The Job is Knowing When to Be Deterministic & When to Be Probabilistic. This week, I gave a short talk at AGNTCon titled, "Context Fails & Fixes," that functioned as a sort of "State of Context Engineering." How did context engineering emerge, where has it gone, and where might it go in the future? As model makers target new capabilities, they inadvertently create challenges we, as users and harness builders, must overcome. Below are the slides and a lightly edited transcript.
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Hi, everyone. Thanks for joining me today. We're going to walk through context fails and fixes. I have a book coming out in a bit, The Context Engineering Handbook. When I first talked to the team at O'Reilly, back in June 2025, they asked me a very reasonable question: "How do we know this won't become obsolete in a few weeks?" The trouble with AI textbooks is that many age extraordinarily quickly. I said, I don't know, but I'm optimistic.
Giving clear instructions about what to do and what not to do, and ensuring those instructions are consistent and not noisy, will forever be a challenge with AI, no matter how good it gets. We have coworkers, partners, and other people we know and interact with who are sentient. They pass the Turing test, they are "AGI," and yet: we still have a hard time conveying instructions, ideas, and concepts among each other. Context engineering will continue to be hard. But it will be hard in unique ways, because models aren't people.
We can make assumptions about how models think or interpret things (use whatever anthropomorphizing word you like), but it helps to understand how they're built, optimized, and trained. Their unique attributes and construction create a unique set of failure modes we need to reckon with. So let's review how we got here. Let's flash back to the end of 2024. If you remember, this was the height of the "RAG is dead" moment. The hope was: if we can just make the context window large enough, we can put the entire internet in it. We can put everything in it. We don't have to be selective. We don't have to build agentic systems. We can fit it all in there, and AI will be solved.
Model builders started aiming at long context capabilities and we had a big leap from ~128,000-token context window models to million-token context window models, starting with Gemini 1.5. If you look at the marketing material for Gemini 1.5 Pro, it was mostly about the million-token context window. They were even teasing a 10-million-token context window model they had in the lab, that would come out soon. But they got beaten to the punch. Llama 4 Scout came out with a 10-million-token context window, and well, it wasn't quite what we hoped. Inference was a challenge, and Scout never really caught on, and we never got another Llama. But we still got a million tokens. That's pretty good. We'll take it.
So this was the brief at the time: just put everything in the context and let the model sort it out. But with more context, we had room for more problems. Long contexts are not a panacea. When I wrote about this, I identified four key failure modes. Context poisoning is when something gets into the context that shouldn't be there. It could be a hallucination or a bad fact, and the model can't get past it. Context distraction is when the context grows so long that the model starts to focus on the context over its base knowledge. We can see this in some really interesting ways. Context confusion is when extraneous facts—things that don't relate to the problem I'm working on—come in and influence the model in ways we don't fully understand. This one's really pernicious. We're not going to dig into it today, but it's one of my favorite failure modes. The model makes assumptions about who you are, and those assumptions influence the answer. Finally, context clash is when tool calls, skills, and other information within your harness start to confl...
Source: Hacker News · Summarized by HeadlinesBriefing