AI agents that think fast and slow

DeepMind has designed an agentic framework that mimics the two systems thinking of humans.

Agentic systems are promising to create new paradigms for developing applications and solving tasks. However, the types of tasks that AI agents solve have different levels of complexity and require different memory and reasoning capabilities.

Inspired by the two-systems thinking paradigm presented by Nobel laureate Daniel Kahneman, researchers at DeepMind have developed a new agentic framework called Talker-Reasoner.

The premise of the framework and the accompanying study is that current agentic frameworks are mostly designed to solve System 1 tasks, which rely on pattern recognition and instant memory access. When it comes to System 2 tasks, which require more methodical reasoning and planning, agentic systems fall apart.

Talker-Reasoner solves this problem by dividing the agent into two modules: the Talker and the Reasoner.

The Talker is analogous to System 1. It handles real-time interactions with the user and the environment. It uses the in-context learning (ICL) abilities of LLMs to perceive the world, interpret language, use memory, and generate conversations.

The Reasoner module is designed after System 2. It performs complex reasoning and planning. It interacts with tools and external data sources to augment its knowledge and make informed decisions.

The two modules interact through a shared memory system. The Reasoner updates the memory with its latest beliefs and reasoning results. The Talker retrieves this information to make fast decisions.

The researchers tested their framework in a sleep coaching application that interacts with users through natural language and provides personalized guidance and support for improving sleep habits. This application requires a combination of quick, empathetic conversation and deliberate, knowledge-based reasoning. The same pattern can be applied to other types of applications, including personalized education assistants and customer service.

Read more about Talker-Reasoner on VentureBeat.

Read the paper on arXiv.