Building an effective multi-agent system with large language models (LLMs) requires thoughtful planning of how the agents will think, act, and collaborate. This guide walks through key design choices, from decision-making patterns and core components to architectural styles and human oversight, to help you develop a modern multi-agent system. We’ll cover:

• Agent Decision-Making Patterns: Choosing between ReAct loops, plan-then-act, reflection feedback, or tree-of-thought exploration.
• Core Components: The “cognitive” subsystems every agent needs (perception, memory, planning, execution).
• Architectural Paradigms: How to organize single vs. multiple agents (single-loop agents, planner/executor/verifier triads, hierarchical orchestrators, etc.).
• Multi-Agent Collaboration (MCP & Beyond): Using frameworks/protocols like Model-Context Protocol (MCP) and Google’s A2A for agent cooperation.
• Choosing the Right Design: Matching agent architectures to task types (decision support, knowledge retrieval, automation) with notes on when to use each pattern.
• Human-in-the-Loop: Incorporating human oversight and control for safety and reliability.

Throughout, we’ll reference recent state-of-the-art approaches and frameworks to ensure the guidance is up-to-date for 2025.

Agent Decision-Making Patterns (Execution Loops)

Before diving into tooling or coding, start with first principles: define the execution pattern, i.e. the “rhythm” by which an agent (or agents) will cycle through thinking and acting. The execution pattern determines how the agent breaks down a task and iteratively works toward a goal. Four common patterns are:

• ReAct Loop (Think-Act-Observe-Repeat): The agent interleaves reasoning and actions step-by-step. It thinks about what to do, performs an action (e.g. calls a tool or queries an API), observes the result, then repeats. This Reason+Act loop continues until the task is complete. ReAct is very flexible and allows dynamic adjustment based on real-time feedback, ensuring each step is validated by observations before moving on. It mirrors how humans solve problems iteratively and is well-suited for open-ended tasks or environments where each action yields new information (e.g. browsing the web or interacting with a user interface).
• Plan-then-Act: The agent first plans out an entire sequence of actions or a detailed solution, and only after the plan is complete does it execute the steps. This deliberative approach works best when the task is well-defined or predictable. The agent essentially does all the “thinking” up front (possibly via chain-of-thought reasoning) and then carries out the plan. Modern implementations often allow some adaptability (re-planning if needed), but the emphasis is on upfront strategizing. This pattern shines for complex but structured tasks where a clear plan can be formulated (for example, writing a detailed report by first outlining all sections, then filling them in).
• Reflection Feedback Loops: Here the agent adds an explicit feedback step after one or more actions. The agent pauses to evaluate its own output or progress, and may revise its strategy or retry a step if the results are not satisfactory. In other words, after execution the agent checks if the task succeeded; if not, it can adjust the plan, try an alternative, or even ask for help. This self-reflective pattern is useful for improving reliability and correctness. For example, a reflection agent might critique its last answer for errors and refine it, or after executing a tool it verifies the outcome and decides whether to proceed or backtrack. Such feedback loops make agents more resilient in dynamic or error-prone scenarios, allowing learning from mistakes on the fly.
• Tree-of-Thoughts (Parallel Exploration): Instead of following one linear chain of reasoning, the agent branches out to explore multiple possibilities or solution paths in parallel, then evaluates and converges on the best one. The Tree-of-Thoughts (ToT) approach maintains a tree of intermediate “thoughts” (steps), using search algorithms (like breadth-first or depth-first search) to systematically consider different branches with lookahead and backtracking. The agent can simulate evaluating partial solutions (pruning those deemed unpromising and expanding those likely to lead to a goal). This pattern is inspired by how humans might brainstorm many approaches and then choose the most promising. It has been shown to significantly improve problem-solving on complex tasks by not getting stuck on a single line of thought. Tree-of-thoughts is powerful for decision support or strategic planning problems where multiple options should be weighed, the agent effectively performs a search over the space of solutions.

Each of these patterns has trade-offs. A ReAct loop is highly adaptive but may sometimes loop unnecessarily; plan-then-act can be efficient for straightforward tasks but brittle if surprises occur; reflection loops add reliability at the cost of extra steps; tree-of-thoughts maximizes exploration but can be computationally heavy. In practice, advanced agents often combine patterns, for instance, using an initial plan then a reflection check at the end, or running a ReAct style loop with occasional backtracking (a hybrid of ReAct and tree search). The key is to pick a strategy that fits the nature of your task. Predictable, well-defined tasks benefit from planning, whereas open-ended or high-stakes tasks benefit from iterative reasoning and self-correction.

Core Components of an Agent

No matter which execution pattern you choose, an intelligent agent is built on a few core subsystems that handle distinct aspects of cognition. Think of these as the modules that let an AI agent perceive, remember, plan, and execute in its environment:

1. Perception / Input Handling: The agent’s interface to the world. It takes in information or observations from the environment. This could be a user’s query, the text of a web page, the result of an API call, sensor data, etc. Essentially, perception is the agent reading the world so it knows the current state or context. For instance, a web-browsing agent’s perception module might parse the HTML of a page to extract text, or a developer agent’s perception might be the content of error logs. Designing good perception means providing the agent the relevant info at each step (and abstracting away irrelevant details).
2. Memory (State): A mechanism to store and recall information over time. Agents typically have working memory for short-term context (e.g. the current conversation or the last tool result) and may also have long-term memory for persistent knowledge (e.g. past interactions, a knowledge base). Memory prevents the agent from forgetting earlier events and allows it to build on past outcomes. In LLM-based systems, working memory is often the conversation history or a scratchpad, while long-term memory might be a vector database of embeddings that the agent can query as needed. Effective memory design ensures the agent isn’t just reacting in isolation each turn, but maintains continuity, for example, remembering a user’s preferences or the goal of the task across multiple steps. Memory can also include episodic memory (recent events) and semantic memory (general world knowledge or facts the agent has learned).
3. Planning Module: The “brain” of the agent where it transforms goals into actions. Given the current goal and context (from perception + memory), the planning component decides what to do next. This could be as simple as the next tool to use, or as complex as forming a multi-step plan. In many LLM agents, the planning is done by the model itself via chain-of-thought prompting , essentially the LLM generates a reasoning trace that outlines steps. Other implementations might use symbolic planners or policy algorithms. Regardless, this module is about reasoning: breaking down tasks, considering options, and choosing actions. For example, an agent might reason “To answer the user’s question, I should search for X, then summarize the result.” The output of planning is an intended action or sequence of actions.
4. Execution / Action Interface: The component that carries out actions in the real world based on the plan. Often this means calling tools or APIs (since an LLM alone only generates text). The execution layer is like the agent’s “hands”, it might fetch a webpage, run code, query a database, or invoke some function. In a simple agent loop, the LLM’s output might directly be a tool command which the system executes, updating the environment or retrieving new data. Execution can also involve interacting with external systems (e.g. sending an email, moving a robot arm). A well-defined execution module includes a library of allowable actions (tools with strict interfaces) and enforces constraints (so the agent doesn’t do something completely out-of-scope). After execution, typically the result of the action is fed back into the perception module, closing the loop (the agent observes what happened and the cycle repeats).
5. Evaluation / Feedback: (Optional, but important in advanced agents) A mechanism to assess outcomes and adjust. After one or several actions, the agent can have a step to check: Did the action achieve the expected result? Is the overall goal met? If not, it can trigger a revision, perhaps altering the plan or invoking a different sub-agent. This evaluation step is essentially the reflection we discussed earlier. For example, after executing a plan, an agent could verify the answer’s correctness (using a verifier model or tests) and, if it detects an error, loop back into planning to fix it. In many architectures, this isn’t a separate physical module but rather a behavior of the agent’s loop (e.g. a self-critique prompt). Still, it’s useful to design your system to allow such feedback insertion, either via an automated check or even a human review (more on human-in-loop later).

These components together enable the sense-think-act cycle common to intelligent systems. When building a multi-agent system, you’ll be instantiating these components for each agent or sharing some across agents. For example, each agent might have its own memory (context) and specialized planning prompt, while they share access to a common tool execution layer. Always ensure the boundaries are clear: what does each agent perceive? What can it remember or not remember? Which agent is responsible for planning vs. execution? These choices shape the architecture.

Architectural Patterns for Single vs. Multi-Agent Systems

With the patterns and components in mind, the next step is choosing an agent architecture: how many agents to use and how they will interact. The architecture is essentially how you organize those components across one or multiple agents, and how tasks are divided. Below are several architectural paradigms, from single-agent designs to sophisticated multi-agent arrangements, along with guidance on when to use each.

• Single-Loop Agent (Monolithic): The simplest architecture is one agent that does everything: it perceives input, plans actions, executes them, and iterates in a loop until done. This single agent might still use tools, memory, etc., but there is no division of labor, one LLM (or one chain) handles the entire task. A single-loop reactive agent is great for narrow-scope tasks or cases where introducing multiple agents would add unnecessary complexity. For example, a self-contained coding assistant that takes an error message and keeps looping (debug -> run tests -> debug again) can be a single agent with a reflection loop. The strength of this design is simplicity: no inter-agent communication overhead. However, it may struggle if the task naturally breaks into distinct subtasks or if parallelism is needed. If one agent’s prompt becomes too large or unwieldy (trying to juggle many responsibilities), that’s a sign you might need multiple specialized agents instead.
• Planner-Executor (with optional Verifier) Architecture: This pattern splits the task between different roles, usually as separate agents or at least separate prompt stages. One agent (or module) is the Planner, it takes the high-level goal and breaks it into sub-tasks or a plan of action. Another agent is the Executor, it receives the plan steps and carries them out one by one (calling tools or performing actions). Optionally, a third agent can act as a Verifier or Checker, reviewing the executor’s output against requirements (e.g. validating an answer or testing generated code). This trio (Planner, Executor, Verifier) provides higher assurance results, as each role is focused and can catch errors. For instance, for a knowledge retrieval question, the Planner might decide which resources to query, the Executor gathers and composes an answer, and a Verifier agent checks the answer’s sources for correctness (flagging if citations don’t support the claims). Or in a coding scenario, the Planner outlines components to implement, the Executor writes the code, and a Tester agent (Verifier) runs tests to ensure it works. Use a planner/executor split when tasks benefit from explicit decomposition or when execution needs oversight. It’s especially useful if the planning context (broad overview) can be kept separate from execution context (detailed focus on one step), so each agent works with a smaller, clearer prompt. The downside is overhead: the agents must pass messages (the plan, the results, etc.) between them, and the system is more complex. But for complex, multi-step tasks or those requiring validation, this architecture greatly improves reliability.
• Hierarchical Orchestrator & Workers: A very common multi-agent architecture is the orchestrator (manager) with specialized sub-agents pattern. In this design, one agent is a lead or supervisor that receives the initial goal and then dynamically delegates subtasks to other agents (the “workers”). The supervisor agent’s job is to figure out what experts or tools are needed and in what order, somewhat like a project manager. The worker agents are specialists, each skilled in a particular type of task, and the orchestrator activates them as needed. For example, an orchestrator agent faced with a complex query might spin up a Researcher agent to do web searches, a Solver agent to analyze data, and a Reporter agent to compile the findings. It coordinates the process: once workers return results, the orchestrator synthesizes them, decides if more work is needed or if the answer is ready. This approach is inherently hierarchical: the top-level agent supervises, and lower-level agents focus on specific jobs. One big advantage is parallelism, the orchestrator can deploy multiple sub-agents in parallel to speed up work (e.g. several search agents exploring different angles of a question simultaneously). It also mirrors human organizations (manager delegating tasks to team members), which often makes designing each agent’s role more straightforward. Frameworks like LangChain’s LangGraph support this pattern by allowing a “supervisor” node (LLM) that decides which agent node to call next. The Anthropic Research system is a real-world example: a LeadResearcher agent spawns multiple search sub-agents for different aspects of a query, then later invokes a CitationAgent to verify sources. Use this orchestrator-worker architecture for: open-ended tasks that involve distinct phases or skills (research, coding, writing, verifying), or whenever tasks can be decomposed into parallel subtasks for efficiency. Keep in mind that coordination logic can become complex, e.g. deciding when all sub-agents are done, merging their outputs, handling dependencies between them. Essentially, the orchestrator agent’s prompt or logic must include instructions on how to manage multi-agent workflow.

Figure: Example of a hierarchical multi-agent architecture for a research task (inspired by Anthropic’s system). A Lead Agent orchestrates the process: it receives the user query, plans the strategy, and then spawns specialized Sub-agents (e.g. multiple web search agents each focusing on different aspects). The sub-agents operate in parallel, each using tools to gather information. The lead agent collects their findings and may refine the plan or spawn additional agents if needed. Once sufficient information is collected, a Verifier agent (here a Citation Checker) is invoked to ensure all claims are supported by sources. Finally, the lead agent compiles the verified results into the answer. This design separates concerns (planning vs. executing vs. verifying) and enables concurrency, making the system far more powerful than a single agent.

• Network of Peer Agents (Decentralized): Not all multi-agent systems have a single leader. In a network architecture, multiple agents can communicate and pass tasks among themselves in a more flexible, peer-to-peer fashion. Here, each agent is like a node in a graph; an agent that finishes a subtask can hand off to whichever agent is appropriate for the next step (based on some routing logic or an LLM decision). For example, Agent A might do an initial analysis then call Agent B for visualization, or Agent C might realize the task is done and produce the final output. This is a more horizontal structure, where any agent can potentially call any other agent as needed. The benefit is flexibility and modular expansion: you can add new agent types and have the others use them when relevant. This approach might emerge in open agent ecosystems, for instance, one can imagine independent services (agents) with APIs where an agent can dynamically discover another agent that can fulfill a needed role (this vision is part of Google’s Agent-to-Agent (A2A) protocol for cross-system agent collaboration). However, purely decentralized agent networks are hard to control and debug. They require careful design of communication protocols so agents understand each other, and safeguards to avoid loops or conflicts. Use case: if your system needs to integrate agents from different organizations or systems, e.g. a travel planning scenario where one agent in your system coordinates with external partner agents (flight booking, hotel booking, etc.) via a standard protocol, a network approach with A2A can enable that interoperability. For most single-organization applications, though, a simpler hierarchy or static workflow is easier to manage.
• Sequential Multi-Agent Pipeline: Another pattern is a fixed sequence of specialized agents, each performing one stage of a pipeline and then handing off to the next. This is akin to an assembly line: Agent 1 does task A, then passes result to Agent 2 to do task B, and so on, until Agent N produces the final output. For example, a document processing system might have: Parser Agent -> Analyzer Agent -> Summary Agent in sequence. This is different from the planner/executor pattern in that each agent has a distinct skill and they execute one after the other in a predetermined order (as opposed to a planner deciding steps dynamically). The benefit is straightforward control flow and clarity (similar to a traditional software pipeline, but with each stage using an LLM agent). However, it’s not flexible if the task deviates from the expected flow, and it doesn’t support parallelism. Use a sequential pipeline when the problem naturally breaks into a linear series of stages that seldom need to loop back, for instance, data ingestion -> transformation -> report generation.
• Aggregator or Debate Patterns: In some cases, you might deploy multiple agents to independently produce solutions and then have a mechanism to aggregate or choose the best outcome. One variant is the “debate” or multi-expert setup: you prompt two or more agents to come up with answers or opinions, possibly let them critique each other, and then either a judge agent (or a human) picks the winner or synthesizes a final answer. Another variant is the aggregator pattern: e.g., have three agents each retrieve data from different sources, then a fourth agent merges the data into a report (like a synthesizer). These patterns are useful for decision support where diversity of thought is valuable, for example, generating multiple potential strategies and then evaluating which is best. They can improve robustness (via self-consistency: if many agents independently arrive at the same answer, it’s likely correct) and reduce individual agent bias. However, managing multiple outputs and comparisons adds complexity. This is often combined with other architectures (you might have an orchestrator that actually sets up a debate between two sub-agents, for instance).

Summary of Architecture Choices: If your problem is simple or well-contained, start with a single agent loop. If it’s complex and multi-faceted, consider multiple agents with clear roles. Use a planner/executor split when reasoning and acting benefits from separation (or when you want a verification step for safety). Use a hierarchical orchestrator when you need dynamic delegation and parallel work. Leverage networks or external agent protocols (like A2A) if coordination beyond your system’s boundary is needed. And remember, these are not mutually exclusive; you can hybridize them. For example, your orchestrator might use a planner/verifier internally (the lead agent plans, a sub-agent executes, another verifies), or a sequential pipeline might branch into parallel sub-agents at one stage. The goal is to maximize reliability, efficiency, and clarity by structuring agents in an appropriate way for the task.

Multi-Agent Collaboration and MCP (Model Context Protocol)

When building decision-support systems with multiple agents, a key challenge is how the agents communicate and share context. Naively prompting several LLMs to chat can lead to confusion or context loss. This is where frameworks like Model Context Protocol (MCP) come in. MCP is a standardized architecture for multi-agent systems that emphasizes structured roles and context-sharing between agents. It was introduced in frameworks such as Microsoft’s AutoGen and has gained traction as a blueprint for multi-LLM orchestration.

In MCP, agents are categorized into specific roles with a host-client-server model:

• Host: The coordinator (analogous to an orchestrator or “CEO” of the agents). The Host sets up and maintains the shared context, memory, tool access, and routes messages between the other agents. It’s like the project manager that knows the big picture and delegates tasks. In technical terms, the Host manages the group conversation and ensures each agent gets the information it needs.
• Server Agents: These are the specialized worker agents (the execution layer). A server agent has a defined capability or responsibility, for example a ResearchAgent for web searches, a CodeAgent for coding, or AnalysisAgent for data analysis. Each server agent registers with the system, declaring what tools it can use, what inputs it expects, and its function. The Host can call on these server agents to perform their tasks. They are called “server” because they provide a service (skill) on demand, similar to a microservice in software architecture.
• Client (User-Facing Agent): The client agent is the interface to the end-user or calling application. It might take the user’s query and forward it into the system via the Host, then return the answer back to the user. In some designs, the client agent could also do some light processing or act like a persona that interacts conversationally, but the heavy lifting is done by the server agents behind the scenes. You can think of the Client agent as the “front-end” that makes it feel like you’re talking to one AI assistant, even though that assistant is actually a team of cooperating agents.

What makes MCP powerful is that it formalizes how agents share context and messages without the developer hard-coding all the logic for each interaction. The Host handles ensuring that, for example, the ReviewerAgent only sees the code produced by the CoderAgent and not irrelevant parts of the conversation. Each agent’s memory can be isolated or shared depending on configuration. There is a clear protocol for message passing: who speaks when, how turn-taking is managed, and how to incorporate tool results into the context. By using MCP, you get modularity (clear separation of roles), context management (agents get only the info they need, preventing confusion), and scalability (you can add new agents easily without breaking the entire logic).

Benefits of MCP-style Architecture: Teams of agents can achieve what single LLMs cannot. They can maintain persistent memory across the whole system, e.g., all agents have access to a shared knowledge base or chat history when needed.. They allow specialization, meaning each agent can be optimized (via prompting or even fine-tuning) for a specific task, analogous to departments in a company. Communication is structured, which reduces the chance of prompts going off-track or agents misunderstanding each other. And importantly, MCP designs often log all interactions (since there’s an explicit routing through the Host), providing auditability of the process, crucial for decision support in domains like finance or healthcare where you need to trace how a conclusion was reached.

Example: Imagine a multi-agent research assistant helping with decision support for a business. You ask a complex question like “Should we expand into market X and what are the risks?” In an MCP setup, the query goes to the Host (coordinator). The Host might activate a DataGatherer (Server) agent to fetch market data, a RiskAnalyzer (Server) agent to analyze potential risks, and a StrategyAdvisor (Server) agent to formulate recommendations. Each agent works with its piece of the puzzle (with access to shared context when appropriate). The Host maintains an overarching memory of the conversation (“the project context”). Once results come in, perhaps a ReportWriter agent (another Server role) compiles a summary. The client agent then presents the final recommendation to the user. All along, MCP ensured that, say, the RiskAnalyzer had access to the same market data the DataGatherer found (shared context), but the agents weren’t stepping on each other’s toes — each had a clear role and turn to speak.

Other Collaboration Protocols: MCP focuses on internal collaboration within one multi-agent system (one product or one AI assistant’s internal architecture). Another emerging standard, Agent-to-Agent (A2A) protocol by Google, addresses external collaboration, how agents from different systems or organizations can talk to each other securely. For instance, using A2A, your personal AI assistant agent could directly message an airline’s booking agent to book a flight, as long as both adhere to the A2A communication format. A2A provides for agent discovery, authentication, and a universal message schema so that a constellation of agents can coordinate on a larger workflow (think of it as agents API-calling each other). In summary: MCP is about internal integration, A2A is about external cooperation. Designing a state-of-the-art multi-agent system may involve both, internally using MCP-like patterns to structure your agents, and externally being compatible with protocols like A2A to integrate with other AI services.

Frameworks and Tools: You don’t have to implement multi-agent orchestration from scratch. There are frameworks supporting these architectures:

• LangChain / LangGraph: LangChain’s new LangGraph module provides a graph-based orchestration engine for multi-agent workflows. It lets you define agents as nodes in a state machine graph and handle transitions (including a Supervisor node as described earlier). This is great for implementing complex flows like the network or supervisor patterns in a robust way.
• Microsoft AutoGen: A framework specifically for multi-agent conversations using LLMs. AutoGen has native support for MCP concepts (GroupChat with multiple ConversableAgents). It handles a lot of the context management and turn-taking for you, so you can focus on defining agent roles and tools.
• OpenAI Function Calling / Orchestration: OpenAI’s API now supports function calls which can be composed into multi-step workflows. While not a full multi-agent framework on its own, you can implement a simple planner-executor by having GPT-4 output a plan in a structured format, then your code executes it, and maybe calls GPT-4 again for verification. For richer agent collaboration, you’d likely combine this with other tools or frameworks.
• Crew (CrewAI) and OpenDevin: These are emerging open-source projects. CrewAI allows you to define agents with roles, goals, and memory, and manage their interactions, similar to MCP. OpenDevin is a developer assistant agent that uses an MCP-inspired approach to break down software tasks and coordinate multiple models.
• Cognitive Architecture influences: Research from cognitive science (like SOAR or ACT-R) has inspired some of these designs. While those are older symbolic systems, the idea of distinct cognitive modules (working memory, procedural memory, etc.) and multiple cooperating processes is being adapted to LLM agents. For instance, an agent might use a rule-based system to supervise an LLM’s outputs for consistency, echoing a verifier concept.

In practice, you might use a combination. For example, Anthropic’s approach (as described in their 2025 paper) essentially hard-coded an orchestrator and subagents with careful prompt design, rather than using an external framework, but the pattern is similar to what LangGraph or AutoGen would offer. The takeaway is: Leverage these state-of-the-art frameworks to implement your design after you’ve thought through the roles and patterns. They will help ensure things like context window management, message routing, and parallelism are handled correctly so you can focus on agent logic.

Choosing the Right Approach for the Task

A crucial part of the design process is aligning the architecture and patterns to the specific task and constraints. Not all problems require a fancy multi-agent setup; conversely, some problems will fail if you try to force them into a single-agent mold. Here are some guidelines on how to decide which approach to use, with examples:

• If the task is straightforward or highly constrained (e.g., a single-step question answering, a simple database query, or a contained optimization problem), a single agent with a basic ReAct loop may suffice. You likely don’t need complex planning or multiple agents because the sequence of actions is short or predetermined. Keep it simple: one agent can handle perception, a small amount of reasoning, and a tool call if needed. For instance, “lookup the weather and respond” can be done by one agent calling an API. Adding more agents would add overhead without benefit.
• For tasks that require heavy reasoning or long-term planning but are mostly linear, consider a Plan-then-Act strategy or a Planner/Executor duo. For example, writing a detailed analytical report or solving a long math problem benefits from planning out the structure before execution. A single agent could do this with an internal chain-of-thought and then final answer, or you can explicitly separate a planning step (one agent generates a detailed outline) and an execution step (another agent or process fills in each part). This ensures complex tasks are tackled methodically. However, be ready to handle the case where the plan might need adjustment if an unexpected obstacle arises (e.g., the agent planned to use an API that returns no data, it should replan). The deliberative approach shines when you more or less know the subtasks upfront.
• When the environment is unpredictable or tools can fail often, favor feedback loops and retries. A reflection-oriented design or at least a loop with error checking is important. For example, in browser automation or web scraping, pages might not load or content might not be found, so an agent in a loop that catches errors and tries alternative strategies is needed. A straightforward plan-execute might break in such cases. So an agent that continuously observes “did that step work?” and if not, tries something else (or eventually asks for help) will be much more robust. If using multiple agents, you might incorporate a Monitor agent that watches for failures and triggers reattempts or fallback plans.
• For knowledge-intensive tasks where accuracy is paramount, incorporate a Verifier or Checker component in the architecture. This could be a separate agent or just a verification step in the loop. For example, in a research or Q&A system, hallucinations are a risk, so use a verifier agent to check claims against sources. In a multi-agent setup, you might have a dedicated CitationAgent as Anthropic did, which takes the assembled answer and all retrieved documents, and adds specific source attributions. In coding, use a tester or reviewer agent to validate outputs (like running unit tests on generated code, or reviewing it for errors). The general rule: whenever the cost of a mistake is high, build in verification. That could even be a human verification (e.g. route the final answer to a human moderator if confidence is low).
• If the task is naturally parallelizable or covers broad scope, lean toward a multi-agent orchestrator design. Research questions that require covering lots of ground, data analysis that can be split by data source, or any situation where different subtasks can proceed independently will see huge gains from multi-agent parallelism. Anthropic’s study showed multi-agent systems excel at breadth-first exploration: when asked to find information across many items, a lead agent delegating work to sub-agents was far more effective than a single agent slogging serially. If you find your single agent hitting context window limits or taking too long because it has to handle too many sub-queries sequentially, that’s a sign to introduce sub-agents to share the load (each with its own context window). Just be mindful of consolidation, you’ll need to merge results, so design a step for that (either the orchestrator agent does it, or use an aggregator agent).
• When specialized skills or knowledge are needed, use multiple specialized agents rather than one generalist. For example, a complex decision-support query might involve legal analysis and financial calculation. Rather than trying to prompt one agent in a way that it knows both domains, you might have a LegalAgent and FinanceAgent and have them either work sequentially or in parallel on their parts of the problem, then combine insights. This ensures each agent can have a focused system prompt with domain-specific guidance and even use domain-specific tools (perhaps the finance agent uses a spreadsheet tool, the legal agent uses a case law database). The MCP approach of role-based agents makes this easier to manage, as each agent’s context is limited to its domain.
• For real-time or safety-critical tasks, or those involving external actions (DevOps, autonomous control), prioritize determinism and human oversight over full autonomy. In situations like DevOps incident response, an agent might suggest fixes (e.g. restart a server, deploy a patch), but executing those blindly is risky. Here an architecture with a human in the loop or at least a rule-based gate is important. For instance, the agent could be configured in a proposer role (it analyzes the issue and plans a resolution), but then requires human approval to execute. We’ll discuss human oversight more in the next section, but the key design point is: do not let an LLM agent have unchecked control in high-stakes environments. Instead, use the agent to assist the human operator with suggestions and even automated tests, but keep a manual confirmation step for irreversible actions. Architecturally, you might still have a multi-agent setup (planner agent, fixer agent, tester agent), but before the fixer actually runs a command on production, it must get a green light from a human supervisor.
• Consider the complexity vs. benefit trade-off: Multi-agent systems bring powerful capabilities but also introduce complexity (more moving parts) and high token costs (they tend to call the LLM many more times). If the task value is not high or the domain doesn’t justify the overhead, a simpler single-agent solution might be more economical. For example, if you just need an AI to format data from one file to another, spinning up multiple agents would be overkill; a straightforward script or single LLM call could do it. Use multi-agent setups for tasks that truly need the scale or specialization, e.g., deep research, elaborate planning, multi-step workflows, or interacting with numerous systems simultaneously.

In summary, match the architecture to the problem. Ask yourself: Does this problem decompose into parts? Are those parts best handled sequentially or in parallel? Do I need different skillsets (hence different agents)? How critical is correctness (do I need verification)? How much uncertainty or variability (do I need an agent capable of reacting and recovering)? Answering these will point you to an appropriate design. Table 1 below provides a quick mapping of scenarios to recommended patterns:

Table 1: Task Scenarios and Suggested Agent Designs

Scenario	Characteristics	Suggested Design
Simple Q&A or API lookup	Deterministic, one-step or few-step	Single agent, ReAct loop (no need for multi-agent)
Structured task with clear steps (e.g. generate report from template)	Predictable subtasks, little deviation	Plan-then-Act or Planner->Executor (one plans outline, one fills details)
Web research or open-ended query	Uncertain steps, large search space	Orchestrator with multiple search sub-agents (parallel browsing) + synthesizer agent
Complex decision support (mixed domains)	Requires different expertise, analysis	Multiple specialized agents (domain experts) + coordinator (MCP style roles for context sharing)
Code generation and testing	Needs error checking, iterative debugging	Planner (decompose tasks) + Coder agent + Tester/Verifier agent loop until tests pass
Critical operations (DevOps, financial)	High risk if wrong, external side-effects	Agent as advisor + Human-in-the-Loop for approvals (no fully autonomous action); possibly add rule-based safety checks
High-volume data processing (can be parallel)	Same operation repeated on many items	Multiple agents or tool instances in parallel, managed by a dispatcher agent (if LLM used for orchestration)
Dialogue or negotiation between parties	Multi-party conversation, different goals	Separate agent per persona and possibly a mediator agent; or network of agents if truly decentralized

The above are guidelines, the design can be tweaked based on specific needs. The beauty of multi-agent systems is their flexibility: you can tailor the number of agents, their roles, and their interaction pattern precisely to the problem at hand.

Human in the Loop: Ensuring Oversight and Control

No matter how advanced our AI agents get, human oversight (HITL) remains a critical component for many systems. State-of-the-art design is not about replacing humans entirely, but about leveraging AI strengths while keeping humans in control where it counts. Here’s how and why to integrate human-in-the-loop in your multi-agent system:

Why Humans Are Needed: Agents, especially LLM-based ones, can be unpredictable. They may hallucinate non-existent facts or actions, misunderstand subtle intent, or overstep the intended bounds of their authority. In sensitive domains, the cost of a mistake is too high to let the agent operate unchecked. For example, an AI customer support agent might escalate an issue incorrectly or an AI ops agent might execute a dangerous command if not supervised. Humans bring judgment, ethical considerations, and accountability, things AI currently lacks. As one AI safety engineer put it, if LLMs are the brains, human oversight is the conscience and failsafe. By inserting a human approval step at key points, you prevent irreversible mistakes, ensure compliance with policy, and build user trust.

Where to Put Human-in-the-Loop: There are a few common patterns:

• Final Decision Approval: The AI agents do the heavy lifting (research, analysis, recommendations), but before an action is executed or a final answer is given, a human reviews it. For instance, an AI drafting a financial report might require a manager’s sign-off before sending to clients. Or an AI diagnosing medical cases suggests a diagnosis, which a doctor must confirm. In workflow terms, the agent pauses and asks, “I propose to do X, is that okay?”. Only upon human approval does it proceed. This is effectively adding a human “verifier” at the end of the agent chain.
• Exception Handling / Escalation: The AI tries its best, but if it’s unsure or encounters certain triggers, it hands off to a human. For example, a customer support chatbot answers common questions autonomously, but if the conversation becomes complex or the user is unhappy, it escalates to a human agent with full contex. In a multi-agent setup, you might even have a special UserProxy or HumanAgent that represents a human in the loop, the system can route to that agent which essentially notifies a person. Microsoft’s AutoGen supports a UserProxyAgent which is conceptually this idea. Your design can include rules like “if the AI verifier agent is not at least 90% confident, involve a human” or “if the user says they are dissatisfied, switch to human operator.”
• Continuous Oversight (Human as a Supervisor): In some cases, you might have a human literally monitoring each step of the agent’s operation. This could be during a beta phase or for very high-stakes operations. The human can have a dashboard that shows each agent’s action and can intervene if something looks wrong. While not scalable for all tasks, for critical processes this provides maximum safety. In frameworks like LangGraph, one could even imagine the human as part of the loop (e.g., the supervisor agent consults the human agent at certain decision points).
• Periodic Check-ins: If an agent is working on a long-running task (say an autonomous research project that might take hours and multiple steps), it could be designed to periodically summarize progress and ask a human if it should continue or take a different direction. This way the human guides the general direction without micromanaging every action.

From a design perspective, integrating HITL means your system should be able to pause and await input. The agent should be taught (via its instructions) that it may need to ask for approval. For example, your system prompt can say: “If you are about to perform a destructive action, first output an 'Approval Request' and no action, and wait for confirmation.” HITL article, they phrase it as teaching agents to “ask for permission, and wait”. This often involves linking the agent’s tool-use mechanism with a human approval step, e.g., a tool that actually sends a notification to a human, who then returns a yes/no.

Best Practices for HITL:

• Define Clear Trigger Conditions: Decide when the agent should defer to a human. Triggers could be certain confidence scores, specific user requests (“Can I speak to a human?”), or particular actions (e.g., any financial transaction over $1000 requires human ok). Make these rules explicit either in code or even in the agent’s prompt so it knows its limits.
• Design the User Experience for the Human: The human overseer needs to understand the context quickly. So when handing off, have the agent provide a concise summary of what it’s done and what it needs approval for. In a support chatbot, this could be a transcript and suggested response; in a DevOps agent, it might be a summary of the incident and the proposed fix. The agent essentially should justify or explain its reasoning to the human to help them decide. This also adds transparency.
• Logging and Traceability: With humans approving actions, you create an audit trail. Ensure your system logs the decisions: what did the AI propose, who approved it, at what time. This is important for compliance and learning, you can analyze later where AI needed human help and improve the model or process over time.
• Training and Calibration: If using humans regularly in the loop, make sure they are trained for it. The human needs to know their role (e.g., are they expected to double-check factual accuracy, or just give a common-sense check?). Over time, you might adjust the threshold for involving humans as the model improves, always aiming to maximize automation safely but with a human safety net.

Incorporating human-in-the-loop is not a step backward; it’s a modern best practice to responsibly deploy AI agents. Many real-world deployments use a combination of automation and oversight. For example, a content moderation agent might auto-flag posts but humans do the final takedown decision. Or an AI coding assistant might auto-merge trivial code changes, but require human review for complex ones. As AI agent systems become more capable, dynamic HITL is emerging, systems that learn when they truly need help and when they can proceed autonomously, optimizing the human’s time.

Conclusion

Designing a multi-agent system is as much an art as a science, it requires understanding the problem domain deeply and applying the right architectural patterns for intelligence, while keeping safety and manageability in mind. In this guide, we started from first principles: choosing the agent’s thought-action loop that suits your task (be it a reactive approach, deliberative planning, reflective retries, or exploratory search). We identified the core building blocks every agent needs (from perception to execution), which serve as the foundation of any agent or agent team.

Building on that foundation, we explored how to scale from one agent to many: assigning clear roles, whether in a simple planner-executor pair or a complex society of orchestrators and specialists. Modern frameworks like MCP give a blueprint for orchestrating these roles with shared contex, and protocols like A2A hint at a future where agents across organizations collaborate seamlessly. We saw that state-of-the-art systems (in 2025) like Anthropic’s research assistant and others have embraced multi-agent designs to tackle problems single agents couldn’t, achieving better performance through parallelism and specialization.

Yet, with great power comes great responsibility. We discussed how to choose the right approach, not every problem needs a fleet of agents. The best design is one that is as simple as possible but no simpler for the task at hand. Finally, we underscored the importance of human oversight in any loop that carries real risk, echoing the industry consensus that AI agents work best as force-multipliers for humans, not independent loose cannons.

By following these guidelines, deciding on a suitable decision loop, architecting modular agents with the appropriate collaboration pattern, leveraging frameworks to implement them, and baking in oversight, you can develop a cutting-edge multi-agent system that is both powerful and reliable. The field is evolving rapidly, so stay updated on emerging best practices. But the core principles remain: define how the agent(s) think and act, ensure they have the components to support that process, structure their interactions intelligently, and keep a human in the loop where it matters. With this solid design foundation, you can then use tools like LangChain, AutoGen, or others as “scaffolding” to bring your multi-agent system to life, confident that it rests on state-of-the-art architecture rather than adhoc prompt juggling.

Good luck with building your multi-agent system, may your agents collaborate effectively and your design choices serve you well in delivering a robust AI solution!

References:

1. Harun R. Basheer (May 2025). The Model Context Protocol (MCP): A New Standard for Multi-Agent Intelligence in AI Systems (Overview of MCP roles and benefits.)
2. Sikharendu Mitra (June 2025). Multi-Agent Design Pattern with MCP (Model Context Protocol). (Explanation of MCP in multi-agent contexts with examples.)
3. DataSturdy Blogs (June 2025). Multi-Agent Design Pattern with MCP, Conclusion. (Summary of MCP advantages for scalable, modular AI systems.)
4. Anthropic Engineering (June 2025). How we built our multi-agent research system. (Real-world multi-agent architecture: orchestrator with parallel sub-agents and a verifier for citations.)
5. Prompt Engineering Guide. Tree of Thoughts (ToT) (Description of Tree-of-Thoughts prompting for exploring multiple reasoning paths.)
6. Bijit Ghosh (2023). AI Agent Blueprint: From Reflection to Action. (Discussion of ReAct vs Plan-and-Solve patterns and reflection loops.)
7. Lindy AI (2023). A Complete Guide to AI Agent Architecture in 2025. (Overview of agent components and feedback loops; reactive vs deliberative vs hybrid models.)
8. Permit.io (June 2025). Human-in-the-Loop for AI Agents: Best Practices. (Why human oversight is essential and how to implement HITL workflows in agent systems.)
9. DataSturdy Blogs (June 2025). Google’s A2A Protocol: The Future of AI Agent Collaboration. (Differences between MCP (internal) and A2A (external) agent communication protocols.)