Deep Dive into AI Agent Core Patterns: From ReAct to Multi-Agent Collaboration in Engineering Practice#

Agents are reshaping the underlying paradigm of AI applications. Large language models are undergoing a paradigm shift from “passive Q&A” to “active execution.” Before Agents existed, LLMs passively answered questions; with Agents, they became active executors. This is not merely a change in business model, but a fundamental transformation from chat programs to production-grade components.

This article takes an engineering perspective to systematically review the mainstream Agent working patterns—from single-Agent core mechanisms to multi-Agent collaboration architectures—analyzing their respective applicable scenarios, trade-offs, and key challenges for production deployment.

I. Understanding the Essence of Agent: A Closed-Loop System#

Before diving into specific patterns, it is necessary to clarify a foundational concept: What is an Agent?

A sufficiently powerful and practical definition is:

Agent = A closed-loop system capable of continuously completing tasks in uncertain environments

This definition contains three key elements:

1. Uncertain Environment: Excludes pure computation and rigid rule-based scenarios
2. Continuous Completion: Not one question-one answer, but staying focused on the goal until done
3. Closed-Loop System: Not a single call, but a runtime that loops and self-corrects

Without a closed loop, it is not an Agent—it is a Chatbot.

This definition also reveals an easily overlooked truth: what truly determines whether a system is an Agent lies not in whether it uses an LLM, but in who decides what to do next—if it is controlled by code, that is a workflow; if the LLM decides for itself, that is an Agent.

II. Single-Agent Core Working Patterns#

Pattern 1: ReAct (Reasoning + Acting)#

ReAct (Reasoning + Acting) is currently the most popular foundational pattern. Its core is a “Think → Act → Observe” loop: in each round, the Agent first reasons about what should be done, then executes the action (calling a tool or completing a step), observes the result, and enters the next round of reasoning until the task is complete.

User Goal
    │
    ▼
┌─────────────┐
│  Thought    │
└──────┬──────┘
       │
       ▼
┌─────────────┐
│   Action    │
└──────┬──────┘
       │
       ▼
┌─────────────────┐
│  Observation    │
└────────┬────────┘
         │
    ┌────┴────┐
    │         │
 Incomplete  Complete
    │         │
    ▼         ▼
  Think     Return Result

Applicable Scenarios: Scenarios where the task path is unclear and requires gradual exploration, such as research Agents browsing content from multiple sources, personal assistant Agents managing to-dos and emails, and financial Agents pulling real-time market data.

Pros and Cons:

• ✅ Strong flexibility, capable of dynamically adjusting strategy based on intermediate results
• ❌ Each tool call requires invoking the LLM, resulting in higher costs
• ❌ May fall into loops

Pattern 2: Plan-and-Execute (Plan First, Execute Later)#

Unlike ReAct’s “walk and see” style, Plan-and-Execute requires the Agent to generate a complete execution plan before taking action, and then execute step by step in order.

User Goal
    │
    ▼
┌─────────────────────────────────┐
│ Planning Phase: Generate        │
│ Complete Execution Plan         │
└──────────────┬──────────────────┘
               │
               ▼
┌─────────────────────────────────┐
│ Execution Phase: Execute        │
│ Steps in Sequence               │
└──────────────┬──────────────────┘
               │
               ▼
┌─────────────────────────────────┐
│ After Each Step: Compare Plan   │
│ with Results                    │
└────────┬────────────────────────┘
         │
    ┌────┴────┐
    │         │
Needs      Complete
Adjustment
    │         │
    ▼         ▼
Re-plan   Return Result

Two Variants:

Applicable Scenarios: Tasks involving multiple interdependent steps, such as software development requiring architecture design before coding, and business process automation.

Performance Comparison: Research shows that Plan-and-Execute variants (e.g., LLMCompiler) can achieve up to 3.7× latency optimization, 6.7× cost reduction, and approximately 9% accuracy improvement compared to ReAct. Note, however, that these are reported maximum optimization values and may not be achievable for every workload.

Pattern 3: Reflection (Self-Reflection / Self-Correction)#

The Reflection pattern emphasizes the Agent’s inspection and correction of its own output, improving output quality through multiple rounds of “Generate-Evaluate-Improve.”

This pattern is commonly seen in scenarios requiring high-precision output, such as code review and content creation. A typical implementation is:

1. A Generator Agent produces a first draft
2. A Verifier Agent checks facts and logic item by item
3. Correction feedback is returned to the Generator Agent for iterative optimization

III. Multi-Agent Collaboration Patterns#

Building on single-Agent architectures (such as ReAct and Plan-and-Execute), multi-Agent systems expand “one brain” into “a team of experts.” Its core value lies in specialized division of labor—letting different Agents respectively handle data retrieval, analysis, verification, execution, and other stages, thereby accomplishing complex tasks that a single Agent cannot handle alone.

Based on differences in control allocation and communication structure, current mainstream multi-Agent patterns can be summarized into the following categories.

1. Routing Pattern — “Who Does It”#

Characteristics: A central router distributes requests to the most matching specialized Agent based on task type. The center retains full control over task entry.

Applicable Scenarios: Customer service ticket distribution, multi-domain task routing (e.g., “technical issues” routed to technical support Agents, “billing issues” routed to financial Agents).

Pros and Cons:

• ✅ High efficiency, clear logic
• ❌ The router itself is a single point of bottleneck; routing errors may cause quality disasters

2. Sequential / Pipeline Pattern — “Relay Style”#

Characteristics: Agents process tasks in a fixed sequence; the output of the former becomes the input of the latter, forming a data pipeline.

Applicable Scenarios: Content creation pipelines (Research Agent → Writing Agent → Editing Agent → Review Agent), report generation systems.

Pros and Cons:

• ✅ Clear logic, easy to debug, controllable resource consumption
• ❌ Poor flexibility; an error in any link affects the overall process

3. Parallel / MapReduce Pattern — “Working Together”#

Characteristics: A large task is split into multiple independent subtasks, processed in parallel by multiple Agents, and finally aggregated by an aggregation Agent.

Applicable Scenarios: Large-scale data analysis (thousands of contracts reviewed in parallel), multi-channel customer feedback analysis.

Performance Data: Measurements show that 3 Agents running in parallel consume 2.3× the total tokens of a single model, but accuracy improves by 15% and processing time is reduced by 60%.

4. Generator-Verifier Pattern (Maker-Checker) — “Work and Review”#

Characteristics: A Generator Agent produces a first draft; a Verifier/Checker Agent reviews and provides feedback, cycling iteratively until quality thresholds are met. This is also the natural extension of the Reflection pattern discussed earlier in a multi-Agent architecture.

Applicable Scenarios: Code generation + testing, legal document review, fact-checking, compliance validation.

Pros and Cons:

• ✅ Quality is controllable
• ❌ Requires configuring maximum iteration counts and human fallback; otherwise, when verification standards are vague, it is easy to fall into ineffective loops

5. Orchestrator-SubAgent Pattern — “Manager Leads the Team”#

Characteristics: Hierarchical architecture where a central orchestrator breaks down tasks, dispatches them to specialized sub-Agents, and aggregates results—similar to a “manager + employees” model.

Applicable Scenarios: Complex query routing, marketing campaign orchestration, code review, and other tasks requiring cross-domain collaboration.

Pros and Cons:

• ✅ Clear responsibilities, high degree of specialization
• ❌ Orchestrator easily becomes an information bottleneck; cross-sub-Agent information transfer may be lost

6. Group Chat / RoundTable Pattern — “Meeting Discussion”#

Characteristics: Multiple Agents participate in discussions as equals, taking turns speaking, referencing each other’s viewpoints, and iteratively refining cognition until converging on a result. A chat manager controls the speaking order.

Applicable Scenarios: Creative brainstorming, complex decisions requiring multi-perspective debate, multi-round “maker-checker” cycles for quality control.

Pros and Cons:

• ✅ Multi-perspective analysis of problems, suitable for complex reasoning
• ❌ High discussion overhead; when there are too many Agents, process management becomes complex (recommended not to exceed 3)

7. Debate / Consensus Pattern — “The Jury”#

Characteristics: Multiple Agents independently process the same task, reaching consensus through voting or negotiation mechanisms, relying on “wisdom of the crowd” to improve reliability.

Applicable Scenarios: Multi-expert review of legal documents (3 specialized Agents independently analyze, only consensus conclusions are output), medical diagnosis assistance.

Reliability Improvement: Redundant design can improve system reliability from 85% to over 95%. Note, however, that pure debate patterns may deadlock when competing solutions diverge.

8. Swarm / Message Bus / Shared State Pattern — “Emergent Intelligence”#

Characteristics: No central control; Agents indirectly coordinate through a shared environment (event bus, shared storage), relying on simple rules to form self-organizing behavior, similar to the pheromone mechanism of ant colonies (Stigmergy).

Applicable Scenarios: Security operations automation (alert triage, dynamic threat response), collaborative research (multi-dimensional information cross-verification).

Pros and Cons:

• ✅ Loose coupling, high scalability
• ❌ Difficult link tracing; termination conditions must be set to prevent infinite loops or silent failures

IV. Pattern Selection: When to Use Which?#

The Soul-Searching Question: Are You Sure You Need an Agent?#

Before designing an Agent system, you must first answer one question: Can the decision path for this task be written with if-else?

Can it be written with if-else?
    ├─ Yes → Use a workflow, don't use an Agent
    └─ No → How many decision branches are there?
              ├─ 3-5 → Use classifier + workflow, still don't use an Agent
              └─ Too many to write, or need to dynamically adjust strategy
                  based on intermediate results
                  └─ Only then does an Agent make sense

Decision Matrix#

Selection Principle: From Simple to Complex#

In production practice, follow the “single-Agent first → then workflow → finally multi-Agent” principle of minimalism. Only introduce multi-Agent architecture when complexity matches the benefits.

V. Key Challenges and Lessons for Production Deployment#

1. Evaluation (Evals): The Watershed of Agent Success or Failure#

Reliability evaluation of Agent systems is currently the most neglected aspect. Many developers still rely on simple “heartbeat check” style testing rather than systematic evaluation suites. However, Agent failure modes are often implicit—it may “confidently make mistakes” rather than explicitly error out.

Recommendation: Prepare a fixed test set for your Agent, quantitatively evaluating success rate, cost, latency, and other metrics. Evaluation is not only a gatekeeper before going live, but also the foundation for iterative optimization.

2. Determinism Constraints: Don’t Let the Agent “Freestyle”#

A real-world lesson: A team built a fully autonomous customer service Agent. When a user asked about the “return policy,” it queried the database 8 times, called the knowledge base 3 times, took 47 seconds, consumed $0.12, and finally returned a response containing SQL error messages.

Core Principle: Any logic that can be solved with code should never be left for the LLM to guess. Constraining Agent behavior through state machines or directed acyclic graphs (DAGs) is currently the approach more aligned with enterprise-grade engineering practice.

3. Most of Multi-Agent’s “Superiority” Is Just Compute#

Anthropic’s research points out that in the performance improvement of multi-Agent over single-Agent, approximately 80% of the variance can be explained by token consumption. In other words, multi-Agent is strong largely not because “more brains are smarter,” but because it burns vastly more tokens. Multiple controlled experiments in 2026 further confirmed: when “thinking token budgets” are aligned, single-Agent can catch up with or even surpass multi-Agent in multi-hop reasoning.

Before deciding to adopt multi-Agent, ask yourself: Do you really need “multiple collaborations,” or do you just want the model to “think a bit longer”? If it’s the latter, giving a single-Agent a larger thinking budget is often simpler, cheaper, and more controllable.

4. Two Hard Lessons#

1. Don’t let sub-Agents spawn sub-Agents: Recursive proliferation will cause costs to spiral out of control—this limit must be enforced in the orchestration layer with code, not by “begging” the model in the prompt not to do so
2. Don’t use an independent Agent to grade its own homework: Self-evaluation is the easiest place for self-deception in Agent systems

5. Specific Engineering Challenges of Multi-Agent Architecture#

VI. Development Trends: Key Evolutions in 2025-2026#

Rise of Standardization Protocols#

2025 was defined as the “engineering landing year” for Agents. Two key protocols drove this process:

These protocols are driving Agent systems from “isolated islands of individual implementations” toward “an ecosystem of standardized collaboration.”

Agentic Workflow: The Value of a Transitional Form#

Agentic Workflow is a scheme that replaces some nodes in an established workflow with Agents. The outer workflow ensures controllability and auditability, while the inner Agents provide necessary flexibility.

This “transitional form” is not technologically backward, but a pragmatic choice for engineering deployment. Just as autonomous driving gradually evolves from L2 to L3 and beyond, Agentic Workflow provides adjustable space between control and autonomy.

Core Evolution Logic#

The core evolution logic of multi-Agent collaboration can be summarized as: From “controlling the Agent” to “designing the environment.” Early architectures emphasized the center’s explicit control over every step; mature architectures require the center to design rules, states, boundaries, and feedback mechanisms, allowing Agents to autonomously form collaborative order through local interactions.

Conclusion#

Agent pattern selection is not a question of “which is more advanced,” but “which better matches the task.” ReAct is suitable for dynamic exploration, Plan-and-Execute for structured tasks, and multi-Agent for cross-domain collaboration—but each pattern has its cost and complexity trade-offs.

For technical teams, the most critical thing is not chasing the latest architecture, but maintaining engineering rationality:

• Understand the applicable boundaries of each pattern
• Establish a comprehensive evaluation system
• Find balance between determinism and flexibility

After all, Agents are not magic—they still follow the fundamental laws of computer science. And your engineering practice is what determines whether the system can truly land as a reliable production component.