Session Overview
Discover how hidden context costs derail AI agent architectures and how event streaming patterns fix them.
In this talk, Dinesh Chandrasekhar (Chief Analyst at Stratola Research) dives into the "Context Tax"—the invisible compute, token, and warehouse bills accrued when agents repeatedly process the same data. Learn why cheap cache reads break when prompts are structured incorrectly, how compaction silently drops critical business rules, and how streaming patterns (like volatility tiering and CDC delta feeds) keep agent memory fresh without blowing up token budgets.
What You'll Learn
- The 5 context taxes: Cache miss, churn, recall, re-query, and staleness/drift
- Why subtle prompt assembly choices trigger full cache rewrites
- The dangers of naive compaction: Why safety rules drop after multiple rounds
- The 2x2 framework: Mapping context placement against data change frequency and agent read rates
- 5 streaming architecture patterns to optimize agent context and reduce token waste
- Key telemetry metrics to measure context efficiency and freshness lag
Chapters
- 00:00 — Introduction & the AI context cost problem
- 03:07 — Prompt layout mechanics & cache bust pitfalls
- 06:40 — The 5 context taxes: Cache, churn, re-query & staleness
- 16:08 — The 2x2 context placement framework
- 19:42 — 5 streaming architecture patterns to cut token waste
- 25:51 — Auditing context efficiency metrics & wrap-up
