AI app for it and development · no coding needed
AI model and agent performance operations workbench
Reduce the time from detected AI failure to reviewed, deployed improvement.
Made for: Engineering and product teams running AI models and agents in production
What it does for you
The problem
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.
What it gives you
Reviewer-approved improvements with a full audit trail
What you give it
Modelagent tracesevaluation resultsprompt versionsdeployment records
Build your own version of Inductor, LLMonitor and more
One app with what these 10 AI tools do, yours to keep and change: Inductor, LLMonitor, Langfuse 2.0, Openlayer, VoltOps, Langtrace AI, Handit.ai, Evidently AI, LangWatch Optimization Studio, Prefactor.
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
How it works, step by step
- Track model and agent outputs and interactions in real time
- Show metrics and trends in an analytics dashboard
- Alert on unusual or unexpected behaviour
- Connect data sources, development platforms and AI frameworks
- Generate customizable reports
- Process incoming data automatically
- Automate project workflows
- Surface AI-driven insights for decisions
- Create interactive maps with layers and markers
- Support geographic formats such as GeoJSON and KML
- Keep the interface responsive across devices
- Keep the codebase open for contributions and extensions
- Trace every step, including thoughts, tools and memory reads
- Show workflows and data in a visual interface
- Offer lightweight SDKs for integration
- Analyse text for sentiment and context
- Generate coherent content from prompts
- Process and generate text in multiple languages
- Adjust tone, style and other parameters
- Evaluate agent decisions automatically with configurable metrics
- Auto-generate improved prompts, model calls and datasets
- Validate improvements on production data with A/B tests
- Keep full traceability of inputs, outputs, decisions and tool calls
- Run built-in evaluation checks
- Support offline evaluations
- Define custom metrics and LLM-powered judges
- Produce interactive reports and exportable raw scores
- Manage prompt versions, tweaks and deployment
- Track latency, cost and output quality
- Score every agent run in real time with deterministic risk profiles
- Hold, approve or block runs that violate thresholds
- Version, stage and promote agents from dev to production
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 model and agent performance operations workbench 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 model and agent performance operations workbench 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 links6 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 data201 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 the time from detected AI failure to reviewed, deployed improvement. For engineering and product teams running AI models and agents in production, convert model and agent traces, evaluation results, prompt versions and deployment records into reviewer-approved improvements with a full audit trail. The benefit is a testable hypothesis, measured through detected failures resolved per engineering hour and regressions after deployment; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect model and agent traces, evaluation results, prompt versions and deployment records, then follow this sequence: 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. Resolve uncertain cases with qualified reviewers, approve reviewer-approved improvements with a full audit trail, and measure detected failures resolved per engineering hour and regressions after deployment against a documented baseline.
How the AI works
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.
Safeguards
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.
What to build first
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.
What it can connect to
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.
The screens in detail
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.
