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A CLI doing all this is still a better UI for the agent though.

by barrkel 6d ago

0x696C6961 6d ago
What does that even mean?
- bdangubic 6d ago
  totally makes no sense. so weird after all this time people don’t “get” the value of MCPs, so weird
viccis 5d ago
What if the agent doesn't have a CLI?
- barrkel 5d ago
  You hobble the expressiveness of the LLM and reduce its capability. Think of an agentic harness as like a kind of body for the LLM. It gives it primitive inputs (read_file, web_search or whatever) and primitive outputs (edit file, respond to user, etc). Give it a command line environment (in a locked down sandbox, with as few or as many tools as you prefer), and you've given it a toolbox. It can do a whole lot more, faster and more efficiently. It can compose tools together. It makes fewer transcription errors manually shifting data around. It can tame verbosity with good protections in the harness and access to grep, sed and awk. It's really up to you how useful you want your agent to be.
  - viccis 5d ago
    Yeah but you're still assuming it's running on someone's machine with a CLI to even use.
pixlmint 5d ago
What if I don't want my agent to have terminal access though? I get the feeling many people here simply never worked with smaller models, those will confuse cli args really quickly once context expands, and then you have no idea what damage they might do. With MCP's, they get just the access they actually need. Is the Principle of least privilege just not something we want to apply anymore?
- barrkel 5d ago
  CLIs, run in a sandbox as tight as your preferred choosing, live in an ecosystem, where, via pipes and redirection, input and output can be easily manipulated. An agent can do similar things but more laboriously (and less token efficiently) via Python or similar but it would still live in a sandbox somewhere. Going without the sandbox means hobbling the LLM. It can do things directly but is less able to construct ad-hoc programs to deal with looping, conditionality, tame verbosity, connect tools together, and so on. It's a choice to not give the LLM an environment. As you say, it can be necessary if you're using dumb models. I don't find it particularly worth the trade most of the time.
  - pixlmint 5d ago
    If not even OpenAI can properly box in their models I definitely won't trust myself to do so with the very limited time available to me, and instead just use a standard that's already defined, and proven to work.

A CLI doing all this is still a better UI for the agent though.

0x696C6961 6d ago

What does that even mean?

bdangubic 6d ago
totally makes no sense. so weird after all this time people don’t “get” the value of MCPs, so weird

viccis 5d ago

What if the agent doesn't have a CLI?

barrkel 5d ago
You hobble the expressiveness of the LLM and reduce its capability. Think of an agentic harness as like a kind of body for the LLM. It gives it primitive inputs (read_file, web_search or whatever) and primitive outputs (edit file, respond to user, etc). Give it a command line environment (in a locked down sandbox, with as few or as many tools as you prefer), and you've given it a toolbox. It can do a whole lot more, faster and more efficiently. It can compose tools together. It makes fewer transcription errors manually shifting data around. It can tame verbosity with good protections in the harness and access to grep, sed and awk. It's really up to you how useful you want your agent to be.
- viccis 5d ago
  Yeah but you're still assuming it's running on someone's machine with a CLI to even use.

pixlmint 5d ago

What if I don't want my agent to have terminal access though? I get the feeling many people here simply never worked with smaller models, those will confuse cli args really quickly once context expands, and then you have no idea what damage they might do. With MCP's, they get just the access they actually need. Is the Principle of least privilege just not something we want to apply anymore?

barrkel 5d ago
CLIs, run in a sandbox as tight as your preferred choosing, live in an ecosystem, where, via pipes and redirection, input and output can be easily manipulated. An agent can do similar things but more laboriously (and less token efficiently) via Python or similar but it would still live in a sandbox somewhere. Going without the sandbox means hobbling the LLM. It can do things directly but is less able to construct ad-hoc programs to deal with looping, conditionality, tame verbosity, connect tools together, and so on. It's a choice to not give the LLM an environment. As you say, it can be necessary if you're using dumb models. I don't find it particularly worth the trade most of the time.
- pixlmint 5d ago
  If not even OpenAI can properly box in their models I definitely won't trust myself to do so with the very limited time available to me, and instead just use a standard that's already defined, and proven to work.