AWS Machine Learning Blog

Category: Amazon Bedrock Agents

Design multi-agent orchestration with reasoning using Amazon Bedrock and open source frameworks

This post provides step-by-step instructions for creating a collaborative multi-agent framework with reasoning capabilities to decouple business applications from FMs. It demonstrates how to combine Amazon Bedrock Agents with open source multi-agent frameworks, enabling collaborations and reasoning among agents to dynamically execute various tasks. The exercise will guide you through the process of building a reasoning orchestration system using Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Bedrock Agents, and FMs. We also explore the integration of Amazon Bedrock Agents with open source orchestration frameworks LangGraph and CrewAI for dispatching and reasoning.

Syngenta develops a generative AI assistant to support sales representatives using Amazon Bedrock Agents

In this post, we explore how Syngenta collaborated with AWS to develop Cropwise AI, a generative AI assistant powered by Amazon Bedrock Agents that helps sales representatives make better seed product recommendations to farmers across North America. The solution transforms the seed selection process by simplifying complex data into natural conversations, providing quick access to detailed seed product information, and enabling personalized recommendations at scale through a mobile app interface.

Getting started with Amazon Bedrock Agents custom orchestrator

In this post, we explore how Amazon Bedrock Agents simplify the orchestration of generative AI workflows, particularly with the introduction of the custom orchestrator feature. You can use the custom orchestrator to fine-tune and optimize agentic workflows that align more closely with specific business and operational needs. We outline the feature’s key benefits, including full control over orchestration, real-time adjustments, and reusability, followed by a breakdown of how it manages state transitions and contract-based interactions between Amazon Bedrock Agents and AWS Lambda.

Use Amazon Bedrock Agents for code scanning, optimization, and remediation

For enterprises in the realm of cloud computing and software development, providing secure code repositories is essential. As sophisticated cybersecurity threats become more prevalent, organizations must adopt proactive measures to protect their assets. Amazon Bedrock offers a powerful solution by automating the process of scanning repositories for vulnerabilities and remediating them. This post explores how you can use Amazon Bedrock to enhance the security of your repositories and maintain compliance with organizational and regulatory standards.

Flow diagram of custom hallucination detection and mitigation : The user's question is fed to a search engine (with optional LLM-based step to pre-process it to a good search query). The documents or snippets returned by the search engine, together with the user's question, are inserted into a prompt template - and an LLM generates a final answer based on the retrieved documents. The final answer can be evaluated against the reference answer from the dataset to get a custom hallucination score. Based on a pre-defined empirical threshold, a customer service agent is requested to join the conversation using SNS notification

Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents

This post demonstrates how to use Amazon Bedrock Agents, Amazon Knowledge Bases, and the RAGAS evaluation metrics to build a custom hallucination detector and remediate it by using human-in-the-loop. The agentic workflow can be extended to custom use cases through different hallucination remediation techniques and offers the flexibility to detect and mitigate hallucinations using custom actions.