Empowering Long-Running AI Agents with Timers

There’s been a lot of discussion lately about how AI struggles with long-running tasks. And it makes sense when you think about it. These large language models can generate a ton of text in a few seconds. But then what? They’ve put out all these words or code and don’t really have a clear direction on what to do next.

That’s where things can start to go off the rails. The AI might start hallucinating or get into loops. It’s like giving a toddler a microphone - entertaining for a minute, but it quickly turns into incoherent babbling.

However, current AI models seem to really shine when given a narrow focus. So how do we leverage this strength while tackling longer, multi-step tasks?

Introducing the AI Snooze Button

What if we gave the model the ability to “sleep” and set a wake-up timer for itself? I imagine it being a tool that is exposed to the agent where it can pick and choose the next time it “wakes” to accomplish a task.

Let’s say you want an AI to manage your social media presence and post a few times throughout the day on various platforms.

Instead of having it generate all the posts at once and then twiddling its digital thumbs for the next 24 hours, the AI could analyze the task, set a timer to wake itself up at the optimal posting times, generate the appropriate content, post it, and then set another timer for the next posting window.

It’s like giving the AI a snooze button that it can manage itself. It wakes up, does a quick burst of focused work, and then goes back to sleep until the next scheduled task. This way, it’s not running constantly in the background, potentially going off course or wasting resources.

The Benefits of AI Power Naps

This approach could have a few key benefits:

  1. Improved task management: By breaking up a long-running task into discrete, timed chunks, the AI can better manage its progress and stay on track.

  2. Resource optimization: An AI that can sleep and wake itself up as needed is a more efficient use of computational resources. It’s not constantly running.

  3. Enhanced coherence: With a narrow, timely focus for each work sprint, the output and is likely to be more coherent and on-topic.

The Future of AI Productivity

This was just one potential idea I had for tackling the challenge of long-running AI agents. But I think it’s an intriguing concept to try. As these models become increasingly sophisticated, finding ways to harness their strengths and mitigate their weaknesses will be a huge market

- Joseph

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