first I jot down my notes then I work with one agent to write a plan. this is just the first draft.
Once I have a draft, I start to leave comments to ask questions and launch multiple agents to research and refine parts of the plan.
(optionally share the plan with my team for feedback)
then have multiple agents work on implementing parts of the plan
Ref is great for this because it can orchestrate agents and you can track all the progress on one place. It's hosted so you just work from a browser like Google Docs but can connect to local agents too
I've tried it a few times, but none of them went well. There are a few recurring issues:
1.When I try agent based orchestration and multi tasking, some parts work well overall, but certain areas keep repeating local issues.
2.So I tried having AI review each other's work and set up guardrails, like stopping when certain values or patterns repeat. That didn't work well either.
3.In the end, most of the time, if I define what the 'result' should look like and run it, the system eventually reaches that outcome.
The problem for me is that the orchestration code itself doesn't fit the style I like, so it's hard to look at. Using orchestration code basically means you're trusting the AI to handle everything without looking at the code. A lot of people say results come out quickly and work well, and models around the SOL level seem to perform decently. But for me, I haven't been able to get good results regardless
i like to work via Plans in ref.tools
first I jot down my notes then I work with one agent to write a plan. this is just the first draft.
Once I have a draft, I start to leave comments to ask questions and launch multiple agents to research and refine parts of the plan.
(optionally share the plan with my team for feedback)
then have multiple agents work on implementing parts of the plan
Ref is great for this because it can orchestrate agents and you can track all the progress on one place. It's hosted so you just work from a browser like Google Docs but can connect to local agents too
I've tried it a few times, but none of them went well. There are a few recurring issues:
1.When I try agent based orchestration and multi tasking, some parts work well overall, but certain areas keep repeating local issues.
2.So I tried having AI review each other's work and set up guardrails, like stopping when certain values or patterns repeat. That didn't work well either.
3.In the end, most of the time, if I define what the 'result' should look like and run it, the system eventually reaches that outcome.
The problem for me is that the orchestration code itself doesn't fit the style I like, so it's hard to look at. Using orchestration code basically means you're trusting the AI to handle everything without looking at the code. A lot of people say results come out quickly and work well, and models around the SOL level seem to perform decently. But for me, I haven't been able to get good results regardless