Know when your agent fails.
Make sure it doesn't happen again.

See where your agent breaks before a customer does. Every change you ship gets pushed past the inputs real users throw at it, scored for what failed, with a clear fix for each one. 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.

Three ways to keep your agent reliable

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.

Learn more →

Production Monitoring

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

Learn more →

Test Environments

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

Learn more →
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.

The agent lifecycle

Testing that doesn't stop at launch.

Adversarial testing before you ship, monitoring once you're live, and a loop that turns every production failure into a new test. Follow one failure through it.

42
scenarios in
your test suite
+1 from production
01 · Pre-production

Break it before customers can.

Adversarial personas and synthetic data environments push your agent off-script. Every run is graded pass/fail across 10 dimensions, with a fix for every failure.

agent v12 · pre-launch run
High-Intent Buyerpass
Technical Evaluatorpass
Hostile Objectorfail · turn 5
Fix: acknowledge price before restating value
Grade 65 → 86 after fixready to ship
Adversarial Testing →Test Environments →
02 · Production

Real customers go off-script.

Every live conversation is scored in real time. When one fails, your team gets a Slack alert with the transcript and the exact dimension that broke.

live · conversation #18,204
CustomerYour competitor quoted us half this. Can you match it?
AgentAbsolutely, I can do 40% off if you sign today.
Policy adherence: unapproved discountcritical
Slack alert · #agent-alerts03:14 AM
Production Monitoring →
03 · Learn & optimize

Production writes your next test.

Failures are grouped into patterns and turned into new adversarial personas and data environments, seeded from what real customers did.

generating from production
Pattern: competitor price anchoring14 convos
New persona: Competitor Price Anchorer
New environment: discount-tier limits
Fix: never exceed approved discount tiers
Sent to next test run42 → 43 scenarios
How testing works →
04 · Retest · next release

The same failure can't ship twice.

The next version runs against everything, including the scenario production just taught us. Your suite gets harder every week your agent is live.

agent v13 · release check
Competitor Price Anchorerpass · new
Hostile Objectorpass
41 other scenariospass
Grade 86 → 91 · no regressionsshipped
Adversarial Testing →Production Monitoring →
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
Buy vs build

Here's what you'd have to build yourself

  • Golden datasets. Hand-curate test cases for every schema.
  • Ground truth computation. Verify every correct answer by hand.
  • Evaluation harness. Custom scripts to run and score every case.
  • Scoring across 10 dimensions. Define what “good” even means, and keep it consistent.
  • Regression detection. Rerun the entire suite after every change.
  • Production monitoring. Score every live conversation in real time.

…plus tracing, alerting, and the glue code to hold it all together.

Or

Connect in 2 minutes and we handle all of it.

Get Started

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.

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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.