Enhance agentic workflows with enterprise search using Kore.ai and Amazon Q Business

This post was written with Meghana Chintalapudi and Surabhi Sankhla of Kore.ai.

As organizations struggle with exponentially growing volumes of data distributed across multiple repositories and applications, employees lose significant time—approximately 30% according to the International Data Corporation (IDC)—searching for information that could be spent on higher-value work. The complexity of modern enterprise data networks demands solutions that can efficiently integrate, process, and deliver actionable insights across disparate systems.

In this post, we demonstrate how organizations can enhance their employee productivity by integrating Kore.ai’s AI for Work platform with Amazon Q Business. We show how to configure AI for Work as a data accessor for Amazon Q index for independent software vendors (ISVs), so employees can search enterprise knowledge and execute end-to-end agentic workflows involving search, reasoning, actions, and content generation. We explore the key benefits of this integration, including advanced search capabilities across more than 90 enterprise connectors and how to extend agentic experiences on top of a search foundation. The post includes a step-by-step implementation guide to help you set up this integration in your environment.

Components of the integration

Kore.ai is a leading Enterprise AI platform consistently recognized by Gartner as a leader in conversational AI. With three key Kore.ai offerings, AI for Work, AI for Process, and AI for Service, enterprises can build and deploy AI solutions based on their business needs. The AI for Work platform helps employees be more productive by making it possible to search across applications, take context-aware actions, generate content, and automate repetitive tasks. The platform goes beyond standalone search to deliver comprehensive agentic orchestration and workflows, helping employees follow up with clients, send weekly updates, or research and write marketing content with a single command. With AI for Work, your employees can create simple no-code agents while your admins have the flexibility to create more advanced low-code or pro-code agents. AI for Process, on the other hand, automates knowledge-intensive business processes end-to-end. AI for Service helps organizations deliver differentiated customer service experiences through self-service, proactive outreach campaigns, and agent assistance.

Amazon Q index for ISVs is a powerful, managed vector search service that supports seamless integration of generative AI applications with customers’ enterprise data through a unified, secure index. ISVs can access and retrieve relevant content through the SearchRelevantContent API for cross-application data retrieval without needing direct access or individual indexing of each data source, while customers retain full control over data access and governance.

When combined with additional search connectors offered by AI for Work platform and its ability to create and orchestrate agents, organizations gain a complete solution that transforms how employees access enterprise data and execute tasks end-to-end. The following video shows one such agentic experience in action, where the AI for Work interface seamlessly orchestrates agents to help a sales executive prepare for a client meeting—compiling information from Amazon Q index and AI for Work connectors, summarizing talking points, and sending them as an email, all from a single query.

Benefits for enterprises

Enterprises often struggle with fragmented data access and repetitive manual tasks that slow down critical business processes. For example, imagine a scenario where a product manager needs to compile quarterly feature requests—with the integration of Kore.ai’s AI for Work and Amazon Q index, they can instantly gather requests from Salesforce, support tickets, and JIRA; automatically generate a structured roadmap; and schedule stakeholder meetings, all with a single query. This seamless integration changes the way enterprises interact with enterprise systems, through multiple key advantages:

• Improved search capabilities – Amazon Q index augments the generative AI experience by providing semantically relevant enterprise content across connected systems through its distributed vector database, delivering query responses at enterprise scale. Now, together with AI for Work, your employees can search data from over 90 connectors, integrating with enterprise systems like Microsoft 365, Salesforce, and Workday while also connecting with custom internal knowledge systems and third-party search providers. AI for Work’s orchestrator manages complex query processing and agent routing across multiple data sources, resulting in contextually appropriate and actionable results that significantly reduce search time while also enabling intelligent automations that extend far beyond traditional search capabilities.
• Enhanced data processing – The system continuously ingests and analyzes data through the document processing pipeline in Amazon Q index, which automatically handles multiple formats using intelligent chunking algorithms that preserve semantic context. The AI for Work platform unifies search, content generation, and actions in a single interface, to support the creation of multi-step agentic experiences grounded in search. Through real-time incremental indexing that processes only changed content, the system maintains data freshness while converting siloed raw data into actionable insights and multi-step business processes that can be saved and reused across the organization.
• Cost optimization – Organizations can achieve significant cost savings by streamlining routine tasks through agents that reduce operational overhead and improve resource allocation. AI for Work supports a wide range of agent-building options, from no-code and low-code to pro-code, for both non-technical employees and technical experts to build agents for themselves and to share across the organization, so teams can accomplish more with existing resources and benefit from sustained productivity improvements.
• Security benefits – Security remains paramount, with Amazon Q index implementing vector-level security through end-to-end encryption using AWS Key Management Service (AWS KMS) customer managed keys and document-level access controls that filter search results based on user identity and group membership. The joint solution implements robust role-based access control and audit trails. This zero-trust security approach maintains compliance with industry standards while providing granular control over sensitive enterprise data, making sure users only see information from documents they have explicit permissions to access while maintaining complete data sovereignty. With AI for Work’s robust security and governance tools enterprises can manage permissions and agent access, monitor usage, and enforce guardrails for secure, enterprise-wide deployment of AI solutions at scale.

Solution overview

The Amazon Q Business data accessor provides a secure interface that integrates Kore.ai’s AI for Work platform with Amazon Q index. The integration delivers a robust solution that uses enterprise data across multiple systems to power intelligent agentic actions and content generation capabilities that transform how organizations handle routine tasks and automate complex processes end-to-end.

When a user submits a query through AI for Work, its orchestrator intelligently routes requests between Kore.ai’s native retrievers and Amazon Q index based on predefined routing rules and advanced intent recognition algorithms. For Amazon Q index requests, the architecture implements secure cross-account API calls using OAuth 2.0 tokens that transform into temporary AWS credentials, supporting both security and optimal performance while maintaining strict access controls throughout the entire system. With AI for Work’s agents, users can take follow up actions, such as drafting proposals or submitting tickets—directly on top of search results, for end-to-end task completion in a single interface. Users can also build personalized workflows of pre-defined steps and execute them from a single query to further save time.

This supports use cases such as automated roadmap generation, where a product manager can query feature requests across multiple systems and receive a structured roadmap complete with stakeholder notifications, or RFP response automation, where sales executives can generate comprehensive proposals by pulling compliance documentation and tailoring responses based on client requirements.

The following diagram illustrates the solution architecture.

Prerequisites

Before enabling the Amazon Q index integration with Kore.ai’s AI for Work, you must have the following components in place:

• An AWS account with appropriate service access
• Amazon Q Business set up with AWS IAM Identity Center for user authentication
• Access to Kore.ai’s AI for Work (as a workspace admin)

With these prerequisites met, you can complete the basic configuration steps on both the Amazon Q Business and Kore.ai consoles to get started.

Add Kore.ai as a data accessor

After creating an Amazon Q Business application with AWS IAM Identity Center, administrators can configure Kore.ai as a data accessor through the Amazon Q Business console. Complete the following steps:

1. On the Amazon Q Business console, choose Data accessors in the navigation pane.
2. Choose Add data accessor.
3. Choose Kore.ai as your data accessor. You must retrieve tenantID, a unique identifier for your application tenant. Refer to Prerequisites for instructions to retrieve the TenantId for your application. Similar instructions are also listed later in this post.
4. For Data source access, configure your level of access. You can select specific data sources from your Amazon Q index to be available through the data accessor. This makes it possible to control which content is surfaced in the AI for Work environment.
5. For User access, specify which users or groups can access the Amazon Q index through the data accessor. This option makes it possible to configure granular permissions for data accessor accessibility and manage organizational access controls.

After you have added the data accessor, the Amazon Q Business console displays configuration details that you need to share with Kore.ai to complete the setup.

6. Note down the following information for the next step:
   1. Amazon Q Business application ID
   2. AWS Region of the Amazon Q Business application
   3. Amazon Q Business retriever ID
   4. Region for IAM Identity Center instance

Configure Amazon Q index in Kore.ai’s AI for Work

Kore.ai’s AI for Work supports flexible integration with Amazon Q index based on your enterprise search needs. There are two configuration options: configuring Amazon Q index as the primary enterprise knowledge source or configuring it as a search agent. We provide instructions for both options in this post.

Option 1: Configure Amazon Q index as the primary enterprise knowledge source

If you want Amazon Q index to act as the primary fallback search layer, coming into play, complete the following steps:

1. In AI for Work, go to Workspaces on the admin console. Then navigate to Enterprise Workspace, which is the default workspace.

2. Choose Configure to configure an enterprise knowledge data source.
3. On the Create New dropdown menu, choose Amazon Q.

4. Enter a source name and brief description.
5. Copy the tenant ID displayed—this is required during the setup of the data accessor in AWS, as described in the previous section.
6. Enter the details captured earlier:
   1. Amazon Q Business application ID
   2. Region of the Amazon Q Business application
   3. Amazon Q Business retriever ID
   4. Region for IAM Identity Center instance
7. Choose Continue to save and complete the configuration.

The new knowledge source now shows as Active.

Option 2: Configure Amazon Q index as a search agent

If you already have a primary search index, you can configure Amazon Q index as a search agent:

1. In AI for Work, go to Workspaces on the admin console.
2. Choose the workspace where you want to add Amazon Q index. (Enterprise Workspace is used by default).
3. Under AI Agents in the navigation pane, choose Search Agent
4. Choose Create agent.

5. Provide an agent name and purpose. This helps define when the search agent should be invoked.
6. Choose Continue to move to configuration.
7. For Select Search Index, choose Amazon Q.

8. Copy the tenant ID displayed—it is required during the setup of the data accessor in AWS.

9. Preview and test the agent.
10. After you have validated the agent, publish it to selected users or groups.

Your integration is now complete. You can now access the assistant application and start asking questions in the AI for Work console. If you’ve created a search agent, you can also access it from the list of agents and start interacting with it directly.

Clean up

When you are finished using this solution, clean up your resources to avoid additional costs:

1. Disable the Amazon Q index configuration within AI for Work’s settings.
2. Delete the Kore.ai data accessor from the Amazon Q Business console, which will remove permissions and access for users.
3. Delete the Amazon Q Business application to remove the associated index and data source connectors, on your AWS account.

Conclusion

The combination of Kore.ai’s AI for Work and Amazon Q index offers enterprises a transformative approach to boost employee productivity leveraging comprehensive search capabilities while streamlining repetitive tasks and processes. By integrating Kore.ai’s advanced agentic platform with the robust search infrastructure of Amazon Q index, organizations can now execute context aware actions by accessing relevant information across disparate systems while maintaining data ownership and security. This supports faster problem-solving, enhanced productivity, and better collaboration across the organization.

In this post, we explored how enterprises can use the integration between Kore.ai’s AI for Work and Amazon Q Business to streamline their operational processes and unlock valuable productivity gains. We demonstrated how organizations can set up this integration using an Amazon Q data accessor, helping teams access critical information securely and cost-effectively.

Unlock the full potential of your organization’s data and agentic workflows today with the Amazon Q index and Kore.ai’s AI for Work’s unified solution by following the steps in Amazon Q integration with AI for Work.

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About the authors

Siddhant Gupta is a Software Development Manager on the Amazon Q team based in Seattle, WA. He is driving innovation and development in cutting-edge AI-powered solutions.

Chinmayee Rane is a Generative AI Specialist Solutions Architect at AWS, with a core focus on generative AI. She helps ISVs accelerate the adoption of generative AI by designing scalable and impactful solutions. With a strong background in applied mathematics and machine learning, she specializes in intelligent document processing and AI-driven innovation. Outside of work, she enjoys salsa and bachata dancing.

Bobby Williams is a Senior Solutions Architect at AWS. He has decades of experience designing, building, and supporting enterprise software solutions that scale globally. He works on solutions across industry verticals and horizontals and is driven to create a delightful experience for every customer.

Santhosh Urukonda is a Senior PACE (Prototyping & Cloud Engineering) Architect at AWSs with two decades of experience. He specializes in helping customers develop innovative, first-to-market solutions with a focus on generative AI.

Nikhil Kumar Goddeti is a Cloud Support Engineer II at AWS. He specializes in AWS Data Analytics services with emphasis on Amazon OpenSearch Service, Amazon Q Business, Amazon Kinesis, Amazon MSK, Amazon AppFlow, and Amazon Kendra. He is a Subject Matter Expert of OpenSearch. Outside of work, he enjoys travelling with his friends and playing cricket.

Meghana Chintalapudi is a Product Manager at Kore.ai, driving the development of search and agentic AI solutions for the AI for Work platform. She has led large-scale AI implementations for Fortune 500 clients, evolving from deterministic NLP and intent-detection models to advanced large language model deployments, with a strong emphasis on enterprise-grade security and scalability. Outside of work, Meghana is a dancer and takes movement workshops in Hyderabad, India.

Surabhi Sankhla is a VP of Product at Kore.ai, where she leads the AI for Work platform to help enterprises boost employee productivity. With over 13 years of experience in product management and technology, she has launched AI products from the ground up and scaled them to millions of users. At Kore.ai, she drives product strategy, client implementations, and go-to-market execution in partnership with cross-functional teams. Based in San Francisco, Surabhi is passionate about making AI accessible and impactful for all.