I really like this final sentence from the presentation. When I read this phrase, I realized that it captured one of the core dynamics that I've been using to figure out when and how to apply AI to a task. I often find myself trying to figure out if it is better to A) ask the AI to do a task for me or B) ask the AI to build something that can do the task for me. A major component of that answer comes down to how deterministic or probabilistic the task is.
Mmm, to my mind, isn't this mixing completely different things? I thought poisoned context is stuff in the context that makes the model behave poorly. But if it's in the weights, that's just... model issues.
I don't find this to be true, at least with the newer models. I also find that the models fill up to 250k very quickly (presumably mostly by looking through the codebase). Without that context I find the models perform badly. I also don't really notice that large of a change until around 700-800k context and even then it isn't as bad as it used to be. I suspect, that (especially for coding), the long horizon RLVR tasks have helped with this quite a bit.
I've taken to just manually making it write a handoff file and clearing context completely. I'm pretty much manually compacting instead of my harness doing it. Feels weird, but it does work.
I have auto-compact on at 400k
I try to manually manage as much of the context as possible. Most of the time, I limit myself to a context window of 200k, and it works great for my use cases.
From my understanding, if we have turns [A, B, C, D, E] and we decide that turn C is useless context, then we lose our cache for the [D, E] segment. (plz correct me if im wrong, I havent tested this)
Personally, I pay no mind to session state nowadays. I exclusively use provider native hosted compaction, which if you believe the providers, is the way to go. For context management- I spend most of my effort on the things which dump output back to the model.
If anyone has any good reads on benchmarking OpenAI and Anthropic hosted/native compaction against custom stuff, I would love to read!
Personally, I use a harness extension that auto-compacts my context just before the cache expires. Combined with the default compact-when-full, this ends up being pretty much always what I want. It's one less thing to think about.
Since I run everything local, speed benefits from keeping context low ~128k. This gets me through a single agent on multiple tasks through the same repo. It seems to break around 6M but that's more probablistic.
KV cache is definitely valuable but context poisoning happens regardless of size and it's probability increases regardless of intelligence. So at some point you're going to suffer from context overload regardless.
That’s really true, but on the other hand, if there’s too little information, it also ends up working in strange ways. Most AGENT code anti-patterns work well at a medium amount of context, but when there’s too much or too little information, they don’t behave properly.
Interestingly, the artifacts include not just code but alao retroactive design and retroactive “execution plan”. I feed them to another agent to validate the approach and resume work with fresh context and update mission statement.
Unexpectedly, design doc and an execution plan are now outcomes of coding process, not inputs to it. I’m not sure how to think about this.