Bloomberg

What We Learned Deploying AI within Bloomberg’s Engineering Organization – Lei Zhang, Bloomberg

Dec 16, 2025 · 18:21

Lei Zhang, Bloomberg's Head of Technology Infrastructure Engineering, shares lessons from deploying AI across 9,000+ software engineers, emphasizing that real ROI comes from maintenance and incident response rather than greenfield coding. He details how Uplift Agents automate refactoring patches and Incident Response Agents provide unbiased troubleshooting, both leveraging MCP servers. To avoid duplication, Bloomberg built a Paved Path with a model gateway, tool discovery hub, and standardized deployment. Adoption was boosted by integrating AI into onboarding training and across tech communities, while data showed leadership lagging behind individual contributors—prompting targeted leadership workshops. Zhang concludes that AI changes the cost function of engineering, enabling previously expensive tasks to become cheap, forcing a reexamination of what constitutes high-quality software engineering.

Scaling Agents for Gen AI Products - Anju Kambadur, Bloomberg Head of AI Engineering

Apr 1, 2025 · 19:38

Bloomberg's Head of AI Engineering, Anju Kambadur, details the company's shift from building proprietary LLMs to leveraging open-source models for agentic products, emphasizing that scaling agents requires accepting inherent fragility and building resilient guardrails rather than seeking perfect upstream systems. Drawing on Bloomberg's daily scale—400 billion structured data ticks, over a billion unstructured messages, and 40+ years of history—Anju explains how their 400-person AI team organized into 50 teams across London, New York, Princeton, and Toronto. He argues that agents must be semi-autonomous with non-optional guardrails (e.g., preventing financial advice, ensuring factuality) because compounding errors from evolving APIs and LLMs demand self-contained safety checks. The talk uses the example of a research analyst agent that factors query understanding, answer generation, and guardrails into separate components, reflecting an org structure that collapses vertically for fast iteration on single agents then later introduces horizontal teams (like guardrails) for optimization and cost reduction. Anju stresses that readiness to build horizontal teams comes after multiple…

Understanding AI Stakes to Break Production Code: Philip Rathle

Dec 31, 2024 · 23:24

Philip Rathle, CTO of Neo4j, argues that the level of 'stakes' in an AI application determines the production barriers and appropriate solutions, from low-stakes summarization to high-stakes uses requiring knowledge graphs and human-in-the-loop systems. He distinguishes pilot (full autonomy) from co-pilot (human oversight), showing how vector RAG solves moderate stakes but fails for high-stakes needs like tightening bolts on a 737 MAX 9. He promotes Graph RAG for deterministic reasoning and fact retrieval. Attendees share learnings: using LLMs to write code rather than reason directly, adapting to user keyword habits, focusing on few projects with achievable accuracy, building eval pipelines early, and for regulated loans offering three options with explanations instead of a single recommendation.