Open-Source

The Stack for
Self-Repairing Agents

PandaProbe traces every run, evaluates outcomes, and turns failures into validated fixes so agents can learn and recover on their own.

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Why Self-Repair[—/—]

Agents should not fail the same way twice.

Most tools turn failures into information for humans. PandaProbe turns them into validated fixes for agents.

01Validated learning

Failures become validated learning

Trace evidence and outcome evaluation give the repair harness a grounded starting point. Candidate rules are tested against real tasks before becoming reusable learning.

02Higher task success

Future task success improves

Validated rules turn prior failures into fixes for the next relevant run, helping agents avoid known mistakes instead of rediscovering them.

03Less intervention

Agents need less manual repair

As proven fixes accumulate, familiar failures no longer need to begin another manual debugging cycle. Engineers keep policy control without handling every repair.

04Fleet economics

Companies can operate larger, cheaper agent fleets

Reusable learning can be scoped and shared across workflows, allowing more work to run on smaller, lower-cost models with less operational overhead.

How it works[—/—]

Three layers turn failures into proven fixes.

Tracing records what happened, evaluation determines whether agent was successful, and the harness creates and validates reusable fixes.

01Tracing

Record everything the agent did.

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.

Tracing Docs

02Evaluation

Determine whether it worked.

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.

Evaluating Docs

03Harness

Turn the failure into a proven fix.

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.

Harness Docs

Tracing

Evaluation

Harness

Record everything the agent did.

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.

Tracing Docs

Why PandaProbe[—/—]

Built for agents to use, not just humans to watch.

PandaProbe gives agents machine-readable tools, persistent memory, and controlled environments to learn from failures without waiting on a human.

Control plane agents can use

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.

Memory that survives the run

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.

A safe place to prove repairs

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.