The pseudocode-only part was a relief but I still overcomplicated the state structure early on.
Start by clarifying the ReAct loop's core components: reasoning, action, observation, and state. Then outline pseudocode with clear state management, max steps, and error handling, emphasizing modularity and observability. Finally, discuss trade-offs like step limits and error recovery strategies.
Pro tip: Mention that you'd log each reasoning step and tool call for debugging and evaluation, and consider using a timeout per tool call to prevent hangs. This shows production awareness beyond basic pseudocode.
Initialize a state object to track the conversation history, current step count, and any intermediate results. Set a maximum step limit to prevent infinite loops.
At each iteration, generate a thought based on the current state, deciding whether to call a tool or produce a final answer. This is typically done by an LLM.
If a tool is chosen, execute it with the given arguments, capture the result or error, and append the observation to the state. Handle tool errors gracefully.
Increment the step count and check if the max steps is reached or if a final answer is produced. If so, exit the loop and return the result.
Implement try-catch around tool calls, log errors, and optionally retry or fallback. Also include a timeout for each step to avoid hanging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.