Overthinking AI Agents: Why It Happens and How to Fix It

Hello folks! Welcome to another issue of Technical Tuesday. Today, we're diving into a fascinating paper about the dangers of AI models overthinking and how to tackle it.

Let's dive in!

Overthinking about overthinking 🧠

Overthinking in AI agents happens when models spend too much time reasoning internally and too little time interacting with their environment. Sounds familiar? Like me at lunchtime- scrolling through food options on my phone as opposed to just stepping out to eat!

The authors identified 3 types of overthinking:

1. Analysis paralysis- LLMs shifting their focus from immediate actions to elaborate future planning.
2. Rogue actions- LLMs generating a chain of interdependent steps without waiting for feedback from the environment.
3. Premature disengagement- LLMs terminating tasks based on their own internal state, as opposed to waiting for environmental validation.

Common causes of overthinking ∵

Model selection is critical in AI agents. It dictates the intelligence, performance and cost. However, other factors contribute to overthinking too:

1. Reasoning models tend to overthink more. Not surprising! If you've read the somewhat neurotic chain of thoughts from DeepSeek r1, you will expect this.
2. Smaller models tend to overthink more. The authors suspect that this is due to the lack of capability to assess the various contexts of actions, reactions and environment by smaller models. Larger models, with more capacity, are less prone to this.
3. More token usage correlates with less overthinking. This surprised me! I thought overthinking meant burning through tokens, but models using more tokens actually reason more clearly and overthink less.

Fixing overthinking 🔨

We can't prevent models from overthinking (meditation isn’t an option yet), but we can mitigate its effects. A simple fix: use an LLM as a judge to grade how much a model is overthinking. Between multiple runs, you want to pick the solution with the lower overthinking score. Those solutions tend to be more accurate.

That’s it! If you want to dig deeper, here’s the paper- https://www.arxiv.org/abs/2502.08235

Have you faced AI models overthinking in your AI agents? Drop a comment below!