The biggest success in automated loop architectures can be if we can add a layer for the agent to autonomously know when human intervention or human help is needed, instead of taking shortcuts or just chasing goals. Also, verifiers sound interesting, and for the fact that 'evidence comes from an external check that the agent cannot easily talk its way around'—because from its own generated text, an agent can think a task or goal is completed even when the problem is still there. It's called the phantom convergence crisis. (The Phantom Convergence Crisis in AI agents refers to a systemic failure mode in multi-agent reinforcement learning (MARL) and autonomous agent workflows. It occurs when a group of interacting agents mathematically or procedurally appears to reach a stable, optimized equilibrium [convergence] during training or local execution, but this stability is entirely illusory [phantom]). And the most important is 'Live Artifacts,' which can help humans to know the current state and when to intervene, because generated text streams can be superficial. Despite all these methods, we need to take an agent-completed task with a grain of salt and check it.
One of the best overviews of loops I’ve seen. If anyone asks me what a loop is I’ll send them here.
Thanks for the kind words. Working on a follow up to this to break down the loop engineering part more in detail.
The biggest success in automated loop architectures can be if we can add a layer for the agent to autonomously know when human intervention or human help is needed, instead of taking shortcuts or just chasing goals. Also, verifiers sound interesting, and for the fact that 'evidence comes from an external check that the agent cannot easily talk its way around'—because from its own generated text, an agent can think a task or goal is completed even when the problem is still there. It's called the phantom convergence crisis. (The Phantom Convergence Crisis in AI agents refers to a systemic failure mode in multi-agent reinforcement learning (MARL) and autonomous agent workflows. It occurs when a group of interacting agents mathematically or procedurally appears to reach a stable, optimized equilibrium [convergence] during training or local execution, but this stability is entirely illusory [phantom]). And the most important is 'Live Artifacts,' which can help humans to know the current state and when to intervene, because generated text streams can be superficial. Despite all these methods, we need to take an agent-completed task with a grain of salt and check it.
Check out http://www.Martinloop.com
open source governance for autonomous coding agents that solves loop problems of today
I dont run any loops without it l, amazing stuff
excellent high-level description paper