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Discussion (18 Comments)Read Original on HackerNews
I've found that similarly to how teams can degrade into spending more time bikeshedding and on the watercooler than on work, agents also tend to end up spending way too much time coordinating as opposed to doing the work. And so I rediscovered that it's better to have one agent that's the Manager (on a Manager Schedule) and the rests be builders (on a Builder's Schedule), where the manager might be interrupt driven, but the builders need to be able to focus for a while without interruption (context poisoning).
Thanks for writing this and demonstrating that writing about anything is useful to share knowledge and practices. In the end, I learned a lot from Martin Fowler and his gang and I guess I should pay back and write about my own discoveries, however trivial they seem to me.
Ed: for posterity
https://www.reuters.com/world/europe/openai-agents-hijacked-...
Another Blackboard Example: https://github.com/halbritt/striatum/tree/main/docs/rfcs
(Useful to see how this compares to another Blackboard type platform - Gastown https://github.com/halbritt/striatum/blob/main/docs/records/...)
https://github.com/horiacristescu/playbook-harness
Another way to see the blackboard - it is like code, it executes, can be passed around like in higher-order programming, but this it even stranger - it can reflect on itself, not just execute. The task.md file is the agent.
That's only because of a deficiency in CI. If you get smarter CI that doesn't trip on files unrelated to the build, using the repo as a blackboard or wiki is probably fine
The biggest problem I encountered using a similar setup for doing long-term and iterative data analysis is error propagation. My team and I are characterizing and modelling a physical system, there are real-world experiments that need to be run and then fed back into the analysis pipeline and then the next frontier of questions comes up. Any time there has been an erroneous analysis somewhere along the line, that error continues to be treated as a correct fact until it has been decisively eradicated. If one analysis script or document has the error written as a correct fact, that error will continue to pollute future analyses. Oh and these errors can also end up in the agents’ memory files as well. I have gotten very careful about making sure that every reference is corrected everywhere because it seems that the initial error is weighted heavier than the correction.
I may have explored a related approach but as an append-only log riding on source-control to sync state between checkouts (git trailer metadata specifically)
https://gist.github.com/corv89/c506780881b260f4c5a4618fe8d92...
Excited to see where such concepts can take "multiplayer" agentic systems
"My goal is a very simple to use tool that drops straight into your project and immediately offers a communication channel for agents to coordinate work. The first step is to get Talwrn to a point where it can support its own development. I’m planning to post about it regularly as I’m hoping to use it as a single, evolving example of how pure agentic engineering can proceed."
Term: "Blackboard" https://en.wikipedia.org/wiki/Blackboard_system
The future may be AI structured as a corporation, rather than AI as a human competitor.
The boulder, it even found a path down the mountain. on its own, by doing so-amazing path finding its own super amazing path finding algorithm, and all the boulders co-ordinated falling down, ON THEIR OWN! And they all reached the ground!
I tell you! The future may be these boulders washing your underpants and putting you to sleep. It is clear!