ClientCoded
Summary
You pushed a prompt update at 11 PM, and by morning your AI SDR had been stalling on price objections for six hours — deals going cold while you slept. ClientCoded exists to catch that before your customers do.
ClientCoded runs adversarial multi-turn conversations against your agent, scores every exchange across 10 published dimensions, and fires a Slack alert when a prompt change drops a metric overnight. The platform covers both conversational agents — support bots, SDRs, lead qualifiers — and data agents tested against synthetic CRM, ticketing, or knowledge-base environments with precomputed ground truth. Regression detection is the headline: when a score drops between releases, you get a pinpointed failure with the exact turn and dimension. The vendor states no SDK is required — one webhook connects to CI/CD. Where it thins out: the scoring rubric is fixed at 10 dimensions, and teams needing domain-specific evaluation criteria will find precious little flexibility to extend it.
Bottom line: Bet on ClientCoded when your support bot or AI SDR needs regression coverage across releases and you want Slack alerts before users file complaints — plan around it when your quality criteria cannot be expressed inside a 10-dimension rubric.
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Pros
Sign in to edit- Adversarial persona generation creates conversations outside the scripts your engineers wrote, so edge cases that ground-truth suites miss — like a prospect who derails on price at turn 5 — surface in testing instead of production.
- A published 10-dimension scoring rubric grades every conversation consistently, which means regression comparisons between releases produce the same baseline instead of drifting with each LLM judge call.
- Regression detection is automatic on deploy: when a prompt change drops a score overnight, you get a Slack alert with the exact dimension and turn that failed — so you are not waiting for a customer complaint to locate the break.
- 35+ pre-built synthetic data environments for Salesforce, Jira, Stripe, and similar systems let data agent teams run grounded tests with known-correct answers, so accuracy scoring is not approximate.
- No-SDK webhook integration means connecting an existing LangChain, Botpress, or Voiceflow agent to the test runner does not require refactoring the agent codebase.
Cons
Sign in to edit- The scoring rubric is fixed at 10 dimensions and the docs describe no mechanism to add or replace them — teams whose quality definition includes domain-specific criteria (say, regulatory compliance phrasing or brand voice grading) have no path to extend the rubric and typically instrument a parallel custom eval layer.
- The platform is cloud-only with no self-hosted option, so teams under strict data residency or HIPAA-type constraints cannot route live production conversations through it; those teams evaluate on-prem alternatives or build internal replay pipelines instead.
- Multi-turn testing covers conversational and data agents, but the vendor page describes no coverage model for agents that take real-world actions — file writes, API calls with side effects, payment processing — which means teams building those systems will run this alongside a separate integration test harness or switch to a broader agent-simulation platform.
About
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-09-15T04:35:34.272Z
Best For
Who it's for
- Engineering teams maintaining AI agents
- Teams using LangChain, CrewAI, Botpress or similar frameworks
- Organizations needing outcome-based pass/fail grading
- Products requiring multi-turn conversation testing
What it does well
- Adversarial testing of support bots and AI SDRs
- Regression detection after prompt or model updates
- Real-time production monitoring with Slack alerts
- Benchmarking agent quality across releases
- Testing data agents against synthetic CRM or ticketing systems
Integrations
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Sign Up to ContributeFrequently Asked Questions
- Is ClientCoded free?
- ClientCoded has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is ClientCoded open source?
- No — ClientCoded is a closed-source tool. Source code is not publicly available.
- Does ClientCoded have an API?
- Yes. ClientCoded exposes a developer API. See the official documentation at https://clientcoded.com for details.
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ClientCoded is a QA and monitoring platform for AI agents. The core workflow is: connect your agent via webhook, run a battery of adversarial synthetic personas across multi-turn conversations, receive a pass/fail grade per dimension, and get regression alerts when a deploy degrades scores. The platform scores conversations across 10 dimensions — including context retention, info accuracy, objection handling, and escalation — using a published rubric so results are auditable and consistent across runs. No SDK is required; the vendor states it works with any agent built on LangChain, CrewAI, AutoGen, Botpress, Voiceflow, or a custom REST endpoint.
The differentiating feature is adversarial persona generation. Most QA setups test the scenarios an engineer wrote in advance. ClientCoded generates synthetic users — a Hostile Objector, a Tire Kicker, an Off-Topic Derailer — that push your agent into conversations no one scripted. For data agents, the platform builds a synthetic version of your Salesforce, Jira, or Stripe environment with 200 adversarial queries and precomputed correct answers, which means the test knows what the right output looks like before the agent responds.
Production monitoring runs in real time: every live conversation is scored and Slack alerts fire when quality drops below threshold. The vendor also states the platform automates approximately 90% of repetitive QA work and saves roughly two weeks per release cycle, though those figures carry a footnote that estimates vary by team. The platform is cloud-only — no self-hosted option exists — which matters for teams with data residency requirements. Teams needing custom scoring dimensions beyond the fixed 10 will hit a ceiling; the rubric is a product decision, not a configurable input.