A thinking style for AI where it talks to itself to figure out a plan, takes an action (like searching the web or running code), looks at the result, and then decides what to do next. It’s the difference between an AI that just guesses an answer, and an AI that actually does research to find it.
A thinking style for AI where it talks to itself to figure out a plan, takes an action (like searching the web or running code), looks at the result, and then decides what to do next. It’s the difference between an AI that just guesses an answer, and an AI that actually does research to find it.
Standard LLMs generate text in a single, linear pass. If they don't know the answer, they hallucinate. The ReAct (Reasoning and Acting) paradigm, introduced by Yao et al., interleaves Thought traces (the model's internal reasoning) with Action steps (executing a tool like a search engine, calculator, or database) and Observation (the result of that action). This creates a feedback loop. The model can correct its own mistakes, gather missing information, and break down complex, multi-step tasks that are impossible to solve in a single generation.
# Conceptual: The ReAct Loop
def react_agent(user_query, tools):
context = user_query
for step in range(max_steps):
# 1. THOUGHT: The model reasons about what to do next
thought = llm.generate(f"{context}\nThought: What should I do next?")
# 2. ACTION: The model selects a tool and arguments
action, args = llm.generate(f"{thought}\nAction: [Tool Name]({args})")
# 3. OBSERVATION: Execute the tool and get the result
if action in tools:
observation = tools[action](args)
else:
observation = "Tool not found."
# 4. Update context and loop
context += f"\nThought: {thought}\nAction: {action}\nObservation: {observation}"
if "Final Answer:" in thought:
return thought.split("Final Answer:")[-1]Enterprise Automation: ReAct enables AI to move beyond simple chatbots and actually perform work—like querying a CRM, updating a database, or generating a report based on live data. Reducing Hallucinations: By forcing the model to ground its reasoning in actual tool outputs (Observations), ReAct drastically reduces factual errors in enterprise applications.
A detective solving a case. They don't just sit in their office and guess who the killer is. They form a hypothesis (Thought), go to the crime scene to look for clues (Action), examine the fingerprints (Observation), and then form a new hypothesis based on what they found.
A thinking style for AI where it talks to itself to figure out a plan, takes an action (like searching the web or running code), looks at the result, and then decides what to do next. It’s the difference between an AI that just guesses an answer, and an AI that actually does research to find it.
Standard LLMs generate text in a single, linear pass. If they don't know the answer, they hallucinate. The ReAct (Reasoning and Acting) paradigm, introduced by Yao et al., interleaves Thought traces (the model's internal reasoning) with Action steps (executing a tool like a search engine, calculator, or database) and Observation (the result of that action). This creates a feedback loop. The model can correct its own mistakes, gather missing information, and break down complex, multi-step tasks that are impossible to solve in a single generation.
Enterprise Automation: ReAct enables AI to move beyond simple chatbots and actually perform work—like querying a CRM, updating a database, or generating a report based on live data. Reducing Hallucinations: By forcing the model to ground its reasoning in actual tool outputs (Observations), ReAct drastically reduces factual errors in enterprise applications.