How Bridgewater Built an AI Analyst That Does Hours of Expert Research in Minutes

Bridgewater explains the architecture of PAT, an AI analyst deployed to hundreds of internal investors that compresses hours of expert research into minutes. What makes this worth your time is the engineering specifics: a compiler-style split between a chat agent and a coding agent, correctness enforced in the architecture rather than by agent orchestration, and a 'Teach' button that turns user feedback into pull requests. It's one of the most concrete enterprise agent case studies I've seen.

• Separating the planning agent (natural language) from the coding agent (compiles plans to Python) makes programming an invisible implementation detail — and lets 20-task plans generate in parallel, roughly 4x faster than Claude Code.
• Search accuracy jumped from 50% to 90% by embedding human-like inspection logic: agents verify frequency, currency, and whether values match priors, not just series names.
• 95% reproducibility comes from enforcing correctness in the architecture (static analysis, DAG validation) rather than trusting agents to remember to validate.
• Every user interaction feeds a continuous-learning loop: autonomous agents review conversations, create benchmarks, and submit PRs to improve the system for everyone.

Part of Issue Nº 003: Evals Are the New PRDs: How Anthropic, Bridgewater, and SonderMind Actually Ship AI