AI model and agent performance operations workbench

For

Engineering and product teams running AI models and agents in production

Solves

Model and agent behaviour is monitored in disconnected tools, so teams cannot trace a failure, score it, improve it and prove the fix in one place.

Delivers

Reviewer-approved improvements with a full audit trail

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce the time from detected AI failure to reviewed, deployed improvement.

1. Track model and agent outputs and interactions in real time.
2. Show metrics and trends in an analytics dashboard.
3. Alert on unusual or unexpected behaviour.
4. Connect data sources, development platforms and AI frameworks.
5. Generate customizable reports.
6. Process incoming data automatically.
7. Automate project workflows.
8. Surface AI-driven insights for decisions.
9. Create interactive maps with layers and markers.
10. Support geographic formats such as GeoJSON and KML.
11. Keep the interface responsive across devices.
12. Keep the codebase open for contributions and extensions.
13. Trace every step, including thoughts, tools and memory reads.
14. Show workflows and data in a visual interface.
15. Offer lightweight SDKs for integration.
16. Analyse text for sentiment and context.
17. Generate coherent content from prompts.
18. Process and generate text in multiple languages.
19. Adjust tone, style and other parameters.
20. Evaluate agent decisions automatically with configurable metrics.
21. Auto-generate improved prompts, model calls and datasets.
22. Validate improvements on production data with A/B tests.
23. Keep full traceability of inputs, outputs, decisions and tool calls.
24. Run built-in evaluation checks.
25. Support offline evaluations.
26. Define custom metrics and LLM-powered judges.
27. Produce interactive reports and exportable raw scores.
28. Manage prompt versions, tweaks and deployment.
29. Track latency, cost and output quality.
30. Score every agent run in real time with deterministic risk profiles.
31. Hold, approve or block runs that violate thresholds.
32. Version, stage and promote agents from dev to production.

Everything these tools do, in one app

• Real-time monitoring Continuously tracks AI model or agent outputs and interactions as they happen.Found in LLMonitor, Langfuse 2.0, VoltOps and 3 more
• Analytics dashboard Provides a visual interface with metrics and trends to analyze AI performance.Found in Inductor, LLMonitor, Langfuse 2.0 and 2 more
• Alerting and anomaly detection Sends alerts when unusual or unexpected AI behavior is detected.Found in LLMonitor, Langfuse 2.0
• Integration with platforms Connects with various data sources, development platforms, and AI frameworks.Found in Inductor, LLMonitor, Langfuse 2.0 and 7 more
• Customizable reporting Allows users to generate reports tailored to specific needs or metrics.Found in LLMonitor, Langfuse 2.0, Evidently AI
• Automated data processing Automatically processes data to reduce manual effort.Found in Inductor
• Workflow automation Automates workflows to fit specific project needs.Found in Inductor
• AI-driven insights Provides insights generated by AI to enhance decision making.Found in Inductor
• Interactive map creation Enables creation of interactive maps with customizable layers and markers.Found in Openlayer
• Geographic data format support Supports multiple geographic data formats such as GeoJSON and KML.Found in Openlayer
• Responsive design Ensures seamless use across different devices and screen sizes.Found in Openlayer
• Open-source codebase Allows community contributions and extensions to the tool.Found in Openlayer, Handit.ai, Evidently AI and 1 more
• Structured tracing Traces every step in AI workflows, including thoughts, tools, and memory reads.Found in VoltOps, Handit.ai, Prefactor
• Visual interface Provides an intuitive visual representation of AI workflows and data.Found in VoltOps
• Lightweight SDKs Offers lightweight software development kits for easy integration.Found in VoltOps
• Text analysis Analyzes text for sentiment and context detection.Found in Langtrace AI
• Natural language generation Generates coherent and relevant content from prompts.Found in Langtrace AI
• Multilingual support Processes and generates text in multiple languages.Found in Langtrace AI
• Customizable settings Allows adjustment of tone, style, and other parameters.Found in Langtrace AI
• Automatic evaluation Automatically evaluates AI agent decisions using configurable metrics.Found in Handit.ai, Evidently AI, LangWatch Optimization Studio
• Auto-generation of improvements Automatically generates improved prompts, model calls, and datasets.Found in Handit.ai, LangWatch Optimization Studio
• A/B testing framework Validates improvements on production data before deployment.Found in Handit.ai
• Full traceability Provides full traceability of inputs, outputs, decisions, and tool calls.Found in Handit.ai
• Built-in evaluation checks Includes over 100 built-in checks for various AI evaluation scenarios.Found in Evidently AI
• Offline evaluations Supports offline testing and evaluation of AI models.Found in Evidently AI
• Custom metrics Allows definition of custom metrics and LLM-powered judges.Found in Evidently AI, LangWatch Optimization Studio
• Interactive reports Generates interactive reports and exportable raw evaluation scores.Found in Evidently AI
• Prompt management Manages prompt versions, tweaking, and deployment.Found in LangWatch Optimization Studio
• Observability tools Tracks performance metrics such as latency, cost, and output quality.Found in LangWatch Optimization Studio
• Real-time run scoring Scores every agent run in real time with deterministic risk profiles.Found in Prefactor
• Runtime enforcement Automatically holds, approves, or blocks runs that violate thresholds.Found in Prefactor
• Agent lifecycle management Versions, stages, and promotes agents from dev to production.Found in Prefactor

What goes in, what comes out

The workflow

1. In
   Start with

   Model and agent traces, evaluation results, prompt versions and deployment records

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect model and agent traces

4. 3

   Evaluation results

5. 4

   Prompt versions and deployment records

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved improvements with a full audit trail

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 connected agent set and one evaluation metric set; final deployment and risk decisions remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Connected sources and agents, Live trace and evaluation workspace, Improvement and deployment review. Use a project list for connected agents, a central trace timeline with scores and alerts, and a right-hand panel for metrics, prompt versions and reviewer comments. Let users compare runs and prompt versions side by side. Display monitoring, under review, approved and deployed states. Provide a client preview link with comments anchored to the relevant trace. Make the task-specific outcome reviewer-approved improvements with a full audit trail visible beside its evidence, review state and value baseline.

Accounts and administration

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

Integrations and data access

Customer-owned traces, evaluation results and prompt repositories. Cloud trace storage, development platforms and AI frameworks. 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: track model and agent outputs and interactions in real time; show metrics and trends in an analytics dashboard. 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

   3 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 engineering and product teams running AI models and agents in production use it to solve "model and agent behaviour is monitored in disconnected tools, so teams cannot trace a failure, score it, improve it and prove the fix in one place"?
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: Detected failures resolved per engineering hour and regressions after deployment.
4. Measure, then decide. Track detected failures resolved per engineering hour and regressions after deployment; 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 connected agent set and one evaluation metric set; final deployment and risk decisions remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: track model and agent outputs and interactions in real time; show metrics and trends in an analytics dashboard. Support the third module with operator review: alert on unusual or unexpected behaviour. 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 improvements with a full audit trail. Retain the explicit scope boundary: One connected agent set and one evaluation metric set; final deployment and risk decisions remain engineering.

What the build depends on. Trace upload and preview, asynchronous evaluation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One connected agent set and one evaluation metric set; final deployment and risk decisions remain engineering.

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 set. Offer a monthly production allowance after repeat demand. Quote complex multi-agent or specialist evaluation separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved improvements with a full audit trail. 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 the time from detected AI failure to reviewed, deployed improvement. Demonstrate a concrete reviewer-approved improvements with a full audit trail using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering and product teams running AI models and 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 improvements with a full audit trail from a small authorized input set, with a transparent calculation of detected failures resolved per engineering hour and regressions after deployment and no promised savings.

The first 30 days

1. Week 1: interview five engineering and product teams running AI models and agents in production and inspect a recent example of model and agent behaviour is monitored in disconnected tools, so teams cannot trace a failure, score it, improve it and prove the fix in one place.
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 detected failures resolved per engineering hour and regressions after deployment, 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: Detected failures resolved per engineering hour and regressions after deployment. 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

Detected failures resolved per engineering hour and regressions after deployment; 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 improvements with a full audit trail. 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 evaluation metrics, trace patterns and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering and product teams running AI models and agents in production. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Inductor, LLMonitor, Langfuse 2.0, Openlayer, VoltOps, Langtrace AI, Handit.ai, Evidently AI, LangWatch Optimization Studio and Prefactor, plus in-house scripts. Compare this product with the buyer's present method on detected failures resolved per engineering hour and regressions after deployment. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Trace storage, evaluation compute, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved improvements with a full audit trail. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve trace accuracy, source attribution, evaluation integrity and usage permissions. Engineering owners approve substantive changes and deployment scope. One connected agent set and one evaluation metric set; final deployment and risk decisions remain engineering. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.