u/bsa-saa

I've used a simple review loop before: after the main agent makes some changes, a reviewer with new context is called, and the results are fed back to the main agent, repeating this cycle.

However, AI tends to always find problems when you ask them to find one, every additional review round wastes a lot of time. I've also tried skipping the cycle and just doing one round of review, but that feels like I'm just kidding myself.

How do you strike a balance between accuracy and efficiency?

I’ve been experimenting with a multi-agent orchestration model designed by my agent.

The core concept is a WorkItem DAG — basically an ordered dependency graph similar to a structured todo list.

- A Planner generates the execution DAG

- Worker agents execute work items mechanically along the graph

- If unexpected situations happen, a RePlanner patches the DAG and creates a new execution path

So agents themselves are intentionally “dumb”. Most of the intelligence is concentrated in planning and replanning.

This feels very different from most current multi-agent architectures I’ve seen. I’m currently building this system based mostly on intuition, and honestly I’m not even sure whether this architecture will actually work well in practice.

I’m curious:

Has anyone here experimented with similar DAG-based orchestration models? How did they perform compared to message-passing systems?

Are there good benchmarks or evaluation methods for testing whether this kind of architecture is actually effective?

Would love to hear thoughts or related papers/projects.

I’ve been experimenting with a multi-agent orchestration model designed by my agent

The core concept is a WorkItem DAG — basically an ordered dependency graph similar to a structured todo list.

I used GPT to create this flowchart, the system works like this:

- A Planner generates the execution DAG

- Worker agents execute work items mechanically along the graph

- If unexpected situations happen (failure, new information, human interruption, etc.) a RePlanner patches the DAG and creates a new execution path

So agents themselves are intentionally “dumb”. Most of the intelligence is concentrated in planning and replanning.

I’m currently building this system based mostly on intuition, and honestly I’m not even sure whether this architecture will actually work well in practice.

I’m curious:

Has anyone here experimented with similar DAG-based orchestration models? How did they perform?

Are there good benchmarks or evaluation methods for testing whether this kind of architecture is actually effective?

https://preview.redd.it/4gx4xlarto0h1.png?width=1536&format=png&auto=webp&s=485e39c2140832dcb02704022f0b20912ccf3c46

I'm currently working on some agent workflows and I'm curious how everyone initializes their projects and how well it performs.

Can you share the prompt you use for your AGENTS.md

I’ve been running Pi for two weeks without any subagents, agent teams, or anything like that. Compared to my previous workflow, I haven’t noticed any difference at all. In fact, the AI even seems a bit more accurate without subagents.

Something I think is really important: Is there a good way to automatically start/stop another agent client and let them send messages to each other?

Would love to hear any methods or tools that can do this!