AI voice and chat agent evaluation workspace

For

Product and QA teams operating AI voice and chat agents in production

Solves

Agent failures surface in live conversations, and teams lack one place to simulate, monitor and review voice and chat behaviour before and after deployment.

Delivers

Reviewer-approved evaluation reports linked to release decisions

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce undetected agent failures while keeping release decisions with named reviewers.

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.
5. Test agents across multiple languages.
6. Analyse tone, audio quality and response timing.
7. Stress test with accents, noise and interruptions.
8. Evaluate live calls in progress.
9. Link failing outputs to function calls and traces.
10. Simulate multi-turn conversations.
11. Integrate with multiple agent frameworks.
12. Let domain experts and developers co-create tests.
13. Provide transparent, extendable platform code.
14. Chain prompts and actions into workflows.
15. Select models and criteria per step.
16. Run batch tests over dedicated datasets.
17. Merge LLM metrics, human review and product signals.
18. Give non-engineers dashboards to run experiments.
19. Detect errors in outputs automatically.
20. Compare the reviewed result with the recorded baseline and value assumptions.
21. Capture corrections and named-owner approval before release.
22. Export a versioned reviewer-approved evaluation report linked to release decisions with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Agent configurations, test scenarios, live transcripts and audio recordings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent configurations

4. 3

   Test scenarios

5. 4

   Live transcripts and audio recordings

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved evaluation reports linked to release decisions

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, agent versions, reviewer comments, approval states, usage allowances, test limits, export history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: generate and run simulated voice and chat interactions; define custom evaluation metrics in plain language. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product and QA teams operating AI voice and chat agents in production use it to solve "agent failures surface in live conversations, and teams lack one place to simulate, monitor and review voice and chat behaviour before and after deployment"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted evaluation cases per reviewer hour and post-release incidents traced to untested behaviour.
4. Measure, then decide. Track accepted evaluation cases per reviewer hour and post-release incidents traced to untested behaviour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved evaluation reports linked to release decisions. Retain the explicit scope boundary: One fixed agent framework and one language pair; final release and quality decisions remain human.

What the build depends on. Agent upload and preview, asynchronous simulation jobs, editable version history, reviewer access and tested export formats. High-fidelity voice testing requires specialist audio QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed agent framework and one language pair; final release and quality decisions remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined agent package. Offer a monthly production allowance after repeat demand. Quote complex voice, multilingual or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved evaluation report linked to release decisions. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce undetected agent failures while keeping release decisions with named reviewers. Demonstrate a concrete reviewer-approved evaluation report linked to release decisions using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and QA teams operating AI voice and chat agents in production professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved evaluation report linked to release decisions from a small authorized input set, with a transparent calculation of accepted evaluation cases per reviewer hour and post-release incidents traced to untested behaviour and no promised savings.

The first 30 days

1. Week 1: interview five product and QA teams operating AI voice and chat agents in production and inspect a recent example of agent failures surface in live conversations, and teams lack one place to simulate, monitor and review voice and chat behaviour before and after deployment.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted evaluation cases per reviewer hour and post-release incidents traced to untested behaviour, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted evaluation cases per reviewer hour and post-release incidents traced to untested behaviour. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted evaluation cases per reviewer hour and post-release incidents traced to untested behaviour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved evaluation reports linked to release decisions. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved test scenarios, reviewer corrections and verified operating constraints, together with reliable delivery for a narrow agent-operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and QA teams operating AI voice and chat agents in production. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Coval, TestAI, Relyable, Vocera, Simulate by Future AGI, LangWatch Scenario - Agent Simulations, Basalt Agents, Scorecard and Future AGI. Compare this product with the buyer's present method on accepted evaluation cases per reviewer hour and post-release incidents traced to untested behaviour. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Simulation runs, audio processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved evaluation reports linked to release decisions. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.