Currently, AI is extremely popular, and for us engineers, the main focus is on multi-agent collaboration to complete vertical business tasks.
Through exploration, it seems that the paradigm of AI does not deviate significantly from computational paradigms; its core collaboration logic still follows the classic control flow patterns in computer science. It represents a natural evolution and complexity of classic control logic enhanced by AI capabilities.
AI Multi-Agent Paradigm
1. Routing Decision Branch
2. Forward and Backward Dependency Waiting
3. Result Merging Decision
Routing Decision Branch: Intelligent “Conditional Jump”
The essence of routing is to allocate tasks to the most suitable execution unit based on input conditions. This is similar to if-else or switch-case statements in traditional computing, evolving into a more dynamic, semantically-based intelligent distribution in multi-agent systems.
Logical Deduction
When a user requests to enter the system, the first step is to determine “what type of task this is and who should handle it.” This process is a typical branching decision.
Technical Validation
Rule-Based Routing: In practical applications of group chat APIs, routers (Router) perform preliminary distribution based on keywords. For example, if the message contains “order,” it routes to the logistics Agent, and if it contains “fault,” it routes to the repair Agent. This is essentially a set of predefined business rules, directly reflecting conditional branching.
Intelligent Routing: More advanced frameworks like Agno and LangGraph have automated this process. Coordinators (Coordinator) or Supervisor Routers analyze the semantics of user queries rather than simply matching keywords, then distribute them to the expert Agent whose role description matches best. For example, the route(state) function in LangGraph is a dynamic conditional judgment node that checks the current state (such as user intent) to decide whether the next node is handled by AI or transferred to a human. This is akin to a dynamically generated conditional branching statement based on LLM inference.
Conclusion Validation
Routing decision branches, whether based on rules or LLM intelligent judgment, fundamentally share the same purpose and logical structure as branching control in traditional programming, serving as the cornerstone for task division and process orientation.
Forward and Backward Dependency Waiting: Stateful “Synchronization Points”
Complex tasks often involve steps that must be executed in order, with subsequent steps depending on the outputs of previous steps. This is manifested in multi-agent systems as explicit waiting and state transfer.
Logical Deduction
In software development, the “design → coding → testing” process represents a strong dependency relationship. In parallel computing, this requires synchronization of threads/processes through mechanisms like locks, semaphores, or barriers. In multi-agent systems, this concept is abstracted into state management and sequential workflows.
Technical Validation
StateFlow: The core of LangGraph is state management. A shared data structure called AgentState (like a Python dictionary) runs through various nodes of the graph. Each node (Agent) writes results into the state after execution, and the next node reads the required information from the state before starting work. This forms a clear producer-consumer model, ensuring dependency relationships.
Sequential Agent: The sequential workflow in Google’s ADK framework clearly reflects this, where the output of one sub-agent (saved via output_key) is set as the input for subsequent sub-agents. This is entirely consistent with the logic of function call chains A(B(C(input))).
Agent Loop: The “reasoning-action-observation” cycle (ReAct paradigm) within a single Agent is also a miniature sequential dependency process, where the result of the previous action serves as the basis for the next reasoning.
Conclusion Validation
Forward and backward dependency waiting is achieved through shared state machines or explicit input-output mappings, with core ideas that are entirely consistent with process calls, data flow programming, and synchronization mechanisms in parallel computing, ensuring the correctness and order of task execution.
Result Merging Decision: Complex “Data Aggregation”
When multiple Agents process subtasks in parallel, their outputs need to be integrated into a unified, high-quality final answer. This goes beyond simple string concatenation and involves evaluation, denoising, weighting, and comprehensive reasoning decision processes.
Logical Deduction
This is similar to the “Reduce” phase in the MapReduce paradigm or aggregation functions (like SUM, AVG) in database queries, but introduces AI-specific uncertainty management and semantic fusion capabilities.
Technical Validation
Aggregator: In microservice-style multi-agent systems, there is a dedicated aggregator component responsible for collecting responses from multiple Agents and performing merging, denoising, and prioritization. This is akin to a customized data aggregation service.
Coordinated Integration: In the models of Google ADK and LangGraph, a coordinator Agent (like LlmAgent) typically serves as the final decision-maker. After calling various specialized Agents through AgentTool, it receives all returned results, and its core LLM performs reasoning, summarization, and comparison based on instructions to generate a comprehensive response. This is like an intelligent “decision function.”
Consensus Mechanism: In more complex scenarios (like the TRIAGEAGENT medical diagnosis system), multiple Agents reach a consensus through multiple rounds of discussion. The system introduces an “early stopping mechanism” to terminate discussions early when it determines that the agents have reached a consensus, avoiding unnecessary calculations. This is similar to convergence judgments in optimization algorithms and represents an efficient group decision-making strategy.
Conclusion Validation
Result merging decisions combine traditional data aggregation techniques with LLM reasoning capabilities to achieve information fusion and final decision-making. Its goals (to obtain the best output from multiple inputs) and foundational processes (collecting-processing-output) are consistent with traditional paradigms, but the means of implementation become smarter and more flexible due to AI.
Example (Intelligent Customer Service Ticket Processing)
|
Core Paradigm |
Manifestation in this Scenario |
Corresponding Traditional Computing Concept |
|
Routing Decision Branch |
Upon receiving the user message “My order hasn’t arrived, and the device reports error E12,” the system routes it in parallel to the “logistics Agent” and “repair Agent” based on keywords. |
Conditional Branching, Message Queue |
|
Forward and Backward Dependency Waiting |
The “logistics Agent” and “repair Agent” process their respective tasks in parallel, but their results need to be completed simultaneously before being passed to the next “report generation Agent.” The system sets a synchronization point here. |
Synchronization Primitives (like barriers), Asynchronous Programming |
|
Result Merging Decision |
The “report generation Agent” (or a dedicated aggregator) receives the logistics status and device fault information, performs comprehensive reasoning, and generates a final report containing root cause analysis and solution suggestions for the user. |
Data Aggregation, Reduce Phase |
From the example, it can be seen that a multi-agent system is a dynamically orchestrated distributed computing program composed of multiple intelligent functions (Agents) through message passing and state sharing.
Mainly reflected in:
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Node Intelligence: Each execution unit (Agent) itself is an LLM with language understanding and generation capabilities, rather than a pre-coded fixed function.
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Dynamic Orchestration: Process orchestration can be dynamically adjusted based on real-time reasoning rather than being fully predefined.
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Natural Interaction: Collaboration between Agents and interaction with users are conducted in natural language, reducing communication barriers.
Conclusion
Ultimately, it is these “AI characteristics” built on solid computational paradigms that enable multi-agent systems to flexibly and efficiently address complex, open vertical business problems that previously required significant human intervention.