Autometed Agent Optimization
Automated Agent Optimization
Close the self-improvement loop: generate targeted simulation data to iteratively fine-tune and optimize agents without manual cycles.
Targeted Training Data Generation
Run large-scale simulations focused on weak spots (drift, tool failures, multi-hop errors). Export high-signal preference pairs, critique-revise triples, or judge-labeled traces directly for DPO, SFT, or RL.
Prompt & Workflow Iteration
Bootstrap GEPA-style optimization or DSPy-like program refinement. Snowglobe generates fresh, domain-specific multi-turn data that evolves with your agent—keeping prompts and flows current as behavior changes.
Continuous Outer Loop
Feed production drift signals back into simulations. Spawn new risky scenarios, retrain judges, generate updated datasets - creating a self-improving cycle where responses are, more reliable with every iteration.
Static data breaks agents
Agents degrade fast without fresh data. Public or static datasets go stale immediately.
Close the iteration loop in hours, not weeks
Manual iteration takes weeks per cycle. Snowglobe closes the loop in hours—sim → detect → data → retrain.
Surface the long-tail risks humans miss
Real self-improvement needs infinite scale on edge cases. Simulations make that possible programmatically—no more waiting for users to find the next flaw.
Built for Production AI Teams
For teams building production AI systems who need evaluation data that's realistic, comprehensive, and fast.
~500 scenarios in 30 minutes
Replace weeks of manual curation with automated generation
Enterprise context grounding
Scenarios reflect your domain, terminology, and user patterns
Live system interaction
Tests adapt to actual AI responses, not assumed behavior
Multi-turn conversation support
Evaluate complex dialogue flows, not single-exchange Q&A
Programmatic edge case discovery
Systematically explore failure modes humans wouldn't think to test
Risk quantification
Move from "we tested it" to "here's our measured risk surface"