Probe
Probe provides an agent-native research platform that unifies compute management, experiment tracking, and team context in a single interface. The platform automatically monitors runs for failures, alerts users with diagnostic evidence, and helps AI agents select and set up cost-effective compute resources. It connects projects, experiments, and runs in a shared view to reduce coordination overhead for AI/ML research teams.
- Artificial Intelligence
- Developer Tools
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Research slows down when compute, experiments, and context are fragmented across different tools and systems. Teams struggle to track runs, identify failures quickly, and coordinate findings, leading to wasted compute resources and slower scientific progress.
Solution
Probe unifies compute resources, experiment tracking, and contextual data into a single agent-native research platform. The system automatically monitors runs for failures and bad metrics, alerting users with the problem, evidence, and a direct link to the run. Probe assists agents in finding the cheapest compute that fits their needs, handling setup and launch automatically. The platform connects team projects, experiments, and runs in one shared view, enabling everyone to build on the same findings without additional status meetings. Benchmark results indicate that agent swarms using Probe find faster solutions by sharing context and providing clear visibility into agent activities.
Target Audience
Primary users are AI/ML research teams and labs that need to track experiments, manage compute resources, and coordinate research findings across multiple agents and team members.
Features
- Automatic run monitoring that detects failed runs and bad metrics, then alerts users with diagnostic evidence and a link to the run
- Agent-assisted compute selection that finds the cheapest fitting compute, handles setup, and launches experiments automatically
- Shared canvas view connecting projects, experiments, and runs across the team for collaborative visibility
- Integration with GitHub, Weights & Biases, and other experiment-tracking tools for importing run data
- Support for files, artifacts, checkpoints, traces, prompts, and assistant conversations within the platform
- Privacy-preserving architecture that does not use customer data to train general-purpose models