Unlocking complex problem-solving with multi-agent collaboration on Amazon Bedrock Raphael Shu AWS Machine Learning Blog

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Large language model (LLM) based AI agents that have been specialized for specific tasks have demonstrated great problem-solving capabilities. By combining the reasoning power of multiple intelligent specialized agents, multi-agent collaboration has emerged as a powerful approach to tackle more intricate, multistep workflows.

The concept of multi-agent systems isn’t entirely new—it has its roots in distributed artificial intelligence research dating back to the 1980s. However, with recent advancements in LLMs, the capabilities of specialized agents have significantly expanded in areas such as reasoning, decision-making, understanding, and generation through language and other modalities. For instance, a single attraction research agent can perform web searches and list potential destinations based on user preferences. By creating a network of specialized agents, we can combine the strengths of multiple specialist agents to solve increasingly complex problems, such as creating and optimizing an entire travel plan by considering weather forecasts in nearby cities, traffic conditions, flight and hotel availability, restaurant reviews, attraction ratings, and more.

The research team at AWS has worked extensively on building and evaluating the multi-agent collaboration (MAC) framework so customers can orchestrate multiple AI agents on Amazon Bedrock Agents. In this post, we explore the concept of multi-agent collaboration (MAC) and its benefits, as well as the key components of our MAC framework. We also go deeper into our evaluation methodology and present insights from our studies. More technical details can be found in our technical report.

Benefits of multi-agent systems

Multi-agent collaboration offers several key advantages over single-agent approaches, primarily stemming from distributed problem-solving and specialization.

Distributed problem-solving refers to the ability to break down complex tasks into smaller subtasks that can be handled by specialized agents. By breaking down tasks, each agent can focus on a specific aspect of the problem, leading to more efficient and effective problem-solving. For example, a travel planning problem can be decomposed into subtasks such as checking weather forecasts, finding available hotels, and selecting the best routes.

The distributed aspect also contributes to the extensibility and robustness of the system. As the scope of a problem increases, we can simply add more agents to extend the capability of the system rather than try to optimize a monolithic agent packed with instructions and tools. On robustness, the system can be more resilient to failures because multiple agents can compensate for and even potentially correct errors produced by a single agent.

Specialization allows each agent to focus on a specific area within the problem domain. For example, in a network of agents working on software development, a coordinator agent can manage overall planning, a programming agent can generate correct code and test cases, and a code review agent can provide constructive feedback on the generated code. Each agent can be designed and customized to excel at a specific task.

For developers building agents, this means the workload of designing and implementing an agentic system can be organically distributed, leading to faster development cycles and better quality. Within enterprises, often development teams have distributed expertise that is ideal for developing specialist agents. Such specialist agents can be further reused by other teams across the entire organization.

In contrast, developing a single agent to perform all subtasks would require the agent to plan the problem-solving strategy at a high level while also keeping track of low-level details. For example, in the case of travel planning, the agent would need to maintain a high-level plan for checking weather forecasts, searching for hotel rooms and attractions, while simultaneously reasoning about the correct usage of a set of hotel-searching APIs. This single-agent approach can easily lead to confusion for LLMs because long-context reasoning becomes challenging when different types of information are mixed. Later in this post, we provide evaluation data points to illustrate the benefits of multi-agent collaboration.

A hierarchical multi-agent collaboration framework

Here’s an explanation of each of the components of the multi-agent team:

• Supervisor agent – This is an agent that coordinates a network of specialized agents. It’s responsible for organizing the overall workflow, breaking down tasks, and assigning subtasks to specialist agents. In our framework, a supervisor agent can assign and delegate tasks, however, the responsibility of solving the problem won’t be transferred.
• Specialist agents – These are agents with specific expertise, designed to handle particular aspects of a given problem.
• Inter-agent communication – Communication is the key component of multi-agent collaboration, allowing agents to exchange information and coordinate their actions. We use a standardized communication protocol that allows the supervisor agents to send and receive messages to and from the specialist agents.
• Payload referencing – This mechanism enables efficient sharing of large content blocks (like code snippets or detailed travel itineraries) between agents, significantly reducing communication overhead. Instead of repeatedly transmitting large pieces of data, agents can reference previously shared payloads using unique identifiers. This feature is particularly valuable in domains such as software development.
• Routing mode – For simpler tasks, this mode allows direct routing to specialist agents, bypassing the full orchestration process to improve efficiency for latency-sensitive applications.

The following figure shows inter-agent communication in an interactive application. The user first initiates a request to the supervisor agent. After coordinating with the subagents, the supervisor agent returns a response to the user.

Evaluation of multi-agent collaboration: A comprehensive approach

Evaluating the effectiveness and efficiency of multi-agent systems presents unique challenges due to several complexities:

1. Users can follow up and provide additional instructions to the supervisor agent.
2. For many problems, there are multiple ways to resolve them.
3. The success of a task often requires an agentic system to correctly perform multiple subtasks.

Conventional evaluation methods based on matching ground-truth actions or states often fall short in providing intuitive results and insights. To address this, we developed a comprehensive framework that calculates success rates based on automatic judgments of human-annotated assertions. We refer to this approach as “assertion-based benchmarking.” Here’s how it works:

• Scenario creation – We create a diverse set of scenarios across different domains, each with specific goals that an agent must achieve to obtain success.
• Assertions – For each scenario, we manually annotate a set of assertions that must be true for the task to be considered successful. These assertions cover both user-observable outcomes and system-level behaviors.
• Agent and user simulation We simulate the behavior of the agent in a sandbox environment, where the agent is asked to solve the problems described in the scenarios. Whenever user interaction is required, we use an independent LLM-based user simulator to provide feedback.
• Automated evaluation – We use an LLM to automatically judge whether each assertion is true based on the conversation transcript.
• Human evaluation – Instead of using LLMs, we ask humans to directly judge the success based on simulated trajectories.

Here is an example of a scenario and corresponding assertions for assertion-based benchmarking:

• Goals:
  • User needs the weather conditions expected in Las Vegas for tomorrow, January 5, 2025.
  • User needs to search for a direct flight from Denver International Airport to McCarran International Airport, Las Vegas, departing tomorrow morning, January 5, 2025.
• Assertions:
  • User is informed about the weather forecast for Las Vegas tomorrow, January 5, 2025.
  • User is informed about the available direct flight options for a trip from Denver International Airport to McCarran International Airport in Las Vegas for tomorrow, January 5, 2025.
    get_tomorrow_weather_by_city is triggered to find information on the weather conditions expected in Las Vegas tomorrow, January 5, 2025.
  • search_flights is triggered to search for a direct flight from Denver International Airport to McCarran International Airport departing tomorrow, January 5, 2025.

For better user simulation, we also include additional contextual information as part of the scenario. A multi-agent collaboration trajectory is judged as successful only when all assertions are met.

Key metrics

Our evaluation framework focuses on evaluating a high-level success rate across multiple tasks to provide a holistic view of system performance:

Goal success rate (GSR) – This is our primary measure of success, indicating the percentage of scenarios where all assertions were evaluated as true. The overall GSR is aggregated into a single number for each problem domain.

Evaluation results

The following table shows the evaluation results of multi-agent collaboration on Amazon Bedrock Agents across three enterprise domains (travel planning, mortgage financing, and software development):

All experiments are conducted in a setting where the supervisor agents are driven by Anthropic’s Claude 3.5 Sonnet models.

Comparing to single-agent systems

We also conducted an apples-to-apples comparison with the single-agent approach under equivalent settings. The MAC approach achieved a 90% success rate across all three domains. In contrast, the single-agent approach scored 60%, 80%, and 53% in the travel planning, mortgage financing, and software development datasets, respectively, which are significantly lower than the multi-agent approach. Upon analysis, we found that when presented with many tools, a single agent tended to hallucinate tool calls and failed to reject some out-of-scope requests. These results highlight the effectiveness of our multi-agent system in handling complex, real-world tasks across diverse domains.

To understand the reliability of the automatic judgments, we conducted a human evaluation on the same scenarios to investigate the correlation between the model and human judgments and found high correlation on end-to-end GSR.

Comparison with other frameworks

To understand how our MAC framework stacks up against existing solutions, we conducted a comparative analysis with a widely adopted open source framework (OSF) under equivalent conditions, with Anthropic’s Claude 3.5 Sonnet driving the supervisor agent and Anthropic’s Claude 3.0 Sonnet driving the specialist agents. The results are summarized in the following figure:

These results demonstrate a significant performance advantage for our MAC framework across all the tested domains.

Best practices for building multi-agent systems

The design of multi-agent teams can significantly impact the quality and efficiency of problem-solving across tasks. Among the many lessons we learned, we found it crucial to carefully design team hierarchies and agent roles.

Design multi-agent hierarchies based on performance targets
It’s important to design the hierarchy of a multi-agent team by considering the priorities of different targets in a use case, such as success rate, latency, and robustness. For example, if the use case involves building a latency-sensitive customer-facing application, it might not be ideal to include too many layers of agents in the hierarchy because routing requests through multiple tertiary agents can add unnecessary delays. Similarly, to optimize latency, it’s better to avoid agents with overlapping functionalities, which can introduce inefficiencies and slow down decision-making.

Define agent roles clearly
Each agent must have a well-defined area of expertise. On Amazon Bedrock Agents, this can be achieved through collaborator instructions when configuring multi-agent collaboration. These instructions should be written in a clear and concise manner to minimize ambiguity. Moreover, there should be no confusion in the collaborator instructions across multiple agents because this can lead to inefficiencies and errors in communication.

The following is a clear, detailed instruction:

Trigger this agent for 1) searching for hotels in a given location, 2) checking availability of one or multiple hotels, 3) checking amenities of hotels, 4) asking for price quote of one or multiple hotels, and 5) answering questions of check-in/check-out time and cancellation policy of specific hotels.

The following instruction is too brief, making it unclear and ambiguous.

Trigger this agent for helping with accommodation.

The second, unclear, example can lead to confusion and lower collaboration efficiency when multiple specialist agents are involved. Because the instruction doesn’t explicitly define the capabilities of the hotel specialist agent, the supervisor agent may overcommunicate, even when the user query is out of scope.

Conclusion

Multi-agent systems represent a powerful paradigm for tackling complex real-world problems. By using the collective capabilities of multiple specialized agents, we demonstrate that these systems can achieve impressive results across a wide range of domains, outperforming single-agent approaches.

Multi-agent collaboration provides a framework for developers to combine the reasoning power of numerous AI agents powered by LLMs. As we continue to push the boundaries of what is possible, we can expect even more innovative and complex applications, such as networks of agents working together to create software or generate financial analysis reports. On the research front, it’s important to explore how different collaboration patterns, including cooperative and competitive interactions, will emerge and be applied to real-world scenarios.

Additional references

• Amazon Bedrock Agents
• Sample codes for using multi-agent collaboration on Amazon Bedrock Agents

About the author

Raphael Shu is a Senior Applied Scientist at Amazon Bedrock. He received his PhD from the University of Tokyo in 2020, earning a Dean’s Award. His research primarily focuses on Natural Language Generation, Conversational AI, and AI Agents, with publications in conferences such as ICLR, ACL, EMNLP, and AAAI. His work on the attention mechanism and latent variable models received an Outstanding Paper Award at ACL 2017 and the Best Paper Award for JNLP in 2018 and 2019. At AWS, he led the Dialog2API project, which enables large language models to interact with the external environment through dialogue. In 2023, he has led a team aiming to develop the Agentic capability for Amazon Titan. Since 2024, Raphael worked on multi-agent collaboration with LLM-based agents.

Nilaksh Das is an Applied Scientist at AWS, where he works with the Bedrock Agents team to develop scalable, interactive and modular AI systems. His contributions at AWS have spanned multiple initiatives, including the development of foundational models for semantic speech understanding, integration of function calling capabilities for conversational LLMs and the implementation of communication protocols for multi-agent collaboration. Nilaksh completed his PhD in AI Security at Georgia Tech in 2022, where he was also conferred the Outstanding Dissertation Award.

Michelle Yuan is an Applied Scientist on Amazon Bedrock Agents. Her work focuses on scaling customer needs through Generative and Agentic AI services. She has industry experience, multiple first-author publications in top ML/NLP conferences, and strong foundation in mathematics and algorithms. She obtained her Ph.D. in Computer Science at University of Maryland before joining Amazon in 2022.

Monica Sunkara is a Senior Applied Scientist at AWS, where she works on Amazon Bedrock Agents. With over 10 years of industry experience, including 6.5 years at AWS, Monica has contributed to various AI and ML initiatives such as Alexa Speech Recognition, Amazon Transcribe, and Amazon Lex ASR. Her work spans speech recognition, natural language processing, and large language models. Recently, she worked on adding function calling capabilities to Amazon Titan text models. Monica holds a degree from Cornell University, where she conducted research on object localization under the supervision of Prof. Andrew Gordon Wilson before joining Amazon in 2018.

Dr. Yi Zhang is a Principal Applied Scientist at AWS, Bedrock. With 25 years of combined industrial and academic research experience, Yi’s research focuses on syntactic and semantic understanding of natural language in dialogues, and their application in the development of conversational and interactive systems with speech and text/chat. He has been technically leading the development of modeling solutions behind AWS services such as Bedrock Agents, AWS Lex, HealthScribe, etc.

“}]] The research team at AWS has worked extensively on building and evaluating the multi-agent collaboration (MAC) framework so customers can orchestrate multiple AI agents on Amazon Bedrock Agents. In this post, we explore the concept of multi-agent collaboration (MAC) and its benefits, as well as the key components of our MAC framework. We also go deeper into our evaluation methodology and present insights from our studies.  Read More Amazon Bedrock, Amazon Bedrock Agents, Amazon Machine Learning, Artificial Intelligence