As organizations continue to integrate more AI agents into their business workflows, the challenge of improving performance without increasing infrastructure costs has become increasingly prominent.
McKinsey’s State of AI 2025 report reveals that 62% of organizations are already experimenting with AI agents, highlighting the growing importance of designing efficient multi-agent systems.
As adoption rates rise, organizations are exploring the potential benefits of using smaller models within multi-agent systems to achieve faster responses and lower costs without compromising performance.
In this article, we will delve into the concept of smaller models within multi-agent architectures, explore where they are most effective, and discuss the trade-offs to consider when building scalable and production-ready AI systems.
What Are Smaller Models in Multi-Agent Systems?
Smaller models within multi-agent systems are lightweight language models that are specifically designed to handle specialized tasks. By assigning these tasks to smaller models, organizations can achieve faster execution and lower costs without relying solely on a single large model for every decision.
A multi-agent system comprises multiple AI agents collaborating to complete a workflow. Each agent is assigned a specific role, such as information retrieval, document summarization, output validation, or interfacing with external tools.
Instead of burdening a high-capacity model with every task, organizations are increasingly opting for smaller models for predictable or repetitive tasks, reserving larger models for more complex reasoning and decision-making processes.
For instance, in a customer support workflow, different AI agents might use different types of models based on their responsibilities, as shown in the table below:
AI Agent Recommended Model Type
Query Classification Small model
Knowledge Retrieval Small model
Response Validation Small model
Complex Reasoning Large model
Final Response Generation Large model
This hybrid approach enables organizations to optimize performance without compromising on response quality.
Why Smaller Models Are Gaining Popularity for Multi-Agent Systems
Smaller models are becoming increasingly popular due to their ability to offer faster inference, lower operational costs, and greater scalability for specialized AI tasks.
In enterprise environments where thousands of AI interactions occur daily, running every request through a large language model can quickly become costly. Smaller models provide a more efficient alternative for tasks that do not require deep reasoning or extensive contextual understanding.
Smaller models are particularly effective for tasks such as intent classification, data extraction, content routing, information retrieval, tool selection, output formatting, and workflow orchestration.
For example, in an AI-powered document processing system, a smaller model can quickly identify invoice numbers or extract dates from forms without the need for a large model. This allows the larger model to focus on more complex reasoning tasks later in the workflow.
By adopting a task-specific approach, organizations can enhance overall system efficiency while managing infrastructure costs more effectively.
How Smaller Models Enhance Multi-Agent System Performance
Assigning specialized tasks to smaller models can lead to reduced latency, improved resource utilization, and enhanced scalability for multi-agent systems.
One of the key advantages of multi-agent architectures is the ability for different agents to use different models based on the complexity of their responsibilities. This flexibility allows organizations to balance speed, accuracy, and cost by applying the most suitable model to each task.
Smaller models contribute to improved performance in several ways, as outlined below:
Benefit Business Impact
Lower latency Faster response times
Reduced inference costs Lower operational expenses
Higher throughput Support for more concurrent users
Better resource allocation Reserve larger models for reasoning
Improved scalability Efficient handling of enterprise workloads
For example, an AI research assistant analyzing hundreds of documents can benefit from smaller models retrieving, classifying, and summarizing relevant content before passing only the most critical information to a larger model for synthesis. This layered approach reduces unnecessary computation while maintaining high-quality outputs.
By aligning model size with task complexity, organizations can develop AI systems that are both responsive and cost-effective.
When Larger Models Are Still Superior to Smaller Models
Despite the advantages of smaller models, they may not be suitable for every AI task. Larger language models remain essential for tasks requiring complex reasoning, long-context understanding, and nuanced decision-making.
Tasks involving strategic planning, advanced coding, multi-step reasoning, or domain-specific analysis often necessitate the broader knowledge and reasoning capabilities of larger models.
Large models are typically better suited for tasks such as complex business analysis, long-context document understanding, strategic planning, multi-step reasoning, advanced code generation, creative content generation, scientific and legal analysis, among others.
For instance, while a smaller model can classify customer support requests, resolving a complex technical issue that demands interpretation of multiple knowledge sources and detailed solutions may require the capabilities of a larger model.
The most effective multi-agent systems leverage both model types, assigning each task to the most suitable model based on its complexity, rather than solely relying on either small or large models.
Cost vs. Performance: Selecting the Right Model for Multi-Agent Systems
Choosing the appropriate model for a multi-agent system involves striking a balance between accuracy, speed, and cost, rather than opting for the largest available model every time.
Each AI task has unique computational requirements. While large language models excel at tasks requiring intensive reasoning, they also consume more computing resources and increase inference costs.
On the other hand, smaller models are adept at handling repetitive and well-defined tasks efficiently, making them a cost-effective choice for many enterprise workflows.
Cost optimization has emerged as a significant focus for enterprise AI initiatives. According to McKinsey, 80% of high-performing organizations prioritize efficiency in their AI initiatives, underscoring the importance of model selection as a key architectural decision, rather than simply selecting the largest model available.
A practical decision-making framework involves asking critical questions related to task complexity, request volume, quality thresholds, latency targets, business risks, and deployment constraints. Evaluating these factors can help organizations determine the most suitable model for each task within their multi-agent system.
The final decision should be informed by task-specific testing, where the same evaluation set is run through a smaller model, a larger model, and a hybrid system. Comparing quality, total cost, latency, accuracy, retries, tool accuracy, and escalation rate can aid in selecting the optimal production design.
Instead of deliberating between smaller and larger models, organizations should focus on choosing the most appropriate model for each stage of the workflow to achieve optimal results.
Measuring the Cost and Speed of a Multi-Agent System
Teams should calculate both cost and latency across the entire workflow to gain a comprehensive understanding of efficiency.
Total workflow cost can be calculated as the sum of model input cost, model output cost, tool cost, infrastructure cost, retry cost, and human-review cost. On the other hand, end-to-end latency can be determined by summing routing time, critical-path model time, tool time, validation time, and retry time.
Cost per successful task is calculated by dividing the total workflow cost by the number of correctly completed tasks.
It’s crucial to recognize that a cheaper model call does not necessarily equate to a cheaper workflow if the system requires multiple retries or frequent escalations. Similarly, a faster model may not result in a quicker workflow if tool execution and agent handoffs dominate the critical path.
Creating a standardized evaluation set that represents common, difficult, ambiguous, and high-risk requests can help test every architecture consistently. Recording metrics such as P50 latency, P95 latency, task success rate, cost per successful task, tool-call correctness, retry rate, and safety failures can provide valuable insights for optimizing multi-agent system performance.
Best Practices for Developing Efficient Multi-Agent Systems
Efficient multi-agent systems thrive on assigning the right model to the right task while ensuring seamless collaboration among specialized AI agents.
Simply replacing every large model with a smaller one may not yield the best outcomes. Instead, organizations should focus on designing workflows where each agent performs a clearly defined responsibility using the most suitable model.
Some best practices for developing efficient multi-agent systems include:
Assigning specialized roles to each AI agent
Utilizing smaller models for repetitive and high-volume tasks
Reserving larger models for reasoning and decision-making
Building modular workflows that allow independent model updates
Regularly monitoring latency, cost, and response quality
Evaluating workflows with real business scenarios before deployment
For instance, an enterprise research assistant can leverage different agents for tasks such as document retrieval, content summarization, source verification, and final report generation. This modular approach enhances efficiency while minimizing unnecessary AI computations.
Challenges of Using Smaller Models in Multi-Agent Architectures
While smaller models offer efficiency gains, they also introduce limitations that organizations must address when designing production-ready AI systems.
Compact models typically possess fewer parameters and a lower reasoning capacity than frontier models, potentially hindering tasks requiring deep contextual understanding, complex planning, or domain-specific expertise.
Some common challenges associated with smaller models include limited reasoning ability, reduced contextual understanding, lower performance on highly specialized tasks, greater reliance on high-quality prompts, and additional orchestration complexity.
To mitigate these limitations, organizations can integrate smaller models with larger reasoning models in a hybrid architecture. This approach enables them to optimize both performance and cost without compromising the overall quality of AI outputs.
How Professionals Can Learn to Develop Production-Ready Multi-Agent Systems
Building efficient multi-agent systems demands more than just knowledge of language models. Professionals must also possess hands-on experience with orchestration frameworks, evaluation techniques, retrieval systems, and deployment strategies.
While experimenting with open-source frameworks serves as a solid starting point, many developers find it challenging to design scalable multi-agent architectures that deliver reliable performance in production environments.
An effective learning path should encompass topics such as multi-agent architecture design, agent orchestration frameworks, Retrieval-Augmented Generation (RAG), tool integration and APIs, memory management, agent evaluation and observability, performance optimization, and production deployment.
Engaging in real-world projects can offer invaluable insights, enabling professionals to grasp how these components function within enterprise AI systems.
For professionals seeking structured guidance, the AI Agents course by Johns Hopkins University provides hands-on learning focused on constructing production-ready AI agents.
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The curriculum covers essential topics such as Agentic RAG, LangGraph, multi-agent systems, reinforcement learning, observability, evaluation, and real-world AI workflows, equipping learners with practical skills aligned with current industry demands.
Final Thoughts
Smaller models can significantly enhance the speed and cost-effectiveness of multi-agent systems when applied to the appropriate tasks.
Rather than replacing large language models, smaller models are most effective when utilized alongside them to strike a balance between speed, accuracy, and operational costs in production AI workflows.
Professionals aiming to develop production-ready multi-agent systems should focus on hands-on training in multi-agent architectures, Agentic RAG, LangGraph, and real-world AI projects.
The AI Agents Certificate Program by Johns Hopkins University provides a structured pathway for acquiring production-ready AI engineering skills.
Frequently Asked Questions
1. Can smaller models completely supplant large language models in multi-agent systems?
While smaller models excel at specialized tasks such as classification, retrieval, routing, and data extraction, they may not be able to entirely replace large language models. Large models remain essential for complex reasoning, long-context understanding, and strategic decision-making. Organizations often achieve optimal results by combining both types of models to balance performance, speed, and cost.
2. Why are smaller models deemed more cost-effective for multi-agent systems?
Smaller models typically demand fewer computational resources, resulting in lower inference costs and faster response times. By allocating them to repetitive or well-defined tasks, organizations can reduce operational expenses while reserving larger models for tasks requiring advanced reasoning.
3. How can organizations select between smaller and larger models?
The selection process hinges on task complexity. Smaller models are ideal for routine, high-volume workflows, while larger models shine in tasks involving multi-step reasoning, detailed analysis, or complex content generation. Matching model size to task complexity often delivers the most favorable balance of cost and performance.
4. What skills are essential for constructing efficient multi-agent systems?
Professionals should possess a solid understanding of AI agent orchestration, large language models, Retrieval-Augmented Generation (RAG), APIs, memory management, evaluation, observability, and deployment. Hands-on experience with frameworks like LangGraph and production workflows is crucial for developing enterprise-ready multi-agent systems.
