The article shows how real-world agentic AI deployments can blow through budgets because multi-step workflows use 5–30× more tokens per task than simple chatbots. It breaks down four hidden cost layers—LLM inference with re-sent context, context rot, tool orchestration, and infrastructure—and offers strategies to curb runaway spending before your production bill arrives.

• Agentic workflows use 5–30× more tokens per task than simple chatbot queries because each task triggers 10–20 model calls that reprocess the same context each time.
• Uber's Claude Code rollout to 5,000 engineers burned through the company's entire annual AI budget in one month, costing $500–$2,000 per engineer.
• Stanford research attributes 62% of agent inference costs to re-sending the same prompts and history on every call, and larger contexts also degrade model accuracy ("context rot"), triggering more retries.
• Routing simpler queries to cheaper, smaller models cut one team's monthly bill from $40,000 to $24,000, though falling per-token prices won't offset runaway consumption growth.

This issue covers the latest in data tooling and AI agents—from Databricks’ Agent Orchestrator and Spotify’s Vedder assistant to Feldera’s incremental view engine and LinkedIn’s MUSE semantic search. It also dives into managing agentic AI costs, building stable model ecosystems, and new open-source releases like Omnigent and Apache DataFusion 54.0.0.

• Spotify's Vedder only kept 12.5% of automatically mined query pairs after expert review, relying on human-curated clusters over the other 70,000-dataset schema-only retrieval
• Uber's 5,000 engineers burned through the annual AI budget by mid-April using Claude Code, showing token pricing becomes a task-economics problem once retries, orchestration, and governance overhead are counted
• DataFusion 54.0.0 added LATERAL joins, SQL lambdas, and spill-to-disk nested loops, pushing join speeds up to 50x faster in some cases
• Databricks' Omnigent unifies Claude Code, Codex, Pi, and in-house agents under one harness with shared security, cost limits, and session sharing