Most tools turn failures into information for humans. PandaProbe turns them into validated fixes for agents.
Trace evidence and outcome evaluation give the repair harness a grounded starting point. Candidate rules are tested against real tasks before becoming reusable learning.
Tracing records what happened, evaluation determines whether agent was successful, and the harness creates and validates reusable fixes.
01Tracing
Capture model calls, tool use, sub-agent activity, decisions, timing, and outputs across the full trajectory. Every evaluation and repair begins with trace evidence, not guesswork.
Produces a structured history of the run.
02Evaluation
Evaluate the final outcome and the complete trajectory to detect incorrect, incomplete, or risky work, even when the run finishes without an error.
Turns a trace into a clear failure signal.
03Harness
The Repair Harness writes a scoped candidate rule into the agent’s workspace, tests it against real tasks, promotes rules that improve results, and retires those that do not.
Converts failure evidence into reusable learning.
Capture model calls, tool use, sub-agent activity, decisions, timing, and outputs across the full trajectory. Every evaluation and repair begins with trace evidence, not guesswork.
Produces a structured history of the run.
PandaProbe gives agents machine-readable tools, persistent memory, and controlled environments to learn from failures without waiting on a human.
01
PandaProbe Cloud is exposed through a structured CLI used by the Repair Harness. Agents can inspect traces, run evaluations, retrieve scores, and manage supported workflows without navigating a human dashboard.
02
The Repair Harness maintains a persistent workspace of learned rules organized by task, workflow, or domain. Agents connected to the same workspace can discover and reuse relevant fixes across turns, sessions, and multi-agent workflows.
03
The Repair Harness validates candidate fixes through developer-controlled task replay or live trials. Restricted tools keep execution bounded, and only fixes that improve evaluated outcomes become trusted rules.
Build agents that remember, validate, and improve.
Start buildingKeep your orchestration and execution loop. PandaProbe adds tracing, evaluation, and harness around the stack you already operate.
Keep your stack. Add a repair loop.
View integrationsDeploy PandaProbe in your data center, your cloud account, or locally without changing how your agents integrate.
Self-host on bare metal or your own Kubernetes. Zero data leaves your perimeter.
Deploy to AWS, GCP, or Azure inside your account. BYOC from day one.
Run the full stack on a workstation for offline dev, demos, or sensitive work.
Need PandaProbe inside your infrastructure?
Talk to usNo credit card required. Scale as you grow.
For hobbyists getting started.
Get StartedFor developers and small teams.
Get StartedFor scaling projects.
Get StartedFor large organizations.
Talk to FoundersSelf-host all core PandaProbe features for free without any limitations.
Deploy NowNeed a custom plan? Contact us
Full pricing detailsConnect your first agent to PandaProbe and turn every failure into a validated fix.
Create your free PandaProbe Cloud account.
Create an API key from the dashboard.
Let your coding agent drive the wireup.
Start free and scale as you grow.
Sign up freeHow PandaProbe enables safe self-repair, who controls it, and how to deploy it.
Join the builders turning agent failures into validated learning that every relevant run can reuse.
Traces and evaluations processed
2 Million+
—stars
Recognized by builders on Product Hunt.
