AI is hitting a wall with the “bigger is better” approach. Instead of continuing to scale up single models, many teams are breaking AI systems into smaller, purpose-built components that work together. This shift mirrors what happened in software development years ago with the move from monoliths to microservices.
This architectural evolution has given rise to a new way of building agents: multi agent systems. Instead of relying on one big model that does everything the system relies on a set of smaller expert sub-agents. Each agent in these systems handles specific tasks it’s good at, and they communicate to solve complex problems together. This approach to artificial intelligence distributes cognitive work across specialized components rather than centralizing it.
When you build with multiple agents, how they communicate becomes just as important as what each agent does individually. This isn’t new—it’s basic systems thinking applied to AI agent design.
Defining clear boundaries between individual agents forces you to think about what information needs to flow between components. In practice, we’ve found that many teams spend more time designing these interaction patterns in multi-agent systems than building the agents themselves. Properly implemented communication protocols ensure that agents can effectively coordinate to solve problems.
Good boundaries between agents let you fix or upgrade parts of the overall system without breaking everything. It’s the difference between swapping out a faulty component and rebuilding an entire engine. This provides agent systems with a level of maintainability that single agent systems often lack.
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Breaking systems into modules brings concrete benefits for artificial intelligence applications:
You can optimize each agent for its specific tasks. For a retrieval agent, you might use vector search techniques that would create unacceptable overhead in a general model. For a reasoning agent, you might use different prompt structures than for a coding agent. These specialized agents can incorporate domain knowledge and capabilities that would be impractical in single agent systems.
When something breaks (and it will), damage is contained. In single agent systems, errors tend to corrupt the entire output. With well-designed multi-agent systems, one agent failing doesn’t necessarily tank the whole operation. Other agents in the system can continue to function, providing graceful degradation rather than complete failure.
The practical result is that teams naturally start building with cleaner interfaces, fewer hidden dependencies, and more testable components. This development approach allows for more rapid innovation and better adaptation to changing requirements.
Multi-agent systems typically use one of two basic structures, each offering different ways for agents to communicate and coordinate:
Hierarchical systems work like a company org chart—manager agents direct the work of specialist agents below them to solve complex problems. CrewAI implements this pattern of multi-agent systems, with agent roles clearly defined in a hierarchy. This structure works well when you need predictable workflows with clear responsibility for each step. The control flows from top to bottom, with specialized agents handling specific tasks assigned by higher-level coordinators.
Peer-to-peer systems allow any agent to talk directly to other agents in the network. Microsoft’s Autogen takes this approach to multi-agent systems, creating more flexible collaboration patterns. These systems adapt better to dynamic environments but can be harder to debug when something goes wrong. Without centralized control, coordination complexity increases, but the system gains resilience and flexibility.
Which approach works better depends largely on whether you need predictability or adaptability for your specific tasks and use cases.
Let’s take a deeper look at the hierarchical structure also know as coordinator-specialist paradigm.
The coordinator breaks down problems and routes work to appropriate agents, while specialized agents focus on specific tasks they’re designed for.
Coordinators need to understand what each of the other agents can do and how to sequence their work. They don’t need to be experts in every domain, but they must know how to orchestrate the specialists effectively to solve complex problems. LangGraph works well for implementing these coordinators in multi-agent systems, as it gives you tools to manage workflow and communication between components.
Specialists just need to be good at their specific tasks—retrieving information, generating code, analyzing data, or interfacing with external systems through tool calling. They don’t need to understand the whole system, just their part in it.
This separation of workloads lets your development teams focus on making each part good at its specific job rather than building one thing that’s mediocre at everything. It’s a more efficient use of engineering resources and leads to better overall system performance.
The most obvious benefit of multi-agent systems is better performance through specialization. Each component in the system can use techniques that make sense for its specific tasks:
Financial analysis agents can use mathematical models and validation rules specific to finance. Code generation agents can incorporate language-specific knowledge without bloating every other part of the system. Planning agents can focus exclusively on creating coherent action sequences. This specialization allows for more sophisticated capabilities in each area.
Our testing consistently shows that specialized agents in multi-agent systems outperform general models on domain-specific tasks. The gap between multi-agent and single agent performance gets bigger as tasks require more specialized knowledge or techniques. Multiple agents working together can bring diverse expertise to bear on complex problems that would overwhelm a single agent.
In addition as each individual workload is smaller the output of multi-agent systems are more predictable.
Multi-agent systems scale better as problems get more complex:
You can run just the components you need for each request instead of firing up an entire massive model every time. This selective activation significantly reduces resource usage and costs. As needs change, you can add more agents with new capabilities without disrupting the existing system.
Development also scales better with multiple agents. Different teams can work on different agents without everybody needing to understand the entire system. This division of labor helps teams move faster as systems grow. It also allows for greater specialization among your engineering team, with experts in different domains focusing on the agents most relevant to their expertise.
The biggest practical advantage of multi-agent systems might be easier maintenance:
When something breaks or needs improvement in a single agent system, you often need to retrain or modify the entire model. With multi-agent systems, you can fix just the specific agent that’s causing problems without touching the rest of the system. This reduces the risk of introducing new bugs when making changes.
You can also incorporate new research or techniques by updating specific agents rather than rebuilding everything. For example, when a better embedding model comes out, you can swap it into your retrieval agent without touching your reasoning or planning components. This incremental upgradeability gives multi-agent systems a significant advantage in fast-moving fields.
Here’s where multi-agent composition is making a difference today across various domains:
Customer Support Systems: Contact centers now use teams of multiple agents to handle customer inquiries. Triage agents classify incoming questions, knowledge agents find relevant information, and conversation agents manage the actual customer interaction. These specialized agents work together to provide better service than any single agent could.
Code Generation Tools: Modern coding assistants use separate agents for different parts of software development. Architecture agents handle planning the overall design, implementation agents write the code, and testing agents verify it works correctly. These AI agents communicate through structured interfaces to produce cohesive results. Microsoft found these multi-agent systems produce code
Research Analysis: Pharmaceutical companies use agent compositions to analyze scientific literature. Document retrieval agents find papers, extraction agents pull out experimental methods and results, and synthesis agents identify patterns across studies. This division of labor among specialized agents finds connections that both human researchers and single agent systems consistently miss.
How agents communicate with other agents is a critical design choice for effective multi-agent systems:
Structured Data Exchange: Frameworks like LangGraph and CrewAI use JSON or similar formats for agent communication. This makes interactions predictable and reduces misunderstandings, but can limit expressiveness. Production systems typically need this reliability to ensure consistent coordination between agents.
Natural Language: Autogen and similar agent systems let agents communicate in natural language. This enables more flexible interactions but introduces ambiguity. It works better for exploratory research than mission-critical applications where precision is essential. Natural language communication can make debugging more challenging but allows for more nuanced information exchange.
Mixed Approaches: Production-grade multi-agent systems increasingly use both structured data and natural language for communication. This gives you the reliability of defined formats for critical information while keeping the flexibility of language for complex concepts. Tool calling often serves as the bridge between these approaches, with structured function calls implemented alongside natural language explanations.
Efficient resource management becomes important as multi-agent systems scale:
Selective Activation: Only run the agents needed for the current task in your multi-agent system. This can cut computational costs considerably compared to always-on approaches that activate all agents simultaneously. With good planning and coordination, you can ensure the right agents are available at the right time.
Prioritized Resources: Give critical agents more computing power and memory, while keeping lightweight agents on leaner resources. This ensures key functions in your multi-agent system remain responsive without overprovisioning the entire environment. Different agents often have very different resource needs based on their specific tasks and capabilities. Additionaly each sub-agent can run on just the right size model 7B, 70B, 405B etc.
Smart Caching: Store and reuse results between agents when possible. This prevents redundant computation, especially for knowledge retrieval where the same questions often recur. Shared knowledge bases can significantly improve efficiency in multi-agent systems, allowing one agent to build on the work of others.
Larger multi-agent systems need orchestration layers to manage these resources effectively, particularly under varying load conditions and in dynamic environments. Proper resource management becomes an increasingly important factor as you scale from a few to many agents.
Multi-agent systems fail differently than single agent models:
Message Problems: Agents can misinterpret other agents or drop information during exchanges. Well-implemented multi-agent systems need standardized error handling and retry mechanisms to prevent these communication failures from breaking the entire workflow. When agents fail to communicate effectively, the overall system performance suffers.
Coordination Failures: Planning agents sometimes assign tasks incorrectly or fail to integrate results properly from multiple agents. Adding verification steps and redundant validation helps catch these issues before they affect final outputs. This is especially important when multiple independent agents are working together on complex tasks.
Consistency Issues: Different specialized agents might produce contradictory outputs. Explicit reconciliation mechanisms become necessary to resolve these conflicts and ensure the overall system maintains coherent behavior. Without good coordination, individual agents might work at cross-purposes.
Production multi-agent systems need monitoring, graceful degradation paths, and fallback options to maintain acceptable performance even when individual components fail. This is more complex than monitoring single agent systems but provides greater resilience.
Agent composition works well with other technical approaches in artificial intelligence:
Symbolic-Neural Combinations: Hybrid agents that combine logical reasoning with pattern recognition are becoming more common in multi-agent systems. These address weaknesses in both approaches used alone, allowing for more capable agents within the broader system.
Feedback-Based Learning: Multi-agent systems that adapt based on human feedback over time, aligning outputs with user preferences through ongoing interaction. This helps the overall system evolve to better meet user needs without explicit reprogramming.
Multi-Modal Processing: Specialized agents for different types of data—images, audio, text—that work together to handle mixed-format information in a shared environment. This allows multi-agent systems to process and reason about the world more like humans do, by integrating multiple sensory channels.
LiquidMetal’s own SmartBuckets product itself is a multi-agent system.
At its foundation, SmartBuckets implements a hierarchical multi-agent architecture with clear separation of responsibilities. The system consists of:
This multi-agent approach enables SmartBuckets to decompose complex retrieval problems into manageable subtasks handled by specialized expert agents. Rather than using a single large model to process all aspects of data retrieval, SmartBuckets distributes cognitive work across purpose-built components.
When a user submits a complex query to their SmartBucket, the system activates its agent composition framework. For example imagine the following query: “Find me all pdfs that talk about financial projections, include results charts and were accessed in the last quarter”.
The multi-agent system attacks the problem as follows:
The Supervisor Agent analyzes the query and breaks it down into discrete tasks for specialized agents.
Specialized agents activate in parallel:
Each agent applies optimized techniques for its task:
The Supervisor Agent aggregates and refines results from all specialists to produce a coherent response.
This division of labor enables each agent to excel at its specific task, handling queries that would overwhelm a single model approach.
Breaking AI systems into specialized components represents a fundamental shift in how we build effective artificial intelligence. Rather than relying on single massive models, teams are creating networks of purpose-built agents that collectively outperform monolithic approaches. These multi-agent systems provide more flexibility, better performance, and easier maintenance.
This approach delivers concrete benefits: better performance on specific tasks, improved scaling as problems grow more complex, and much simpler maintenance and upgrades. The ability to mix and match components gives multi-agent systems advantages that single agent systems simply cannot match.
As the field advances, we’ll see more sophisticated ways for agents to coordinate, automatically configure themselves for different tasks, and combine multiple AI approaches. For teams building AI solutions today, understanding how to effectively implement multi-agent systems isn’t optional—it’s becoming essential for tackling real-world problems across various domains.
The path forward isn’t about building bigger models; it’s about creating smarter teams of specialized agents that communicate effectively to solve problems together. Multi-agent systems provide the practical framework to make that happen.
Single-agent systems rely on one large model to handle all tasks, while multi-agent systems distribute work across specialized components that collaborate. Multi-agent systems excel through specialization, allowing each agent to use techniques optimized for its specific tasks. This approach provides better maintainability, more focused capabilities, and greater resilience when components fail.
When something breaks in a single model system, you typically need to modify or retrain the entire model. With multi-agent systems, you can fix or upgrade specific components without disrupting the whole system. This targeted maintenance reduces the risk of introducing new bugs and makes incremental improvements much easier to implement.
Multi-agent systems typically follow either hierarchical or peer-to-peer structures. Hierarchical systems use manager agents to coordinate specialized workers in a top-down approach (like CrewAI), offering predictable workflows with clear responsibilities. Peer-to-peer systems allow direct communication between any agents (like Autogen), providing more flexibility but potentially increasing debugging complexity.
Communication protocols in multi-agent systems typically use structured data exchange (JSON), natural language, or mixed approaches. Structured formats provide reliability and reduce misunderstandings but limit expressiveness. Natural language enables more flexible interactions but introduces ambiguity. Production systems often combine both approaches, using structured formats for critical information and natural language for complex concepts.
Key challenges include managing coordination complexity, handling communication failures between agents, resolving contradictory outputs from different agents, and efficiently allocating computational resources. As systems scale to dozens or hundreds of agents, these challenges intensify, requiring sophisticated orchestration and monitoring solutions.
SmartBuckets uses a hierarchical multi-agent architecture with a Supervisor Agent that coordinates multiple specialized worker agents. When processing a user query, the system activates specific agents like Metadata Analysis, Semantic Search, Visual Content, PII Detection, and Document Type agents in parallel. Each agent applies optimized techniques for its task, and the Supervisor aggregates their results to produce a coherent response.
Not necessarily. While multi-agent systems involve multiple components, they can be more resource-efficient through selective activation—only running the agents needed for each specific task rather than activating the entire system. This approach reduces computational costs compared to always deploying massive models for every request, especially as problems grow more complex.