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New Research Reassesses the Value of Agents.md Files for AI Coding

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Re: New Research Reassesses the Value of Agents.md Files for AI Coding

#4
post #3

The research mostly points to LLM-generated context lowering performance. Human-generated context improves performance, but any kind of AGENTS.md file increases token use, for what they say is "fake thinking." More research is needed.

Agree. Also, sometimes I intentionally want the agent to do something differently to how it would naturally solve the problem. For example, there might be a specific design decision that the agent should adhere to. Obviously, this will lead to slower task completion, higher inference costs etc. because I’m asking the agent not to take the path of least resistance.

This kind of benchmark completely misses that nuance.

Re: New Research Reassesses the Value of Agents.md Files for AI Coding

#6
post #3

The research mostly points to LLM-generated context lowering performance. Human-generated context improves performance, but any kind of AGENTS.md file increases token use, for what they say is "fake thinking." More research is needed.

I’d say that it needs to be maintained and reviewed by a human, but it’s perfectly fine to let an LLM generate it.

Re: New Research Reassesses the Value of Agents.md Files for AI Coding

#8
I suspect AGENTS.md files will prove to be a short-lived relic of an era when we had to treat coding agents like junior devs, who often need explicit instructions and guardrails about testing, architecture, repo structure, etc. But when agents have the equivalent (or better) judgement ability as a senior engineer, they can make their own calls about these aspects, and trying to "program" their behaviour via an AGENTS.md file becomes as unhelpful as one engineer trying to micro-manage another's approach to solving a problem.

Re: New Research Reassesses the Value of Agents.md Files for AI Coding

#10

I suspect AGENTS.md files will prove to be a short-lived relic of an era when we had to treat coding agents like junior devs, who often need explicit instructions and guardrails about testing, architecture, repo structure, etc. But when agents have the equivalent (or better) judgement ability as a senior engineer, they can make their own calls about these aspects, and trying to "program" their behaviour via an AGENTS…

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