Tool call × big model thinking = super intelligence: How ReAct strategy changes AI capabilities
How can AI agents achieve deep thinking and precise actions through the ReAct strategy?
Core content:
1. ReAct strategy definition: giving AI agents the ability to observe, think, act, and give feedback
2. How the ReAct strategy enables AI to solve problems like a detective
3. Two case studies of the ReAct strategy in solving complex problems
Imagine that if ordinary AI is a robot that only executes instructions mechanically, then the intelligent agent using the ReAct strategy is like a detective who thinks before acting.
When solving problems, it does not jump into the abyss of conclusions hastily, but follows the spiral ladder of "observation-thinking-action-observation" and approaches the truth step by step. This is the charm of the ReAct (Reasoning + Acting) strategy.
ReAct strategy: the agent's "inner monologue" and "external action"
ReAct is like equipping AI with two gears: "inner monologue" and "action actuator", and letting them rotate alternately:
1. Observe the environment : The agent opens its eyes and perceives the world around it
2. Inner thinking : Analyze clues and plan solutions in your mind like a detective
3. Take action : Take real steps to interact with your environment
4. Observe the results : examine the changes brought about by the actions and provide new clues for the next round of thinking
This strategy allows the AI to stop running around like a headless fly and instead act like a chess master, thinking carefully about every move.
Case 1: Find the birth year of a celebrity
Imagine an agent faced with the question: "In what year was Beethoven born?"
Pseudocode example:
Observation: Received the question "In what year was Beethoven born?"
Think: This is a question about a historical figure. I need to find information about Beethoven’s life.
Action: [Search] Search for "Beethoven's life"
Observation: The search returns "Ludwig van Beethoven (1770-1827), German composer..."
Think: From the results I can see that Beethoven was born in 1770.
Action: [Answer] "Beethoven was born in 1770."This process is like a student doing history homework. He first understands the problem, thinks about how to obtain information, consults materials, and finally extracts the answer from the materials.
Case 2: Solving complex problems that require multi-step reasoning
Now for a more complex question: "Who had a longer musical career, Beethoven or Mozart?"
Pseudocode example:
Observation: Received the question "Who had a longer musical career, Beethoven or Mozart?"
Question: To compare career lengths, I would need to know the year each person was born, the year they died, and when they started making music.
Action: [Search] Search for "Beethoven's life"
Observation result: Get "Ludwig van Beethoven (1770-1827), began to study music at the age of 8..."
Thinking: Beethoven was born in 1770, died in 1827, and began his musical career at the age of 8. Now I need to find information about Mozart.
Action: [Search] Query "Mozart's Life"
Observation result: "Wolfgang Amadeus Mozart (1756-1791), started composing at the age of 5..."
Thinking: Mozart was born in 1756, died in 1791, and began his musical career at the age of 5. Now I need to calculate the length of their respective musical careers.
Action: [Calculate] Beethoven's musical career = 1827 - (1770 + 8) = 49 years
Mozart's musical career = 1791 - (1756 + 5) = 30 years
Think about it: After calculation, Beethoven's musical career was 49 years, and Mozart's was 30 years, so Beethoven's musical career was longer.
Action: [Answer] "Beethoven's musical career was longer, 49 years in total. Mozart's was 30 years. Although Mozart started earlier (5 years old), Beethoven lived longer and therefore had more time to compose."This example is like a puzzle game, where you need to collect multiple pieces and piece them together through logical reasoning to see the complete picture.
How ReAct improves the capabilities of intelligent agents
Correct the route like a navigator
Ordinary AI is like a train that follows a fixed route, while the ReAct agent is like a car equipped with GPS, which reassesses its position every time it travels a certain distance and adjusts its route at any time. When it encounters a wrong turn, it will immediately detect and correct it, instead of stubbornly heading to the wrong destination.
For example, when searching for "napoleon's birthplace", if the first query returns ambiguous results, ReAct will think: "I need a more precise query" and try a new search term "napoleon bonaparte's birthplace details".
Break down the problem like building blocks
When faced with a complex problem like "calculate how old Einstein was when he published the theory of relativity", ReAct breaks it down into:
1. Query Einstein’s birth year
2. Check the year the theory of relativity was published
3. Calculate the difference between the two
It's like a chef preparing all the ingredients first and then cooking them step by step, instead of blindly throwing everything into the pot.
ReAct application scenarios in life
Smart Shopping Assistant
Imagine a ReAct agent that helps you choose a new laptop:
User: Please recommend me a laptop suitable for graphic design.
Thinking: Users need a laptop suitable for graphic design and should consider graphics card performance, screen color reproduction and processor performance.
Action: [Search] Query "Recommended Notebooks for Graphic Design in 2025"
Observation results: Information about multiple high-performance notebooks was obtained...
Thinking: You need to understand the user's budget and requirements for thinness and lightness.
Action: [Ask] "What is your budget? Do you have any requirements for the laptop to be thin and light?"
User: The budget is 8,000 yuan and I hope it is easy to carry.
Thinking: According to the budget and thinness requirements, it is necessary to balance performance and portability...
...(Continuous Thought-Action Cycle)
Action: [Recommendation] "Based on your needs, I recommend the following three laptops..."This process is like that of an experienced shopping guide, who constantly understands needs through communication, thinks about feasible solutions, and ultimately provides personalized suggestions.
Health advisor robot
A ReAct agent that helps users develop fitness plans:
Thinking: It is necessary to understand the user's physical condition, fitness goals and time schedule.
Action: [Query] Collect basic user information...
Think about it: The user is a fitness novice, hopes to gain muscle, has 3 days a week to exercise, and needs to design a step-by-step plan.
Action: [Search] Search for "muscle building training plan for beginners"
Thinking: According to the user's situation, it is recommended to start with basic movements and gradually increase the intensity...
Action: [Generate] Create a personalized three-phase fitness plan...Like a personal trainer, the ReAct agent not only provides standardized advice, but also continuously adjusts the plan based on the user's characteristics and feedback.
Thinking: AI’s dance of thought
The ReAct strategy brings the dual capabilities of "thinking and acting" to the intelligent agent, just like giving the machine a special dance rhythm. In this dance, thinking is the prudent steps and action is the dancer's leap, and the two are intertwined to form an elegant and efficient problem-solving process.
Through this alternating approach, AI is no longer a simple command executor, but an intelligent assistant that can think, plan, execute, and adjust. In the future development of AI, the ReAct strategy undoubtedly represents a key leap from "mechanical reaction" to "thinking action", paving the way for building smarter and more natural human-computer interaction.
Just as detectives need clues, reasoning, and action to solve cases, intelligent agents equipped with ReAct strategies can help us solve various difficult problems in a more humane way in a complex and changing world.
