In this article
Problem: Agents that call tools blindly without reasoning produce incorrect tool arguments, chain unnecessary calls, and cannot recover from errors.
Solution: Structure agent execution as a Thought-Action-Observation loop: the LLM writes its Thought (reasoning), then specifies an Action (tool name + arguments), receives the Observation (tool result), and repeats — continuing until it can produce a Final Answer.
Implementation:
- System prompt defines the ReAct format: "Thought:", "Action:", "Action Input:", "Observation:", "Final Answer:"
- Parse each LLM response to detect action vs. final-answer
- Execute the tool, append Observation to context
- Loop with a max_iterations guard (e.g., 10)
Trade-Offs:
- ✔ Pro: Interpretable — every reasoning step and tool call is logged
- ✔ Pro: Self-correcting — wrong intermediate steps are caught via observations
- ✔ Pro: Works with any LLM that can follow the format
- ✖ Con: Verbose — trace is longer than direct tool calls
- ✖ Con: Multi-step overhead: N reasoning rounds = N LLM calls
When To Use: Any agent that uses tools and must make sequential decisions based on intermediate results. When to avoid: Simple single-tool use cases where a direct function call is sufficient.