Today, we’re announcing the general availability (GA) of multi-agent collaboration on Amazon Bedrock. This capability allows developers to build, deploy, and manage networks of AI agents that work together to execute complex, multi-step workflows efficiently.
Since its preview launch at re:Invent 2024, organizations across industries—including financial services, healthcare, supply chain and logistics, manufacturing, and customer support—have used multi-agent collaboration to orchestrate specialized agents, driving efficiency, accuracy, and automation. With this GA release, we’ve introduced enhancements based on customer feedback, further improving scalability, observability, and flexibility—making AI-driven workflows easier to manage and optimize.
What is multi-agent collaboration?
Generative AI is no longer just about models generating responses, it’s about automation. The next wave of innovation is driven by agents that can reason, plan, and act autonomously across company systems. Generative AI applications are no longer just generating content; they also take action, solve problems, and execute complex workflows. The shift is clear: businesses need AI that doesn’t just respond to prompts but orchestrates entire workflows, automating processes end to end.
Agents enable generative AI applications to perform tasks across company systems and data sources, and Amazon Bedrock already simplifies building them. With Amazon Bedrock, customers can quickly create agents that handle sales orders, compile financial reports, analyze customer retention, and much more. However, as applications become more capable, the tasks customers want them to perform can exceed what a single agent can manage—either because the tasks require specialized expertise, involve multiple steps, or demand continuous execution over time.
Coordinating potentially hundreds of agents at scale is also challenging, because managing dependencies, ensuring efficient task distribution, and maintaining performance across a large network of specialized agents requires sophisticated orchestration. Without the right tools, businesses can face inefficiencies, increased latency, and difficulties in monitoring and optimizing performance. For customers looking to advance their agents and tackle more intricate, multi-step workflows, Amazon Bedrock supports multi-agent collaboration, enabling developers to easily build, deploy, and manage multiple specialized agents working together seamlessly.
Multi-agent collaboration enables developers to create networks of specialized agents that communicate and coordinate under the guidance of a supervisor agent. Each agent contributes its expertise to the larger workflow by focusing on a specific task. This approach breaks down complex processes into manageable sub-tasks processed in parallel. By facilitating seamless interaction among agents, Amazon Bedrock enhances operational efficiency and accuracy, ensuring workflows run more effectively at scale. Because each agent only accesses the data required for its role, this approach minimizes exposure of sensitive information while reinforcing security and governance. This allows businesses to scale their AI-driven workflows without the need for manual intervention in coordinating agents. As more agents are added, the supervisor ensures smooth collaboration between them all.
By using multi-agent collaboration on Amazon Bedrock, organizations can:
- Streamline AI-driven workflows by distributing workloads across specialized agents. Improve execution efficiency by parallelizing tasks where possible. Enhance security and governance by restricting agent access to only necessary data. Reduce operational complexity by eliminating manual intervention in agent coordination.
A key challenge in building effective multi-agent collaboration systems is managing the complexity and overhead of coordinating multiple specialized agents at scale. Amazon Bedrock simplifies the process of building, deploying, and orchestrating effective multi-agent collaboration systems while addressing efficiency challenges through several key features and optimizations:
- Quick setup – Create, deploy, and manage AI agents working together in minutes without the need for complex coding. Composability – Integrate your existing agents as subagents within a larger agent system, allowing them to seamlessly work together to tackle complex workflows. Efficient inter-agent communication – The supervisor agent can interact with subagents using a consistent interface, supporting parallel communication for more efficient task completion. Optimized collaboration modes – Choose between supervisor mode and supervisor with routing mode. With routing mode, the supervisor agent will route simple requests directly to specialized subagents, bypassing full orchestration. For complex queries or when no clear intention is detected, it automatically falls back to the full supervisor mode, where the supervisor agent analyzes, breaks down problems, and coordinates multiple subagents as needed. Integrated trace and debug console – Visualize and analyze multi-agent interactions behind the scenes using the integrated trace and debug console.
What’s new in general availability?
The GA release introduces several key enhancements based on customer feedback, making multi-agent collaboration more scalable, flexible, and efficient:
- Inline agent support – Enables the creation of supervisor agents dynamically at runtime, allowing for more flexible agent management without predefined structures. AWS CloudFormation and AWS Cloud Development Kit (AWS CDK) support – Enables customers to deploy agent networks as code, enabling scalable, reusable agent templates across AWS accounts. Enhanced traceability and debugging – Provides structured execution logs, sub-step tracking, and Amazon CloudWatch integration to improve monitoring and troubleshooting. Increased collaborator and step count limits – Expands self-service limits for agent collaborators and execution steps, supporting larger-scale workflows. Payload referencing – Reduces latency and costs by allowing the supervisor agent to reference external data sources without embedding them in the agent request. Improved citation handling – Enhances accuracy and attribution when agents pull external data sources into their responses.
These features collectively improve coordination capabilities, communication speed, and overall effectiveness of the multi-agent collaboration framework in tackling complex, real-world problems.
Multi-agent collaboration across industries
Multi-agent collaboration is already transforming AI automation across sectors:
- Investment advisory – A financial firm uses multiple agents to analyze market trends, risk factors, and investment opportunities to deliver personalized client recommendations. Retail operations – A retailer deploys agents for demand forecasting, inventory tracking, pricing optimization, and order fulfillment to increase operational efficiency. Fraud detection – A banking institution assigns agents to monitor transactions, detect anomalies, validate customer behaviors, and flag potential fraud risks in real time. Customer support – An enterprise customer service platform uses agents for sentiment analysis, ticket classification, knowledge base retrieval, and automated responses to enhance resolution times. Healthcare diagnosis – A hospital system integrates agents for patient record analysis, symptom recognition, medical imaging review, and treatment plan recommendations to assist clinicians.
Deep dive: Syngenta’s use of multi-agent collaboration
Syngenta, a global leader in agricultural innovation, has integrated cutting-edge generative AI into its Cropwise service, resulting in the development of Cropwise AI. This advanced system is designed to enhance the efficiency of agronomic advisors and growers by providing tailored recommendations for crop management practices.
Business challenge
The agricultural sector faces the complex task of optimizing crop yields while ensuring sustainability and profitability. Farmers and agronomic advisors must consider a multitude of factors, including weather patterns, soil conditions, crop growth stages, and potential pest and disease threats. In the past, analyzing these variables required extensive manual effort and expertise. Syngenta recognized the need for a more efficient, data-driven approach to support decision-making in crop management.
Solution: Cropwise AI
To address these challenges, Syngenta collaborated with AWS to develop Cropwise AI, using Amazon Bedrock Agents to create a multi-agent system that integrates various data sources and AI capabilities. This system offers several key features:
- Advanced seed recommendation and placement – Uses predictive machine learning algorithms to deliver personalized seed recommendations tailored to each grower’s unique environment. Sophisticated predictive modeling – Employs state-of-the-art machine learning algorithms to forecast crop growth patterns, yield potential, and potential risk factors by integrating real-time data with comprehensive historical information. Precision agriculture optimization – Provides hyper-localized, site-specific recommendations for input application, minimizing waste and maximizing resource efficiency.
Agent architecture
Cropwise AI is built on AWS architecture and designed for scalability, maintainability, and security. The system uses Amazon Bedrock Agents to orchestrate multiple AI agents, each specializing in distinct tasks:
- Data aggregation agent – Collects and integrates extensive datasets, including over 20 years of weather history, soil conditions, and more than 80,000 observations on crop growth stages. Recommendation agent – Analyzes the aggregated data to provide tailored recommendations for precise input applications, product placement, and strategies for pest and disease control. Conversational AI agent – Uses a multilingual conversational large language model (LLM) to interact with users in natural language, delivering insights in a clear format.
This multi-agent collaboration enables Cropwise AI to process complex agricultural data efficiently, offering actionable insights and personalized recommendations to enhance crop yields, sustainability, and profitability.
Results
By implementing Cropwise AI, Syngenta has achieved significant improvements in agricultural practices:
- Enhanced decision-making: Agronomic advisors and growers receive data-driven recommendations, leading to optimized crop management strategies. Increased yields: Utilizing Syngenta’s seed recommendation models, Cropwise AI helps growers increase yields by up to 5%. Sustainable practices: The system promotes precision agriculture, reducing waste and minimizing environmental impact through optimized input applications.
Highlighting the significance of this advancement, Feroz Sheikh, Chief Information and Digital Officer at Syngenta Group, stated:
“Agricultural innovation leader Syngenta is using Amazon Bedrock Agents as part of its Cropwise AI solution, which gives growers deep insights to help them optimize crop yields, improve sustainability, and drive profitability. With multi-agent collaboration, Syngenta will be able to use multiple agents to further improve their recommendations to growers, transforming how their end-users make decisions and delivering even greater value to the farming community.”
This collaboration between Syngenta and AWS exemplifies the transformative potential of generative AI and multi-agent systems in agriculture, driving innovation and supporting sustainable farming practices.
How multi-agent collaboration works
Amazon Bedrock automates agent collaboration, including task delegation, execution tracking, and data orchestration. Developers can configure their system in one of two collaboration modes:
- Supervisor mode
- The supervisor agent receives an input, breaks down complex requests, and assigns tasks to specialized sub-agents. Sub-agents execute tasks in parallel or sequentially, returning responses to the supervisor, which consolidates the results.
- Simple queries are routed directly to a relevant sub-agent. Complex or ambiguous requests trigger the supervisor to coordinate multiple agents to complete the task.
Watch the Amazon Bedrock multi-agent collaboration video to learn how to get started.
Conclusion
By enabling seamless multi-agent collaboration, Amazon Bedrock empowers businesses to scale their generative AI applications with greater efficiency, accuracy, and flexibility. As organizations continue to push the boundaries of AI-driven automation, having the right tools to orchestrate complex workflows will be essential. With Amazon Bedrock, companies can confidently build AI systems that don’t just generate responses but drive real impact—automating processes, solving problems, and unlocking new possibilities across industries.
Amazon Bedrock multi-agent collaboration is now generally available.
- Learn more: Automate tasks in your application using AI agents Code samples: Amazon Bedrock agent samples on GitHub Try it out today in the AWS Management Console for Amazon Bedrock.
Multi-agent collaboration opens new possibilities for AI-driven automation. Whether in finance, healthcare, retail, or agriculture, Amazon Bedrock helps organizations scale AI workflows with efficiency and precision.
Start building today—and let us know what you create!
About the authors
Sri Koneru has spent the last 13.5 years honing her skills in both cutting-edge product development and large-scale infrastructure. At Salesforce for 7.5 years, she had the incredible opportunity to build and launch brand new products from the ground up, reaching over 100,000 external customers. This experience was instrumental in her professional growth. Then, at Google for 6 years, she transitioned to managing critical infrastructure, overseeing capacity, efficiency, fungibility, job scheduling, data platforms, and spatial flexibility for all of Alphabet. Most recently, Sri joined Amazon Web Services leveraging her diverse skillset to make a significant impact on AI/ML services and infrastructure at AWS. Personally, Sri & her husband recently became empty nesters, relocating to Seattle from the Bay Area. They’re a basketball-loving family who even catch pre-season Warriors games but are looking forward to cheering on the Seattle Storm this year. Beyond basketball, Sri enjoys cooking, recipe creation, reading, and her newfound hobby of hiking. While she’s a sun-seeker at heart, she is looking forward to experiencing the unique character of Seattle weather.
