The State of Context Engineering
- ID
- 39565
- Status
- summarized
- Published
- 26 Oct 2026, 4:32 AM
- Fetched
- 26 Oct 2026, 10:39 PM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://www.dbreunig.com/2026/10/24/the-state-of-context-engineering.html
- Source URL
- https://hnrss.org/best
Summary
- Score
- 7.0
- Created
- 26 Oct 2026, 10:40 PM
- Tags
- Audience
- developersvibe_codersai_ml_learnersai_agent_userssaas_startup_founders
What happened
In an AGNTCon talk, dbreunig framed context engineering as knowing when to be deterministic versus probabilistic, and mentioned a forthcoming O’Reilly book, The Context Engineering Handbook. The transcript traces the post-2024 “RAG is dead” hope that huge context windows would remove the need for selective context or agentic systems, citing the jump from ~128,000-token models to million-token models starting with Gemini 1.5. It says Gemini 1.5 Pro marketing emphasized a 1M-token window and teased a 10M-token lab model, while Llama 4 Scout shipped a 10M-token window but “wasn’t quite what we hoped” because inference was a challenge.
Why it matters
If you are choosing an agent or RAG architecture, do not treat a 1M- or 10M-token advertised context window as a substitute for selective context: the transcript pairs Llama 4 Scout’s 10M-token window with an inference challenge, so benchmark cost and latency and keep deterministic retrieval or filtering where it wins.
Discussion angle
Where in your agent stack should context handling be deterministic versus probabilistic, and how would you test an advertised 1M- or 10M-token window against real inference cost and latency?