Automate Tasks Effortlessly with AI Agents on Amazon Bedrock

Today, automation is no longer a luxury but a necessity. From streamlining customer interactions to optimizing internal processes, businesses are increasingly turning to AI-powered solutions to stay competitive. Amazon Bedrock is at the forefront of this revolution, offering a platform that simplifies access to foundation models (FMs) without the need for extensive machine learning expertise.

One of the most exciting features of Amazon Bedrock is the ability to create and deploy AI agents. These agents are designed to automate complex tasks, making it easier than ever to integrate intelligent workflows into your business operations. Whether it’s handling customer inquiries, analyzing large datasets, or even drafting personalized content, AI agents can do the heavy lifting for you.

In this blog, we’ll walk through the basics of Amazon Bedrock while exploring the capabilities of AI agents. We’ll also provide a step-by-step guide to help you build and deploy your first task-automating AI solution.

What is Amazon Bedrock?

Amazon Bedrock is a fully managed GenAI service that offers a choice of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, Mistral AI, and Amazon. Everything is accessible through a single API, along with a broad set of capabilities, including security and privacy. With Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case while also customizing them with your own data.

Techniques such as fine-tuning, Retrieval Augmented Generation (RAG), and AI agents can be used with your enterprise systems and data sources to produce highly accurate results for your GenAI applications. Because Amazon Bedrock is serverless, there’s no need to worry about managing infrastructure. You can securely integrate and deploy generative AI capabilities into your existing applications using the AWS services you are already familiar with.

What are AI Agents?

AI agents are intelligent, autonomous systems designed to perform specific tasks with minimal human intervention. Powered by FMs, these agents can understand, reason, and act on instructions, making them ideal for automating repetitive or complex workflows.

Unlike traditional automation tools, which require predefined rules and rigid programming, AI agents leverage natural language processing (NLP) and machine learning to adapt to a wide range of inputs. This flexibility enables them to handle tasks like answering customer queries, extracting insights from documents, generating content, or even integrating with other systems to complete multi-step operations.

Key Features of AI Agents on Amazon Bedrock:

• Extend FMs to understand user requests and break tasks into smaller, manageable steps
• Collect additional information from users through natural conversation
• Take actions to fulfill customer requests by making external/internal API calls
• Enhance performance and accuracy by querying data sources

Agent Configuration

Model and Instructions

Selecting the right model is perhaps the most critical configuration step when building your AI agent on Amazon Bedrock. The choice of model determines how well your agent can perform specific tasks and respond to user inputs. Currently, Amazon and Anthropic are the two model providers optimized for Bedrock agents, each offering unique strengths depending on your use case.

Once you’ve selected the appropriate model, you can provide clear instructions in natural language to guide the agent’s behavior. These instructions serve as the framework for how the agent interacts with users and executes tasks. For example, a prompt like, “You are an assistant at a large financial institution. You are friendly, polite, and knowledgeable. You will assist in managing an existing stock portfolio” can be used to set the tone and scope of the agent's duties. By carefully crafting these instructions, you ensure that your agent is aligned with your business goals and delivers the desired outcomes with precision and consistency.

Additional Configuration

Several additional configuration options can be applied to enhance your agent's performance and accuracy. For example, you can enable a code interpreter so your agent can handle tasks like writing, running, testing, and troubleshooting code in a secure environment.

AI agents on Amazon Bedrock can also be configured to proactively request additional information from users when needed. This ensures more accurate and context-aware function predictions. Your agent can prompt for extra details when it lacks sufficient context to respond confidently to an utterance. This can happen if, for example, the agent doesn’t understand the user’s query or needs to clarify specific aspects of the query to make a better decision or generate a more accurate answer. This feature is critical as it not only increases the accuracy of the agent but also helps to avoid hallucinations—situations where the agent generates irrelevant or incorrect responses. By guiding users to provide the necessary inputs, your AI agent becomes a reliable partner in executing tasks effectively, even in cases with incomplete or ambiguous information.

Another powerful feature is the ability to retain conversational context across multiple sessions using memory. This capability allows agents to track user interactions over time, enabling more personalized and seamless experiences. For example, an agent assisting a portfolio manager can remember previous inquiries or preferences, ensuring continuity in the conversation and reducing the need for repetitive explanations. This memory retention is valuable for enhancing user satisfaction and automating complex workflows that require multi-step decision-making. By leveraging memory, your AI agent becomes more intelligent, adaptive, and aligned with real-world user needs.

Action Groups

Action groups in Amazon Bedrock allow you to define the specific actions your AI agent can perform, making it more capable and aligned with your use case. An action group is essentially a set of preconfigured tasks or operations that the agent can execute based on user inputs or triggers. For example, an action group might include tasks like sending an email, retrieving data from a database, processing a transaction, or generating a report. By clearly defining these actions, you create a structured framework for your agent, ensuring it performs tasks accurately and efficiently. Action groups not only enhance the agent's functionality but also give you precise control over what the agent is allowed to do, reducing the risk of errors or undesired behavior.

Currently, agents can perform two forms of actions: Lambda functions and APIs. You must define all the parameters of your actions, and their format depends on the type (parameter names and OpenAPI schema). Depending on your use case, you can easily integrate with an existing API or choose to create a custom Lambda function.

Pricing

Agents rely on Bedrock FMs and follow the same pricing system as when using the Bedrock API for inference calls (for more details, see https://aws.amazon.com/bedrock/pricing/). Be sure to set limits for both input and output to prevent excessive charges if someone submits a prompt with a very large context to your agent.

Additionally, if you have action groups that use AWS Lambda functions, you will incur additional costs based on their execution time. Configurations such as timeout settings and concurrency limits can help you manage these costs and avoid unexpected expenses.

Conclusion

The ability to automate tasks with AI agents on Amazon Bedrock is a game-changer for businesses of all sizes. By combining the power of advanced FMs with a user-friendly interface, Bedrock empowers you to deploy intelligent solutions without the need for complex infrastructure or deep machine learning expertise.

Whether you’re looking to enhance customer experiences, streamline internal workflows, or innovate in new areas, AI agents can help you achieve these goals efficiently and at scale. As we’ve seen, getting started is straightforward: define your objectives, select the right foundation model, and integrate the agent into your operations.