

Point your agent at one endpoint. No SDK required for monitoring. Every conversation is scored from the first message.

Terminal
$ curl -X POST https://api.clientcoded.com/monitor \
    -H "Content-Type: application/json" \
    -d '{ "agent_id": "...", "api_key": "...", "transcript": [ ... ] }'

# Every conversation scored.
# Slack alerts when quality drops.
# No SDK. One endpoint.
Want deeper visibility?
Python SDK
$ pip install clientcoded

import clientcoded
clientcoded.init(agent_id="your-agent-id", api_key="your-api-key")
# See every LLM call, tool call, and
# reasoning step inside your agent.

Read the full setup docs →

Before / After

From guessing to knowing

Before ClientCoded
×Manual spot-checks after every update
×"It looks fine" as the QA process
×Failures discovered by customers
×Engineers maintaining eval frameworks
After ClientCoded
✓Scorecard in 15 minutes after every update
✓200 adversarial queries with ground truth
✓Failures caught before production
✓Zero test maintenance

Know exactly where your agent stands.

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

Live 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 Get a Free Audit
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 automated testing infrastructure that tests them continuously. 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 across regulated industries. 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.