Production agent trace and evaluation console

For

Engineering and operations teams running AI agents in production

Solves

Agent behavior in production is hard to trace, score and debug across models, tools and sub-agents, so quality drift and failures surface late.

Delivers

Reviewed agent health reports and debug findings

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 time to diagnose agent failures and quality drift while keeping production traces under the team's control.

1. Capture full agent executions as sessions, traces and spans across LLMs, tools and sub-agents.
2. Group related traces and calls into one session timeline.
3. Score traces and sessions with agent-specific metrics and rubrics.
4. Schedule recurring evaluations and track agent health over time.
5. Track token usage, cost and latency per request and workflow step.
6. Label user intents, corrections and agent resolutions automatically.
7. Collect reasoning blocks and tool decision metadata.
8. Segment metrics by agent version to compare releases.
9. Monitor agent runs in real time with logs and status.
10. Track workflow state in a Kanban-style task and stage view.
11. Support parallel instances and sub-agent reporting under a common task.
12. Debug failures and inspect agent output.
13. Manage agent configuration such as API keys, roles and project mapping.
14. Visualize latency and operation order in a waterfall timeline.
15. Capture TLS-encrypted LLM calls from network traffic without SDKs or proxies.
16. Map agent turns to traces and LLM calls to spans using OpenTelemetry.
17. Convert captured production traffic into fine-tuning training data.
18. Detect unsupported claims and report hallucination-focused KPIs.
19. Generate test questions from the agent's own knowledge.
20. Connect to data sources such as PostgreSQL, internal APIs and Pinecone for reference checks.
21. Compare the reviewed result with the recorded baseline and value assumptions.
22. Capture corrections and named-owner approval before consequential use.
23. Export a versioned reviewed agent health report with source references and unresolved questions.

Everything these tools do, in one app

• Agent execution tracing Captures full agent executions as sessions, traces, and spans across LLMs, tools, and sub-agents.Found in PandaProbe Cloud, PandaProbe, Progress AI Observability and 2 more
• Session grouping Groups related traces and calls into a single session timeline for easier reconstruction.Found in PandaProbe Cloud, PandaProbe, Voker
• Evaluation scoring Scores traces and sessions using agent-specific metrics and rubrics to detect quality drift.Found in PandaProbe Cloud, PandaProbe, Progress AI Observability and 2 more
• Production monitoring Schedules recurring evaluations and tracks agent health over time in production.Found in PandaProbe Cloud, PandaProbe, Progress AI Observability and 1 more
• Cost and latency tracking Tracks token usage, cost, and latency attributed to specific requests and workflow steps.Found in PandaProbe, Foglamp, PromptLayer
• Automatic annotations Automatically labels user intents, corrections, and agent resolutions to simplify triage.Found in Voker
• Reasoning capture Collects reasoning/thinking blocks and tool decision metadata to connect decisions to outcomes.Found in Voker, Foglamp
• Version segmentation Segments metrics by agent version to compare performance across releases.Found in Voker, PandaProbe
• Real-time run monitoring Monitors agent runs in real time with logs and status updates.Found in AgentCenter for OpenClaw
• Workflow tracking Tracks workflow state with a Kanban-style view for tasks and stages.Found in AgentCenter for OpenClaw
• Multi-agent coordination Supports parallel instances and sub-agent reporting under a common task.Found in AgentCenter for OpenClaw, PandaProbe Cloud
• Failure debugging Provides tools to debug failures and inspect agent output to speed troubleshooting.Found in AgentCenter for OpenClaw, Progress AI Observability
• Agent configuration management Manages agent configuration such as API keys, roles, and project mapping from a dashboard.Found in AgentCenter for OpenClaw
• Waterfall timeline view Visualizes latency and order of operations across multi-step runs in a waterfall view.Found in PromptLayer
• Network traffic capture Captures TLS-encrypted LLM calls directly from network traffic without SDKs or proxies.Found in Heron
• OpenTelemetry mapping Maps agent turns to traces and LLM calls to spans using OpenTelemetry.Found in Heron
• SFT trajectory export Converts captured production agent traffic into fine-tuning training data.Found in Heron
• Hallucination detection Detects unsupported claims and reports hallucination-focused KPIs.Found in Rippletide Eval CLI
• Automatic test generation Generates test questions from the agent's own knowledge for evaluation.Found in Rippletide Eval CLI
• Data source integration Connects to common data sources such as PostgreSQL, internal APIs, and Pinecone for reference checks.Found in Rippletide Eval CLI

What goes in, what comes out

The workflow

1. In
   Start with

   Agent execution traces, evaluation rubrics, production metrics and data source connections

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent execution traces

4. 3

   Evaluation rubrics

5. 4

   Production metrics and data source connections

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed agent health reports and debug findings

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 approved capture method and a fixed set of agent versions; final quality judgments and production changes remain with the responsible engineering owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent and project setup, Live run board, Trace and session explorer, Evaluation and rubric workbench, Health and cost dashboard, Export and training-data review. Use a project list with agent versions, a central session timeline with a waterfall view, and a right-hand panel for spans, reasoning blocks, annotations and comments. Let users compare agent versions side by side. Display running, needs review, reviewed and failed states. Provide a shareable trace link with comments anchored to the relevant span. Make the task-specific outcome reviewed agent health reports and debug findings visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, trace retention, rubric versions, reviewer assignments, approval states, usage allowances, 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

Agent-owned traces, authorized evaluation rubrics and permitted production metrics. Cloud trace storage, OpenTelemetry collectors, data sources such as PostgreSQL, internal APIs and Pinecone, and training-data destinations. Start with file exchange and validate destination specifications before promising direct publishing. 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: capture full agent executions as sessions, traces and spans; group related traces and calls into one session timeline; score traces and sessions with agent-specific metrics and rubrics; schedule recurring evaluations and track agent health over time; track token usage, cost and latency per request and workflow step. 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 operations teams running AI agents in production use it to solve "agent behavior in production is hard to trace, score and debug across models, tools and sub-agents, so quality drift and failures surface late"?
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: Time to diagnose a failed run and share of runs with a completed review.
4. Measure, then decide. Track time to diagnose a failed run and share of runs with a completed review; 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 approved capture method and a fixed set of agent versions; final quality judgments and production changes remain with the responsible engineering owner. Implement one approved input format, a bounded representative case set and the first five task modules: capture full agent executions as sessions, traces and spans; group related traces and calls into one session timeline; score traces and sessions with agent-specific metrics and rubrics; schedule recurring evaluations and track agent health over time; track token usage, cost and latency per request and workflow step. Support the remaining modules with operator review: label user intents, corrections and agent resolutions automatically; collect reasoning blocks and tool decision metadata; segment metrics by agent version; monitor agent runs in real time; track workflow state; support parallel instances and sub-agent reporting; debug failures; manage agent configuration; visualize latency in a waterfall timeline; capture TLS-encrypted LLM calls; map agent turns to traces and spans; convert production traffic into fine-tuning training data; detect unsupported claims; generate test questions; connect to data sources. 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 reviewed agent health reports and debug findings. Retain the explicit scope boundary: One approved capture method and a fixed set of agent versions; final quality judgments and production changes remain with the responsible engineering owner.

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 approved capture method and a fixed set of agent versions; final quality judgments and production changes remain with the responsible engineering owner.

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 multi-agent or on-premise capture separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed agent health reports and debug findings. 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 time to diagnose agent failures and quality drift while keeping production traces under the team's control. Demonstrate a concrete reviewed agent health reports and debug findings using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering and operations teams running AI 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 reviewed agent health reports and debug findings from a small authorized input set, with a transparent calculation of time to diagnose a failed run and share of runs with a completed review and no promised savings.

The first 30 days

1. Week 1: interview five engineering and operations teams running AI agents in production and inspect a recent example of agent behavior in production is hard to trace, score and debug across models, tools and sub-agents, so quality drift and failures surface late.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure time to diagnose a failed run and share of runs with a completed review, 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: Time to diagnose a failed run and share of runs with a completed review. 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

Time to diagnose a failed run and share of runs with a completed review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed agent health reports and debug findings. 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 rubrics, agent versions and review examples, together with reliable delivery for a narrow production-agent niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering and operations teams running AI agents in production. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

PandaProbe Cloud, PandaProbe, Progress AI Observability, Voker, Foglamp, Unify, AgentCenter for OpenClaw, PromptLayer, Heron and Rippletide Eval CLI. Compare this product with the buyer's present method on time to diagnose a failed run and share of runs with a completed review. 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 assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed agent health reports and debug findings. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve trace integrity, source attribution, evaluation accuracy and usage permissions. Engineering owners approve substantive changes and production scope. One approved capture method and a fixed set of agent versions; final quality judgments and production changes remain with the responsible engineering owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.