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Cord: Coordinating Trees of AI Agents
31–40 of 93 posts
Re: Cord: Coordinating Trees of AI Agents
#32Re: Cord: Coordinating Trees of AI Agents
#33Feels very AI written in a way that makes it annoying to read with all the repetitive short sentences. Neat concept though, would be cool to see some tests of performance on some tasks.
Re: Cord: Coordinating Trees of AI Agents
#34Re: Cord: Coordinating Trees of AI Agents
#35Not exactly a surprise Claude did this out of the box with minimal prompting considering they’ve presumably been RLing the hell out of it for agent teams: https://code.claude.com/docs/en/agent-teams
Re: Cord: Coordinating Trees of AI Agents
#36all of these frameworks will go away once the model gets really smart. it will just be tool search, tools, and the model in the short run, ive found the open ai agents one to be the best
Re: Cord: Coordinating Trees of AI Agents
#37I wonder if the “spawn” API is ever preferable over “fork”. Do we really want to remove context if we can help it? There will certainly be situations where we have to, but then what you want is good compaction for the subagent. “Clean-slate” compaction seems like it would always be suboptimal.
Re: Cord: Coordinating Trees of AI Agents
#38I wonder if the “spawn” API is ever preferable over “fork”. Do we really want to remove context if we can help it? There will certainly be situations where we have to, but then what you want is good compaction for the subagent. “Clean-slate” compaction seems like it would always be suboptimal.
This is my question also but a bit different. Is there any reason to explicitly have this binary decision. Instead of single primitive where the parent dynamically defines the childs context. Naturally resulting in either spawn or fork or anything in between.
Re: Cord: Coordinating Trees of AI Agents
#39Re: Cord: Coordinating Trees of AI Agents
#40Nice one. You should also try to make context query the first class primitive. Context query parameter can be natural language instruction how to compact current context passed to subagent. When invoking you can use values like "empty" (nothing, start fresh), "summary" (summarizes), "relevant information from web designer PoV" (specific one, extract what's relevant), "bullet points about X" etc. This way LLM can deci…