AI app for it and development · no coding needed
AI voice and chat agent evaluation workspace
Reduce undetected agent failures while keeping release decisions with named reviewers.
Made for: Product and QA teams operating AI voice and chat agents in production
What it does for you
The problem
Agent failures surface in live conversations, and teams lack one place to simulate, monitor and review voice and chat behaviour before and after deployment.
What it gives you
Reviewer-approved evaluation reports linked to release decisions
What you give it
Agent configurationstest scenarioslive transcriptsaudio recordings
Build your own version of Coval, TestAI and more
One app with what these 9 AI tools do, yours to keep and change: Coval, TestAI, Relyable, Vocera, Simulate by Future AGI, LangWatch Scenario - Agent Simulations, Basalt Agents, Scorecard, Future AGI.
Everything these tools do, in one app
- Automated scenario simulation Generates and runs many simulated interactions to test agent behavior without manual effort.Found in Coval, TestAI, Relyable and 2 more
- Custom evaluation metrics Lets users define specific criteria to measure agent performance beyond generic scores.Found in Coval, Simulate by Future AGI, Basalt Agents and 1 more
- Real-time production monitoring Continuously watches live agent interactions to catch issues as they happen.Found in Coval, TestAI, Relyable and 2 more
- CI/CD integration Connects evaluation into development pipelines for automatic testing on code changes.Found in Coval, Scorecard
- Multilingual testing Tests agents in multiple languages to ensure consistent performance across locales.Found in Coval, TestAI, Simulate by Future AGI
- Audio quality evaluation Analyzes tone, audio quality, and response timing in voice interactions.Found in Simulate by Future AGI
- Stress testing with variables Introduces accents, background noise, and interruptions to test agent robustness.Found in TestAI
- Live call evaluation Reviews ongoing voice calls to monitor quality and performance.Found in Relyable
- Trace-level observability Links failing outputs to underlying function calls and execution traces for debugging.Found in Coval, Scorecard
- Multi-turn scenario testing Simulates complex, multi-step conversations to evaluate agent decision-making.Found in LangWatch Scenario - Agent Simulations
- Framework-agnostic integration Works with various agent frameworks and platforms without locking users in.Found in LangWatch Scenario - Agent Simulations
- Collaborative testing environment Enables domain experts and developers to work together on test creation and validation.Found in LangWatch Scenario - Agent Simulations
- Open-source platform Provides transparent, customizable code that users can modify and extend.Found in LangWatch Scenario - Agent Simulations
- Multi-step workflow builder Allows chaining prompts and actions into end-to-end agent workflows.Found in Basalt Agents
- Per-step model selection Lets users choose the best model for each step and set step-specific evaluation criteria.Found in Basalt Agents
- Batch testing with datasets Runs tests across hundreds of scenarios using dedicated datasets to quantify performance.Found in Basalt Agents
- Combined scoring signals Merges LLM-based metrics, human review, and product signals into actionable evaluations.Found in Scorecard
- Non-engineer dashboards Provides interfaces for non-technical users to run experiments and validate outputs.Found in Scorecard
- Automated error detection Uses AI agents to find errors in model outputs without human intervention.Found in Future AGI
- Natural language metrics Allows setting custom evaluation criteria using plain language descriptions.Found in Future AGI
How it works, step by step
- Generate and run simulated voice and chat interactions
- Define custom evaluation metrics in plain language
- Monitor live agent interactions continuously
- Connect evaluation to CI/CD pipelines
- Test agents across multiple languages
- Analyse tone, audio quality and response timing
- Stress test with accents, noise and interruptions
- Evaluate live calls in progress
- Link failing outputs to function calls and traces
- Simulate multi-turn conversations
- Integrate with multiple agent frameworks
- Let domain experts and developers co-create tests
- Provide transparent, extendable platform code
- Chain prompts and actions into workflows
- Select models and criteria per step
- Run batch tests over dedicated datasets
- Merge LLM metrics, human review and product signals
- Give non-engineers dashboards to run experiments
- Detect errors in outputs automatically
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before release
- Export a versioned reviewer-approved evaluation report linked to release decisions with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for AI voice and chat agent evaluation workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds AI voice and chat agent evaluation workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data195 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce undetected agent failures while keeping release decisions with named reviewers. For product and QA teams operating AI voice and chat agents in production, convert agent configurations, test scenarios, live transcripts and audio recordings into reviewer-approved evaluation reports linked to release decisions. The benefit is a testable hypothesis, measured through accepted evaluation cases per reviewer hour and post-release incidents traced to untested behaviour; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect agent configurations, test scenarios, live transcripts and audio recordings, then follow this sequence: 1. Generate and run simulated voice and chat interactions. 2. Define custom evaluation metrics in plain language. 3. Monitor live agent interactions continuously. 4. Connect evaluation to CI/CD pipelines. Resolve uncertain cases with qualified reviewers, approve reviewer-approved evaluation reports linked to release decisions, and measure accepted evaluation cases per reviewer hour and post-release incidents traced to untested behaviour against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed agent framework and one language pair; final release and quality decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve agent behaviour, source attribution, transcript accuracy and usage permissions. Named reviewers approve substantive changes and release scope. One fixed agent framework and one language pair; final release and quality decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One fixed agent framework and one language pair; final release and quality decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate and run simulated voice and chat interactions; define custom evaluation metrics in plain language. Support the third module with operator review: monitor live agent interactions continuously. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Agent-owned configurations, authorized transcripts and permitted monitoring sources. Cloud storage, CI/CD pipelines and agent framework APIs. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Scenario library and test setup, Live monitoring board, Evaluation review and release gate. Use a thumbnail gallery for agent projects, a large central transcript or trace viewer, and a right-hand panel for metrics, reviewer notes and evidence. Let users compare runs side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant turn or trace. Make the task-specific outcome reviewer-approved evaluation reports linked to release decisions visible beside its evidence, review state and value baseline.
