Amazon Bedrock, a fully managed service for building and deploying large language models (LLMs), has recently introduced a groundbreaking feature: Agents. These intelligent agents empower developers to create sophisticated, multi-step applications that can seamlessly interact with users, access diverse data sources, and execute complex tasks. In this blog post, we’ll explore the latest advancements in Agents for Amazon Bedrock, including their ability to retain memory and interpret code, and how these capabilities can transform the way we approach generative AI applications.
Memory retention: fostering adaptive and personalised experiences
One of the key enhancements to Agents for Amazon Bedrock is the introduction of memory retention. This feature allows agents to maintain a persistent memory of their interactions with users, enabling them to adapt and provide a more personalised experience over time. By retaining a summary of previous conversations, agents can pick up where they left off, ensuring a smooth and seamless flow of interactions, particularly for complex, multi-step tasks.
Imagine a user booking a flight with an agent. With memory retention, the agent can learn the user’s travel preferences and use that knowledge to streamline subsequent booking requests, automatically proposing the right seat or meal options based on the user’s previous choices. This level of context-awareness not only enhances the user experience but also simplifies business process automation, as agents can now maintain awareness of previous and ongoing interactions with the same customer without the need for custom integrations.
Code interpretation: expanding the capabilities of generative AI
Another significant advancement in Agents for Amazon Bedrock is the introduction of code interpretation. This feature empowers agents to dynamically generate and execute code snippets within a secure, sandboxed environment, significantly expanding the range of tasks they can address. From data analysis and visualisation to text processing, equation solving, and optimisation problems, code interpretation enables agents to tackle complex challenges that were previously beyond their scope.