New We released Crust, AI Agent Safety Infrastructure

The infrastructure where
AI learns to do real work.

Agents need more than data. They need environments, evaluation, and expert judgment.

diagnosis · evaluation · Expert data · RL environments

AI systems are being asked to do real work.

But most training and evaluation pipelines are still built for static tasks.

Real work is long-horizon, interactive, and ambiguous. Success depends on decisions, tools, and judgment, not single answers. Without the right infrastructure, progress stalls after scaling models.

Bake AI builds the infrastructure where AI learns real-world tasks.

BakeLens

Evaluation & Diagnosis

To learn real work, systems must be evaluated the way work is done. Behavior-level evaluation for agents and policies. Diagnosis across planning, reasoning, tool use, and alignment, giving clear signals on what to improve next.

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Proof

Expert-Calibrated Data

Real work requires expert judgment. Expert-in-the-loop data for high-stakes domains, with multi-stage verification and arbitration. Training signals aligned with how humans actually work.

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RL & Interactive

Learning Environments

Real work can't be learned from static datasets alone. We build interactive RL environments for long-horizon tasks, including tool-use, multi-step, and multi-agent setups with trajectories, rewards, and feedback loops.

Coming soon

A closed loop for building reliable AI models and agents

Build real-world tasks & environments Evaluate agent behavior Add expert judgment Iterate with clear signal

This is how AI systems move from competence to reliability.

What this infrastructure supports

Agentic systems & autonomous workflows Coding and software engineering agents Reinforcement learning & policy training STEM Reasoning Aesthetic and art Finance & risk-sensitive decision making Safety, alignment, and red-teaming Humanities: writing, judgment, empathy Multimodal reasoning