Trace, Diagnose and Prevent AI Agents Unpredictability

Debug Less, Deploy More

DataGusto provides step-by-step visibility into why AI agents fail—not just when they fail—with automated guardrails that prevent issues in real-time.

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Trace

All you need to reproduce enviroment, in one place

datagusto automatically captures every detail of your agent runs—OS settings, library versions, GPU configurations, random seeds, hyperparameters, model checkpoints, prompts, and datasets. With full Git integration and precise environment snapshots, you can replay any scenario instantly.

Automatically records complete environment details (OS, Python version, libraries, CUDA, GPU drivers)

Tracks unique identifiers for model versions (checkpoint hashes, version names)

Versions all training and inference datasets, prompts, and reference data through unique IDs or hashes

Record versions of additional independent systems (such as front-end applications or shared back-end services)

Diagnose

Instantly Pinpoint Failures—Debug in Minutes, Not Days. Ready for Scale from Day One.

datagusto automatically logs every step of your multi-agent workflows, providing clear, real-time visibility into where—and why—failures occur.

Iinstantly highlights problematic steps, clearly indicating where agent logic or output went wrong

Provide clear, actionable recommendations when issues occur

Generate transparent reports detailing issues and suggested changes

Prevent

Bigger Data, More Agents, More Unpredictable Incidents—Stop Before Customers See Them

datagusto’s Guardrail instantly identifies issues like hallucinations or compliance breaches during agent execution. It automatically takes corrective action—like model rollback, fallback responses, or escalation—to prevent errors before they reach users, reducing risk without manual intervention.

Instantly identifying errors, anomalies, or compliance violations as they occur

Automatically intervenes by switching to fallback models, inserting disclaimers, or limiting responses to prevent problematic outputs.

Initiates GitHub rollback pull requests or reverts to stable versions of models or prompts when performance drops or governance rules are breached.