Reasoning paradigms

There is not one standard architecture for building AI agents. Several paradigms exist for solving multi-step problems.

ReAct (Reasoning and Action)

With this paradigm, we can instruct agents to "think" and plan after each action taken and with each tool response to decide which tool to use next. These Think-Act-Observe loops are used to solve problems step by step and iteratively improve upon responses.

Through the prompt structure, agents can be instructed to reason slowly and to display each "thought". The agent's verbal reasoning gives insight into how responses are formulated. In this framework, agents continuously update their context with new reasoning. This can be interpreted as a form of Chain-of-Thought (opens in a new tab) prompting.

ReWOO (Reasoning WithOut Observation)

The ReWOO method, unlike ReAct, eliminates the dependence on tool outputs for action planning. Instead, agents plan upfront. Redundant tool usage is avoided by anticipating which tools to use upon receiving the initial prompt from the user. This is desirable from a human-centered perspective since the user can confirm the plan before it is executed.

The ReWOO workflow is made up of three modules. In the planning module, the agent anticipates its next steps given a user's prompt. The next stage entails collecting the outputs produced by calling these tools. Lastly, the agent pairs the initial plan with the tool outputs to formulate a response. This planning ahead can greatly reduce token usage and computational complexity as well as the repercussions of intermediate tool failure.