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Multi-Agent Collaboration Goes GA in Amazon Bedrock
Multi-agent collaboration on the Amazon Bedrock AI service on the Amazon Web Services (AWS) cloud is now generally available.
First announced last year at the AWS re:Invent 2024 conference, it furthers the company’s agentic AI story, one of the hottest areas of AI development. In agentic AI, systems exhibit autonomy, agency, and decision-making capabilities, enabling them to act independently, make choices, and adapt to changing environments with little supervision.
In fact, a “supervisor agent” provides its own guidance to AI underlings in the AWS scheme.
“Multi-agent collaboration enables developers to create networks of specialized agents that communicate and coordinate under the guidance of a supervisor agent,” the company said in a March 10 announcement. “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.”
These supervisor agents lead the company’s list of what’s new in the GA release, with features based on customer feedback to make 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.
An example demo detailed in the announcement also enlists other kinds of agents in addition to supervisor agents:
- 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.
More information is provided in an Agents for Amazon Bedrock video and Automate tasks in your application using AI agents guidance, along with Amazon Bedrock agent samples on GitHub.
