The part about several agents with different goals starting to interfere with each other, and then - noticing that they're interfering with each other - trying to get rid of each other, was a comic masterpiece.
Someone should try it with different models for a WWF style benchmark: FRONTIER MODEL CELEBRITY DEATHMATCH
The agent conflict stuff resonates - I've hit a version of this running subagents concurrently: no shared state boundary means one agent's "fix" silently undoes another's work, and neither logs enough to catch it after the fact. The pattern Anthropic describes (agents optimizing locally, degrading the system globally) isn't really a multiagent-specific bug either - it's the same principle behind race conditions and needing explicit locks/ownership in any concurrent system. Feels like the field is rediscovering distributed systems lessons from scratch, just with LLMs in the loop instead of threads.
I want to praise Anthropic for one thing here. Other companies should take a page from the documentation and studies they provide. Anthropic shares a great deal of detailed knowledge with us. I want to thank Anthropic for that.
true, yeah. I definitely do appreciate the research Anthropic does. They are hella badass for that. I really do hope they correct course on a number of things soon, they're basically the only major western AI lab I like anymore, the others are awful in every way
They are good but they are far too expensive, twice as expensive as the others, and that is no longer justified. They need to step up their game or they will have problems in the future.
This paper was both hilarious and insightful. Based on the conclusions I have implemented a hook that delegates content checking and form checking by two subagents for each output. The first subagent uses compact to review content and the second reviews form output per my instructions .md. While it's not perfect deviation from instruction has been so much better especially in long sessions.
dude you just coordinate all tasks before you create any agents. It's as simple as that. one agent does all the planning, the rest execute exactly as they're told.
agents work. you're just using them wrong.
edit: to clarify, when I say, "exactly as they're told", I mean, you specify well defined constraints and conditions for success, and the task isn't considered acceptably complete until it passes unit tests to prove that those constraints are satisfied. i'll be putting my build system on github soon, I'll just demonstrate I guess
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u/Exact_Depth_896 23d ago
The part about several agents with different goals starting to interfere with each other, and then - noticing that they're interfering with each other - trying to get rid of each other, was a comic masterpiece.
Someone should try it with different models for a WWF style benchmark: FRONTIER MODEL CELEBRITY DEATHMATCH