Build a dynamic, role-based AI agent using Amazon Bedrock inline agents

AI agents continue to gain momentum, as businesses use the power of generative AI to reinvent customer experiences and automate complex workflows. We are seeing Amazon Bedrock Agents applied in investment research, insurance claims processing, root cause analysis, advertising campaigns, and much more. Agents use the reasoning capability of foundation models (FMs) to break down user-requested tasks into multiple steps. They use developer-provided instructions to create an orchestration plan and carry out that plan by securely invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to accurately handle the user’s request.

Although organizations see the benefit of agents that are defined, configured, and tested as managed resources, we have increasingly seen the need for an additional, more dynamic way to invoke agents. Organizations need solutions that adjust on the fly—whether to test new approaches, respond to changing business rules, or customize solutions for different clients. This is where the new inline agents capability in Amazon Bedrock Agents becomes transformative. It allows you to dynamically adjust your agent’s behavior at runtime by changing its instructions, tools, guardrails, knowledge bases, prompts, and even the FMs it uses—all without redeploying your application.

In this post, we explore how to build an application using Amazon Bedrock inline agents, demonstrating how a single AI assistant can adapt its capabilities dynamically based on user roles.

Inline agents in Amazon Bedrock Agents

This runtime flexibility enabled by inline agents opens powerful new possibilities, such as:

• Rapid prototyping – Inline agents minimize the time-consuming create/update/prepare cycles traditionally required for agent configuration changes. Developers can instantly test different combinations of models, tools, and knowledge bases, dramatically accelerating the development process.
• A/B testing and experimentation – Data science teams can systematically evaluate different model-tool combinations, measure performance metrics, and analyze response patterns in controlled environments. This empirical approach enables quantitative comparison of configurations before production deployment.
• Subscription-based personalization – Software companies can adapt features based on each customer’s subscription level, providing more advanced tools for premium users.
• Persona-based data source integration – Institutions can adjust content complexity and tone based on the user’s profile, providing persona-appropriate explanations and resources by changing the knowledge bases associated to the agent on the fly.
• Dynamic tool selection – Developers can create applications with hundreds of APIs, and quickly and accurately carry out tasks by dynamically choosing a small subset of APIs for the agent to consider for a given request. This is particularly helpful for large software as a service (SaaS) platforms needing multi-tenant scaling.

Inline agents expand your options for building and deploying agentic solutions with Amazon Bedrock Agents. For workloads needing managed and versioned agent resources with a pre-determined and tested configuration (specific model, instructions, tools, and so on), developers can continue to use InvokeAgent on resources created with CreateAgent. For workloads that need dynamic runtime behavior changes for each agent invocation, you can use the new InvokeInlineAgent API. With either approach, your agents will be secure and scalable, with configurable guardrails, a flexible set of model inference options, native access to knowledge bases, code interpretation, session memory, and more.

Solution overview

Our HR assistant example shows how to build a single AI assistant that adapts to different user roles using the new inline agent capabilities in Amazon Bedrock Agents. When users interact with the assistant, the assistant dynamically configures agent capabilities (such as model, instructions, knowledge bases, action groups, and guardrails) based on the user’s role and their specific selections. This approach creates a flexible system that adjusts its functionality in real time, making it more efficient than creating separate agents for each user role or tool combination. The complete code for this HR assistant example is available on our GitHub repo.

This dynamic tool selection enables a personalized experience. When an employee logs in without direct reports, they see a set of tools that they have access to based on their role. They can select from options like requesting vacation time, checking company policies using the knowledge base, using a code interpreter for data analysis, or submitting expense reports. The inline agent assistant is then configured with only these selected tools, allowing it to assist the employee with their chosen tasks. In a real-world example, the user would not need to make the selection, because the application would make that decision and automatically configure the agent invocation at runtime. We make it explicit in this application so that you can demonstrate the impact.

Similarly, when a manager logs in to the same system, they see an extended set of tools reflecting their additional permissions. In addition to the employee-level tools, managers have access to capabilities like running performance reviews. They can select which tools they want to use for their current session, instantly configuring the inline agent with their choices.

The inclusion of knowledge bases is also adjusted based on the user’s role. Employees and managers see different levels of company policy information, with managers getting additional access to confidential data like performance review and compensation details. For this demo, we’ve implemented metadata filtering to retrieve only the appropriate level of documents based on the user’s access level, further enhancing efficiency and security.

Let’s look at how the interface adapts to different user roles.

The employee view provides access to essential HR functions like vacation requests, expense submissions, and company policy lookups. Users can select which of these tools they want to use for their current session.

The manager view extends these options to include supervisory functions like compensation management, demonstrating how the inline agent can be configured with a broader set of tools based on user permissions.

The manager view extends these capabilities to include supervisory functions like compensation management, demonstrating how the inline agent dynamically adjusts its available tools based on user permissions. Without inline agents, we would need to build and maintain two separate agents.

As shown in the preceding screenshots, the same HR assistant offers different tool selections based on the user’s role. An employee sees options like Knowledge Base, Apply Vacation Tool, and Submit Expense, whereas a manager has additional options like Performance Evaluation. Users can select which tools they want to add to the agent for their current interaction.

This flexibility allows for quick adaptation to user needs and preferences. For instance, if the company introduces a new policy for creating business travel requests, the tool catalog can be quickly updated to include a Create Business Travel Reservation tool. Employees can then choose to add this new tool to their agent configuration when they need to plan a business trip, or the application could automatically do so based on their role.

With Amazon Bedrock inline agents, you can create a catalog of actions that is dynamically selected by the application or by users of the application. This increases the level of flexibility and adaptability of your solutions, making them a perfect fit for navigating the complex, ever-changing landscape of modern business operations. Users have more control over their AI assistant’s capabilities, and the system remains efficient by only loading the necessary tools for each interaction.

Technical foundation: Dynamic configuration and action selection

Inline agents allow dynamic configuration at runtime, enabling a single agent to effectively perform the work of many. By specifying action groups and modifying instructions on the fly, even within the same session, you can create versatile AI applications that adapt to various scenarios without multiple agent deployments.

The following are key points about inline agents:

• Runtime configuration – Change the agent’s configuration, including its FM, at runtime. This enables rapid experimentation and adaptation without redeploying the application, reducing development cycles.
• Governance at tool level – Apply governance and access control at the tool level. With agents changing dynamically at runtime, tool-level governance helps maintain security and compliance regardless of the agent’s configuration.
• Agent efficiency – Provide only necessary tools and instructions at runtime to reduce token usage and improve the agent accuracy. With fewer tools to choose from, it’s less complicated for the agent to select the right one, reducing hallucinations in the tool selection process. This approach can also lead to lower costs and improved latency compared to static agents because removing unnecessary tools, knowledge bases, and instructions reduces the number of input and output tokens being processed by the agent’s large language model (LLM).
• Flexible action catalog – Create reusable actions for dynamic selection based on specific needs. This modular approach simplifies maintenance, updates, and scalability of your AI applications.

The following are examples of reusable actions:

• Enterprise system integration – Connect with systems like Salesforce, GitHub, or databases
• Utility tools – Perform common tasks such as sending emails or managing calendars
• Team-specific API access – Interact with specialized internal tools and services
• Data processing – Analyze text, structured data, or other information
• External services – Fetch weather updates, stock prices, or perform web searches
• Specialized ML models – Use specific machine learning (ML) models for targeted tasks

When using inline agents, you configure parameters for the following:

• Contextual tool selection based on user intent or conversation flow
• Adaptation to different user roles and permissions
• Switching between communication styles or personas
• Model selection based on task complexity

The inline agent uses the configuration you provide at runtime, allowing for highly flexible AI assistants that efficiently handle various tasks across different business contexts.

Building an HR assistant using inline agents

Let’s look at how we built our HR Assistant using Amazon Bedrock inline agents:

1. Create a tool catalog – We developed a demo catalog of HR-related tools, including:
   • Knowledge Base – Using Amazon Bedrock Knowledge Bases for accessing company policies and guidelines based on the role of the application user. In order to filter the knowledge base content based on the user’s role, you also need to provide a metadata file specifying the type of employee’s roles that can access each file
   • Apply Vacation – For requesting and tracking time off.
   • Expense Report – For submitting and managing expense reports.
   • Code Interpreter – For performing calculations and data analysis.
   • Compensation Management – for conducting and reviewing employee compensation assessments (manager only access).
2. Set conversation tone – We defined multiple conversation tones to suit different interaction styles:
   • Professional – For formal, business-like interactions.
   • Casual – For friendly, everyday support.
   • Enthusiastic – For upbeat, encouraging assistance.
3. Implement access control – We implemented role-based access control. The application backend checks the user’s role (employee or manager) and provides access to appropriate tools and information and passes this information to the inline agent. The role information is also used to configure metadata filtering in the knowledge bases to generate relevant responses. The system allows for dynamic tool use at runtime. Users can switch personas or add and remove tools during their session, allowing the agent to adapt to different conversation needs in real time.
4. Integrate the agent with other services and tools – We connected the inline agent to:
   • Amazon Bedrock Knowledge Bases for company policies, with metadata filtering for role-based access.
   • AWS Lambda functions for executing specific actions (such as submitting vacation requests or expense reports).
   • A code interpreter tool for performing calculations and data analysis.
5. Create the UI – We created a Flask-based UI that performs the following actions:
   • Displays available tools based on the user’s role.
   • Allows users to select different personas.
   • Provides a chat window for interacting with the HR assistant.

To understand how this dynamic role-based functionality works under the hood, let’s examine the following system architecture diagram.

As shown in preceding architecture diagram, the system works as follows:

1. The end-user logs in and is identified as either a manager or an employee.
2. The user selects the tools that they have access to and makes a request to the HR assistant.
3. The agent breaks down the problems and uses the available tools to solve for the query in steps, which may include:
   1. Amazon Bedrock Knowledge Bases (with metadata filtering for role-based access).
   2. Lambda functions for specific actions.
   3. Code interpreter tool for calculations.
   4. Compensation tool (accessible only to managers to submit base pay raise requests).
4. The application uses the Amazon Bedrock inline agent to dynamically pass in the appropriate tools based on the user’s role and request.
5. The agent uses the selected tools to process the request and provide a response to the user.

This approach provides a flexible, scalable solution that can quickly adapt to different user roles and changing business needs.

Conclusion

In this post, we introduced the Amazon Bedrock inline agent functionality and highlighted its application to an HR use case. We dynamically selected tools based on the user’s roles and permissions, adapted instructions to set a conversation tone, and selected different models at runtime. With inline agents, you can transform how you build and deploy AI assistants. By dynamically adapting tools, instructions, and models at runtime, you can:

• Create personalized experiences for different user roles
• Optimize costs by matching model capabilities to task complexity
• Streamline development and maintenance
• Scale efficiently without managing multiple agent configurations

For organizations demanding highly dynamic behavior—whether you’re an AI startup, SaaS provider, or enterprise solution team—inline agents offer a scalable approach to building intelligent assistants that grow with your needs. To get started, explore our GitHub repo and HR assistant demo application, which demonstrate key implementation patterns and best practices.

To learn more about how to be most successful in your agent journey, read our two-part blog series:

• Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1
• Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 2

To get started with Amazon Bedrock Agents, check out the following GitHub repository with example code.

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About the authors

Ishan Singh is a Generative AI Data Scientist at Amazon Web Services, where he helps customers build innovative and responsible generative AI solutions and products. With a strong background in AI/ML, Ishan specializes in building Generative AI solutions that drive business value. Outside of work, he enjoys playing volleyball, exploring local bike trails, and spending time with his wife and dog, Beau.

Maira Ladeira Tanke is a Senior Generative AI Data Scientist at AWS. With a background in machine learning, she has over 10 years of experience architecting and building AI applications with customers across industries. As a technical lead, she helps customers accelerate their achievement of business value through generative AI solutions on Amazon Bedrock. In her free time, Maira enjoys traveling, playing with her cat, and spending time with her family someplace warm.

Mark Roy is a Principal Machine Learning Architect for AWS, helping customers design and build generative AI solutions. His focus since early 2023 has been leading solution architecture efforts for the launch of Amazon Bedrock, the flagship generative AI offering from AWS for builders. Mark’s work covers a wide range of use cases, with a primary interest in generative AI, agents, and scaling ML across the enterprise. He has helped companies in insurance, financial services, media and entertainment, healthcare, utilities, and manufacturing. Prior to joining AWS, Mark was an architect, developer, and technology leader for over 25 years, including 19 years in financial services. Mark holds six AWS certifications, including the ML Specialty Certification.

Nitin Eusebius is a Sr. Enterprise Solutions Architect at AWS, experienced in Software Engineering, Enterprise Architecture, and AI/ML. He is deeply passionate about exploring the possibilities of generative AI. He collaborates with customers to help them build well-architected applications on the AWS platform, and is dedicated to solving technology challenges and assisting with their cloud journey.

Ashrith Chirutani is a Software Development Engineer at Amazon Web Services (AWS). He specializes in backend system design, distributed architectures, and scalable solutions, contributing to the development and launch of high-impact systems at Amazon. Outside of work, he spends his time playing ping pong and hiking through Cascade trails, enjoying the outdoors as much as he enjoys building systems.

Shubham Divekar is a Software Development Engineer at Amazon Web Services (AWS), working in Agents for Amazon Bedrock. He focuses on developing scalable systems on the cloud that enable AI applications frameworks and orchestrations. Shubham also has a background in building distributed, scalable, high-volume-high-throughput systems in IoT architectures.

Vivek Bhadauria is a Principal Engineer for Amazon Bedrock. He focuses on building deep learning-based AI and computer vision solutions for AWS customers. Oustide of work, Vivek enjoys trekking and following cricket.