Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.

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Today’s customers demand more than just quick answers. They want interactions that feel natural, intuitive, and tailored to their individual preferences. Generative artificial intelligence (AI) offers the potential to transform customer service by empowering agents with intelligent tools that can understand complex queries, generate human-like responses, and even anticipate future customer needs. Customers have invested in building external systems that use generative AI to enhance and streamline customer experiences. Integrating Amazon Connect chat with third-party AI allows customers to extend their in-house generative AI applications and provide a seamless experience.
By integrating Amazon Connect chat with Amazon Bedrock or third-party AI tools, businesses can harness the power of generative AI to enhance their customer interactions. This integration allows for sophisticated and natural conversations, enabling companies to provide personalized support that goes beyond simple FAQs.
At Adobe Inc., “building our own bots has been crucial for scaling our Digital Media customer service automation. Amazon Connect’s seamless integration with our in-house bots through custom bot participants has revolutionized our customer experience. By harnessing this innovative feature, we’ve unlocked unprecedented efficiency and agility, empowering our agents to deliver personalized, timely support like never before. With Amazon Connect, our custom bots seamlessly blend into our workflow, ensuring smooth interactions and unparalleled customer satisfaction.”
In this blog post, we will demonstrate the above pattern by integrating an AI virtual agent that is backed by Amazon Bedrock for generative AI-powered messaging using the Claude v2 foundation model into your Amazon Connect chat flows using the recently rereleased CreateParticipant API. Through the integration of custom participants, you’ll be able to infuse a personal touch into your chat conversations, as the same pattern can be extended to integrate with your desired AI tools giving your customers a more personalized experience.
The architecture (Figure 1.a) integrates the Amazon Connect chat flow with a custom generative AI chatbot powered by the Amazon Bedrock Claude v2 foundation model, enabling AI-driven responses to customer queries via the chat channel. This design enables customers to integrate the Amazon Bedrock Claude v2 foundation model and any 3rd party AI solution of their choice that is supported by Amazon Bedrock.

Figure 1.a: Solution architecture – conversation flows in the order from A through G.
In the sequence diagram shown below (figure 1.b), end user starts the new Amazon Connect chat flow thereby invoking the “Custom Bot Example” entry contact flow.
The entry flow greets the user and invokes the AWS Lambda Function “StartBot” which is responsible for three main operations: (1) adding the custom participant to the chat (via CreateParticipant API), (2) starting the chat streaming (via StartContactStreaming API with the Amazon Simple Notification Service topic as the streaming endpoint) and (3) initiating the first message back to the customer (via SendMessage API). On successful initialization, the customer and the custom participant are connected to the chat session.
The end user can now interact by asking questions in the chat. The questions are published to the SNS topic and delivered to the chatBot AWS Lambda function via SNS trigger. The chatBot Lambda function posts the customer’s messages as inputs to Amazon Bedrock Claude v2 foundation model (via InvokeModel API) and returns the generative AI-powered responses back to the customer (via SendMessage API).
Lastly, all the chat contacts and participant connection details are stored in the chatContacts Amazon DynamoDB table.
Figure 1.b: Sequence diagram that illustrates how the customer interaction flows and processed by different AWS services.
For this walkthrough, you should have the following prerequisites:
For deploying the solution, execute the following steps in the same region where your Amazon Connect instance is deployed:

Figure 2: Custom Bot Example Contact Flow.
Figure 3: Claude model access

Figure 4: Request model access

Figure 5: Review and submit screen
Once installed you can use the test chat within the Amazon Connect console using the below steps:
Figure 8 shows screenshots of the sample interaction of the chat where Amazon Bedrock is the Custom Participant (the AI bot). It uses Claude foundation model to provide the answers to your questions.
Disclaimer: The response text a customer sees will not 100% match, since this is using non-deterministic models. So, the response for the same below question can be different in your case.
Figure 8: Example chat interaction
To avoid incurring future charges, delete the resources using following steps:
In this post, we demonstrated how to add custom participants to Amazon Connect chat flows and integrate Amazon Bedrock for generative AI capabilities. This approach can be extended to work with any third-party application that supports APIs—such as surveys, external bots, or CAPTCHAs—allowing businesses to create more personalized and interactive chat experiences for their customers.
This flexibility opens endless possibilities for enhancing customer interactions, from collecting feedback to handling complex inquiries more efficiently. However, there are important considerations, such as ensuring the AI is trained on relevant data, introducing guardrails to maintain response accuracy, and monitoring costs to balance value with investment. With careful planning, these integrations can significantly elevate customer engagement while maintaining control over the interaction quality and costs.
Amit Bagga is an Amazon Connect Specialty Consultant with Amazon Web Services Professional Services group. With over 15 years of contact center experience, Amit is passionate about diving deep with customers in building solutions that is simple but yet powerful and most importantly delivers the utmost customer benefit.
Michael Goligorsky is a Senior Solutions Architect with Amazon Web Services. With 25+ years of enterprise IT experience in Fortune 100 companies, Michael is passionate about diving deep with customers to architect creative solutions to some of the most complex challenges in cloud computing. In his spare time, he can be found traveling the world with his family.
Charles Phillips is a Senior Specialty Consultant with over 20 years of experience developing web applications and contact center solutions. Leveraging his deep technical knowledge and extensive experience, he ensures customers receive solutions tailored to both their current needs and future growth. As part of the AWS Professional Services group, Charles specializes in providing Amazon Connect contact center solutions customized for each client’s unique requirements.
Karl White is a Senior Amazon Connect Specialty Consultant within the Amazon Web Services (AWS) Professional Services group. With 23 years of experience in the contact center domain, Karl has dedicated his career to the art and science of customer engagement. He is passionate about leveraging Amazon Connect to craft dynamic, configuration-driven experiences to solve complex customer problems.

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