←back to thread

What to build instead of AI agents

(decodingml.substack.com)
233 points giuliomagnifico | 1 comments | 03 Jul 25 00:02 UTC | HN request time: 0.224s | source
Show context
mccoyb ◴[03 Jul 25 01:03 UTC] No.44450552[source]▶
Building agents has been fun for me, but it's clear that there are serious problems with "context engineering" that must be overcome with new ideas. In particular, no matter how big the context window size is increased - one must curate what the agent sees: agents don't have very effective filters on what is relevant to supercharge them on tasks, and so (a) you must leave *.md files strewn about to help guide them and (b) you must put them into roles. The *.md system is essentially a rudimentary memory system, but it could get be made significantly more robust, and could involve e.g. constructing programs and models (in natural language) on the fly, guided by interactions with the user.

What Claude Code has taught me is that steering an agent via a test suite is an extremely powerful reinforcement mechanism (the feedback loop leads to success, most of the time) -- and I'm hopeful that new thinking will extend this into the other "soft skills" that an agent needs to become an increasingly effective collaborator.

replies(4): >>44450945 #>>44451021 #>>44452834 #>>44453646 #
zmgsabst ◴[03 Jul 25 02:06 UTC] No.44450945[source]▶
I’ve found managing the context is most of the challenge:

- creating the right context for parallel and recursive tasks;

- removing some steps (eg, editing its previous response) to show only the corrected output;

- showing it its own output as my comment, when I want a response;

Etc.

replies(2): >>44451001 #>>44451616 #
1. ModernMech ◴[03 Jul 25 04:24 UTC] No.44451616[source]▶
It's funny because things are finally coming full circle in ML.

10-15 years ago the challenge in ML/PR was "feature engineering", the careful crafting of rules that would define features in the data which would draw the attention of the ML algorithm.

Then deep learning came along and it solved the issue of feature engineering; just throw massive amounts of data at the problem and the ML algorithms can discern the features automatically, without having to craft them by hand.

Now we've gone as far as we can with massive data, and the problem seems to be that it's difficult to bring out the relevent details when there's so much data. Hence "context engineering", a manual, heuristic-heavy processes guided by trial and error and intuition. More an art than science. Pretty much the same thing that "feature engineering" was.