To ensure AI reliability, Salesforce builds environments that mimic enterprise CRM workflows, not game worlds. They use synthetic data and introduce corner cases like background noise, accents, or conflicting user requests to find and fix agent failure points before deployment, closing the "reality gap."

As AI generates more code than humans can review, the validation bottleneck emerges. The solution is providing agents with dedicated, sandboxed environments to run tests and verify functionality before a human sees the code, shifting review from process to outcome.

Training AI agents to execute multi-step business workflows demands a new data paradigm. Companies create reinforcement learning (RL) environments—mini world models of business processes—where agents learn by attempting tasks, a more advanced method than simple prompt-completion training (SFT/RLHF).

Many AI projects fail to reach production because of reliability issues. The vision for continual learning is to deploy agents that are 'good enough,' then use RL to correct behavior based on real-world errors, much like training a human. This solves the final-mile reliability problem and could unlock a vast market.

Beyond supervised fine-tuning (SFT) and human feedback (RLHF), reinforcement learning (RL) in simulated environments is the next evolution. These "playgrounds" teach models to handle messy, multi-step, real-world tasks where current models often fail catastrophically.

Companies like OpenAI and Anthropic are spending billions creating simulated enterprise apps (RL gyms) where human experts train AI models on complex tasks. This has created a new, rapidly growing "AI trainer" job category, but its ultimate purpose is to automate those same expert roles.

A niche, services-heavy market has emerged where startups build bespoke, high-fidelity simulation environments for large AI labs. These deals command at least seven-figure price tags and are critical for training next-generation agentic models, despite the customer base being only a few major labs.

As reinforcement learning (RL) techniques mature, the core challenge shifts from the algorithm to the problem definition. The competitive moat for AI companies will be their ability to create high-fidelity environments and benchmarks that accurately represent complex, real-world tasks, effectively teaching the AI what matters.

OpenPipe's 'Ruler' library leverages a key insight: GRPO only needs relative rankings, not absolute scores. By having an LLM judge stack-rank a group of agent runs, one can generate effective rewards. This approach works phenomenally well, even with weaker judge models, effectively solving the reward assignment problem.