alt Hacker NewsI’ve had success with writing eg shell scripts that have a deterministic scaffold for the thing I’m actually trying to do, then call off to the agent for only the things that actually need it. For example I was trying to find a race condition flaky bug in my code, so the shell for loop ran the build N times, and called out to the agent to analyze the build logs if the tests failed, then the shell would ping me on slack when it was done, so the whole thing could run in the background.
I’ve been thinking more about how this deterministic + agents style could work, it’s kinda like the analogy of factories in the 1800s going from the central shaft to electric motors on each desk (where the central shaft in this case is a chat window, and the motor on a desk is calling the llm api from wherever you want just like a normal api call)
Of course, maybe in a few months the agents would just be reliable enough to do the shell part on their own too, but we’ll just have to wait and see
I like your analogy. The main problem though is context and keeping it clean as much as possible as long a parallelization. This is what drove to build this tool: having control of everything that the LLMs will do, controlling all with one main planner that orchestrates the rest. This way we can have cheaper LLMs with a short context window used (less intelligence degradation) while still obtaining the same objective. And again, you can have a clear picture of everything structured as tasks. until we can get to rely on huge swarms of agents (tasks) being directed on the planner alone I don't see how we can get a better framework.