When a large model agent receives a task like 'help me book a high-speed train ticket to Shanghai tomorrow', it has to do much more than just generate a paragraph of text. It must break down vague goals into executable steps, decide what to do first, what to do later, and how to adjust when encountering failures - this is the planning ability of the agent.
1、 Why Planning is the Core of Agents
The simple dialogue model is' one question, one answer '. And the agent needs to maintain the goal in multiple rounds of interaction and decompose the goal into a series of calls to the tool. Without planning, agents can only make locally optimal choices at each step, which can lead to detours, repetition, or giving up halfway. Plan to give the agent a global perspective: first think clearly about the path, and then gradually execute it.
2、 ReAct: Think while doing
ReAct (Reason+Act) is an early and most classic paradigm. Its approach is to have the model alternate between outputting 'Thought' and 'Action': first reasoning about what to do at the moment, then calling a tool to obtain the 'Observation' result and proceed to the next round. This forms a cycle of Thought → Action → Observation.
The advantages are simplicity, flexibility, and strong adaptability to tool environments; The disadvantage is that each step relies on the results of the previous step, lacks a global plan, long tasks are prone to losing direction, and the cost of repeatedly calling the model is high.
3、 Plan and Execute: Plan before executing
In order to overcome ReAct's shortsightedness, Plan and Execute breaks down the process into two stages: first, the Planner generates a complete list of steps at once, and then the Executor gradually completes each step. If necessary, a "Replan" phase can be added to adjust the plan based on feedback during the execution process.
This approach is more efficient in handling tasks with clear and decomposable steps, and also saves model calls; But its adaptability to environmental changes is not as good as ReAct, and if the plan goes wrong, it will cause the entire path to deviate.
4、 Reflection and Self Correction
Another approach is to introduce 'reflection'. The agent reviews the results after execution to determine if the goal has been achieved. If not, it generates improvement suggestions and tries again. It is often used in conjunction with the previous two paradigms: using ReAct or Plan and Execute to complete the main loop, and using reflection to perform quality control at critical nodes.
5、 Choices in practice
In real systems, the three approaches are often mixed: using ReAct for simple tasks is sufficient; Complex and decomposable tasks are suitable for Plan and Execute; Further reflect on the high quality requirements for the results. When making a choice, the main considerations are task complexity, sensitivity to latency and cost, and environmental uncertainty.
In addition, planning ability also relies on two things: clear task and tool descriptions, and reliable feedback. The more structured and accurate the information returned by the tool, the more stable the planning of the agent.
Conclusion
Planning is a crucial step for agents to move from being able to chat to being able to work. Understanding the applicable boundaries of ReAct, Plan and Execute, and reflection is more important than blindly pursuing a certain framework. For developers, it is often more efficient to first think about the form of the task and then choose the appropriate planning paradigm.
[Reference source] Comprehensive compilation of industry information released publicly (paradigms such as ReAct and Plan and Execute are all derived from publicly published academic papers and engineering practice summaries).