Construct a basis mannequin (FM) powered customer support bot with brokers for Amazon Bedrock

From enhancing the conversational expertise to agent help, there are many ways in which generative synthetic intelligence (AI) and basis fashions (FMs) will help ship quicker, higher help. With the growing availability and variety of FMs, it’s tough to experiment and preserve up-to-date with the most recent mannequin variations. Amazon Bedrock is a completely managed service that provides a alternative of high-performing FMs from main AI firms similar to AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon. With Amazon Bedrock’s complete capabilities, you’ll be able to simply experiment with quite a lot of prime FMs, customise them privately along with your knowledge utilizing strategies similar to fine-tuning and Retrieval Augmented Era (RAG).

Brokers for Amazon Bedrock

In July, AWS introduced the preview of agents for Amazon Bedrock, a brand new functionality for builders to create totally managed brokers in a number of clicks. Brokers lengthen FMs to run advanced enterprise duties—from reserving journey and processing insurance coverage claims to creating advert campaigns and managing stock—all with out writing any code. With totally managed brokers, you don’t have to fret about provisioning or managing infrastructure.

On this put up, we offer a step-by-step information with constructing blocks to create a customer support bot. We use a textual content era mannequin (Anthropic Claude V2) and brokers for Amazon Bedrock for this resolution. We offer an AWS CloudFormation template to provision the assets wanted for constructing this resolution. Then we stroll you thru steps to create an agent for Amazon Bedrock.

ReAct Prompting

FMs decide learn how to clear up user-requested duties with a way known as ReAct. It’s a basic paradigm that mixes reasoning and appearing with FMs. ReAct prompts FMs to generate verbal reasoning traces and actions for a activity. This permits the system to carry out dynamic reasoning to create, keep, and alter plans for appearing whereas incorporating extra data into the reasoning. The structured prompts embrace a sequence of question-thought-action-observation examples.

  • The query is the user-requested activity or drawback to unravel.
  • The thought is a reasoning step that helps show to the FM learn how to sort out the issue and establish an motion to take.
  • The motion is an API that the mannequin can invoke from an allowed set of APIs.
  • The statement is the results of finishing up the motion.

Elements in brokers for Amazon Bedrock

Behind the scenes, brokers for Amazon Bedrock automate the immediate engineering and orchestration of user-requested duties. They’ll securely increase the prompts with company-specific data to supply responses again to the consumer in pure language. The agent breaks the user-requested activity into a number of steps and orchestrates subtasks with the assistance of FMs. Motion teams are duties that the agent can carry out autonomously. Motion teams are mapped to an AWS Lambda perform and associated API schema to carry out API calls. The next diagram depicts the agent construction.

Agents for Amazon Bedrock components

Resolution overview

We use a shoe retailer use case to construct the customer support bot. The bot helps clients buy footwear by offering choices in a humanlike dialog. Clients converse with the bot in pure language with a number of steps invoking exterior APIs to perform subtasks. The next diagram illustrates the pattern course of stream.

Sequence diagram for use case

The next diagram depicts a high-level structure of this resolution.

Solution architecture diagram

  1. You possibly can create an agent with Amazon Bedrock-supported FMs similar to Anthropic Claude V2.
  2. Connect API schema, residing in an Amazon Simple Storage Service (Amazon S3) bucket, and a Lambda perform containing the enterprise logic to the agent. (Observe: It is a one-time setup step.)
  3. The agent makes use of buyer requests to create a immediate utilizing the ReAct framework. It, then, makes use of the API schema to invoke corresponding code within the Lambda perform.
  4. You possibly can carry out quite a lot of duties, together with sending electronic mail notifications, writing to databases, and triggering software APIs within the Lambda capabilities.

On this put up, we use the Lambda perform to retrieve buyer particulars, checklist footwear matching customer-preferred exercise, and at last, place orders. Our code is backed by an in-memory SQLite database. You need to use related constructs to write down to a persistent knowledge retailer.


To implement the answer offered on this put up, you must have an AWS account and entry to Amazon Bedrock with brokers enabled (at the moment in preview). Use AWS CloudFormation template to create the useful resource stack wanted for the answer.

us-east-1 CloudFormation stack

The CloudFormation template creates two IAM roles. Replace these roles to use least-privilege permissions as mentioned in Security best practices. Click on here to be taught what IAM options can be found to make use of with brokers for Amazon Bedrock.

  1. LambdaBasicExecutionRole with Amazon S3 full entry and CloudWatch entry for logging.
  2. AmazonBedrockExecutionRoleForAgents with Amazon S3 full entry and Lambda full entry.

Vital: Brokers for Amazon Bedrock should have the function identify prefixed by AmazonBedrockExecutionRoleForAgents_*

Bedrock Brokers setup

Within the subsequent two sections, we are going to stroll you thru creating and testing an agent.

Create an agent for Amazon Bedrock

To create an agent, open the Amazon Bedrock console and select Brokers within the left navigation pane. Then choose Create Agent.

This begins the agent creation workflow.

  1. Present agent particulars: Give the agent a reputation and outline (non-obligatory). Choose the service function created by the CloudFormation stack and choose Subsequent.

Agent details

  1. Choose a basis mannequin: Within the Choose mannequin display, you choose a mannequin. Present clear and exact directions to the agent about what duties to carry out and learn how to work together with the customers.

Select foundation model

  1. Add motion teams: An motion is a activity the agent can carry out by making API calls. A set of actions comprise an motion group. You present an API schema that defines all of the APIs within the motion group. You will need to present an API schema within the OpenAPI schema JSON format. The Lambda perform accommodates the enterprise logic wanted to carry out API calls. You will need to affiliate a Lambda perform to every motion group.

Give the motion group a reputation and an outline for the motion. Choose the Lambda perform, present an API schema file and choose Subsequent.

Agent action groups

  1. Within the ultimate step, evaluation the agent configuration and choose Create Agent.

Check and deploy brokers for Amazon Bedrock

  1. Check the agent: After the agent is created, a dialog field exhibits the agent overview together with a working draft. The Amazon Bedrock console offers a UI to check your agent.

  1. Deploy: After profitable testing, you’ll be able to deploy your agent. To deploy an agent in your software, you have to create an alias. Amazon Bedrock then mechanically creates a model for that alias.

The next actions happen with the previous agent setup and the Lambda code supplied with this put up:

  1. The agent creates a immediate from the developer-provided directions (similar to “You might be an agent that helps clients buy footwear.”), API schemas wanted to finish the duties, and knowledge supply particulars. The automated immediate creation saves weeks of experimenting with prompts for various FMs.
  2. The agent orchestrates the user-requested activity, similar to “I’m searching for footwear,” by breaking it into smaller subtasks similar to getting buyer particulars, matching the customer-preferred exercise with shoe exercise, and inserting shoe orders. The agent determines the appropriate sequence of duties and handles error situations alongside the best way.

The next screenshot shows some instance responses from the agent.

Agent sample responses

By choosing Present hint for every response, a dialog field exhibits the reasoning method utilized by the agent and the ultimate response generated by the FM.

Agent trace1

Agent trace2

Agent trace3


To keep away from incurring future expenses, delete the assets. You are able to do this by deleting the stack from the CloudFormation console.

Delete CloudFormation stack

Be at liberty to obtain and check the code used on this put up from the GitHub agents for Amazon Bedrock repository. You may as well invoke the brokers for Amazon Bedrock programmatically; an example Jupyter Notebook is offered within the repository.


Brokers for Amazon Bedrock will help you improve productiveness, enhance your customer support expertise, or automate DevOps duties. On this put up, we confirmed you learn how to arrange brokers for Amazon Bedrock to create a customer support bot.

We encourage you to be taught extra by reviewing additional features of Amazon Bedrock. You need to use the instance code offered on this put up to create your implementation. Attempt our workshop to achieve hands-on expertise with Amazon Bedrock.

Concerning the Authors

Amit AroraAmit Arora is an AI and ML Specialist Architect at Amazon Net Providers, serving to enterprise clients use cloud-based machine studying providers to quickly scale their improvements. He’s additionally an adjunct lecturer within the MS knowledge science and analytics program at Georgetown College in Washington D.C.

Manju PrasadManju Prasad is a Senior Options Architect inside Strategic Accounts at Amazon Net Providers. She focuses on offering technical steerage in quite a lot of domains, together with AI/ML to a marquee M&E buyer. Previous to becoming a member of AWS, she has labored for firms within the Monetary Providers sector and in addition a startup.

Archana InapudiArchana Inapudi is a Senior Options Architect at AWS supporting Strategic Clients. She has over a decade of expertise serving to clients design and construct knowledge analytics, and database options. She is obsessed with utilizing expertise to supply worth to clients and obtain enterprise outcomes.

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