Simple discovery of the provider's default schemas is possible without any credentials (since they are built into the binary). If you want it to fetch customizations of your specific instance, give it credentials to your org.
We found after deploying many enterprise agents that letting the model choose tools at run time can cause problems. The agent holds the credential and sometimes chooses the wrong tool. Using the same tool every time prevents this.
Absolutely in love. I'll be testing this after the daily grind.
I've had nothing but success with converting daily work into "notes" that then translate into runnable, deterministic application CLIs to completely sidestep "what do I need to say to you to make you do the thing??"
I'm interested in how this spreads across enterprise flows, because the issue is always discovery and usability.
How deep do you usually go with CLI composition and layering? Do you tend to find a flat list of commands and sub command help works most? Have you experimented with connecting CLIs Linux-pipe-style as if it was a dynamic application in the OS?
The objectives are to reduce/eliminate as much inference variability as possible. A side benefit is that inference costs collapse as well.
It is used internally for what we call 'compiled workflow agents' where no inference is necessary. An agent composer determines the exact command at design time. That is part of a discovery loop that can introspect the service to construct the command.
The repo has more details. https://github.com/agent-cli-framework/aclif
We found after deploying many enterprise agents that letting the model choose tools at run time can cause problems. The agent holds the credential and sometimes chooses the wrong tool. Using the same tool every time prevents this.
Absolutely in love. I'll be testing this after the daily grind.
I've had nothing but success with converting daily work into "notes" that then translate into runnable, deterministic application CLIs to completely sidestep "what do I need to say to you to make you do the thing??"
I'm interested in how this spreads across enterprise flows, because the issue is always discovery and usability.
How deep do you usually go with CLI composition and layering? Do you tend to find a flat list of commands and sub command help works most? Have you experimented with connecting CLIs Linux-pipe-style as if it was a dynamic application in the OS?
The objectives are to reduce/eliminate as much inference variability as possible. A side benefit is that inference costs collapse as well.
It is used internally for what we call 'compiled workflow agents' where no inference is necessary. An agent composer determines the exact command at design time. That is part of a discovery loop that can introspect the service to construct the command.
More here. https://www.promptone.ai/resources/downloads/
I have to build a cli for every provider? Seems like a lot of effort.
Yes. But any coding agent can do this in a matter of minutes. There is an example prompt and corresponding Skill.md in the repo.
https://github.com/agent-cli-framework/aclif/blob/main/docs/...
https://github.com/agent-cli-framework/aclif/blob/main/.clau...