AWS Public Sector Blog

Enhance productivity with Amazon Bedrock Agents and Powertools for AWS Lambda

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The public sector faces unique challenges that demand innovative solutions to improve productivity and service delivery. Large language models (LLMs) have shown great promise in various applications, but their true potential is unlocked when they can access real-time information—such as current data, time, weather, or breaking events. This capability is crucial for effective public sector planning and decision-making. However, LLMs alone may fall short in providing timely and relevant insights, especially for agencies managing dynamic and time-sensitive operations.

What you’ll learn

In this post, you will discover:

  • The key challenges faced by public sector agencies and why real-time data access is crucial
  • How Amazon Bedrock Agents and Powertools for AWS Lambda work together to address these challenges
  • Real-world use cases demonstrating practical applications in the public sector
  • Technical implementation details and best practices

Key challenges and the need for real-time data

When implementing LLMs in public sector operations, agencies encounter several critical challenges that limit their ability to leverage artificial intelligence (AI) effectively. Limited access to real-time data prevents LLMs from providing current and actionable insights, while integration difficulties with existing systems and strict compliance requirements further complicate implementation.

Real-time data access is particularly vital for priority responses, where timely information can make the difference in critical situations. It’s equally important for policy compliance, ensuring decisions are based on the latest information, and for resource allocation, helping agencies efficiently direct resources where they’re needed most.

Introducing Amazon Bedrock Agents and Powertools for AWS Lambda

To address these challenges, we can leverage two powerful tools that work seamlessly together:

Amazon Bedrock Agents utilize functional calling to invoke AWS Lambda functions with embedded business logic. This enables access to external APIs and integration with various data sources, facilitating cross-agency communication and data sharing via APIs.

When combined with Powertools for AWS Lambda (Python), a developer toolkit that implements serverless best practices, agencies can significantly increase their development velocity while maintaining high security and compliance standards.

Real-world applications in public sector

Let’s explore how Amazon Bedrock Agents and Powertools for AWS Lambda help public sector agencies improve their operations through practical examples.

Weather forecast prompt

Use case narrative: The state department of agriculture needs to plan pesticide spraying schedules based on weather forecasts to ensure effective pest control while minimizing environmental impact.

  • User: What is the expected rainfall amount for the next 72 hours in Seattle, WA?
  • Agent: The expected rainfall amount for the next 72 hours in Seattle, WA is 0.5 inches.

Compliance document retrieval prompt

Use case narrative: An agencies’ IT department requires the latest FedRAMP compliance documents to ensure that cloud services used by the agency meet federal security standards.

  • User: Does AWS have any recent FedRAMP compliance documents?
  • Agent: Here are some recent FedRAMP compliance documents from AWS …

Workforce planning prompt

Use case narrative: The state employment agency needs to provide up-to-date job listings for IT specialists to help job seekers find employment and ensure a skilled workforce for the region.

  • User: List the ‘IT Specialist’ jobs within 100 miles of Washington, DC.
  • Agent: Here are some ‘IT Specialist’ jobs within 100 miles of Washington, DC …

Key application areas

Data and analytics operations

Agencies can leverage real-time data analytics to drive evidence-based policy decisions and optimize resource allocation. This foundation enables more responsive and efficient government services while reducing operational costs.

Augment policy decisions and resource allocation through:

  • Real-time census and demographic analysis for future planning
  • Economic indicator monitoring for policy assessment
  • Automated fiscal reporting and data retrieval
  • Integration with existing data management systems

Operations and compliance management

Streamlining complex operational workflows with integrated monitoring and reporting capabilities enhances efficiency while maintaining regulatory compliance and reducing operational risks.

Enhance operational efficiency through automated:

  • Real-time aviation status updates and monitoring
  • AWS compliance document management via AWS Artifact
  • Date/time calculations and verification processes
  • Regulatory compliance tracking and reporting

Environmental and infrastructure management

Improve environmental and infrastructure management through comprehensive monitoring and data-driven insights. This approach enables agencies to anticipate changes, respond rapidly, and maintain essential infrastructure more effectively.

Monitor and analyze critical environmental data including:

  • Seismic activity and earthquake tracking
  • Precise geolocation services and mapping
  • Weather forecasting and severe weather alerts
  • Infrastructure status and maintenance scheduling

Workforce and public health

Elevate public health and workforce decisions through real-time data analysis and trend monitoring. This data-driven approach enables precise healthcare planning and strategic resource deployment.

Optimize resource management with:

  • Real-time workforce availability tracking
  • Job market trend analysis
  • Public health data monitoring
  • Population health metrics

Solution architecture

Components

  • Amazon Bedrock LLM: The large language model used for generating responses.
  • Amazon Bedrock Agent: The interface for orchestration and task analysis through which users engage with the solution.
  • AWS Lambda using Powertools for AWS Lambda (Python): Lambda functions that contain the logic for data retrieval and processing.
  • Amazon Bedrock Agent Action Group: Manages the actions performed by the agent.
  • Agent Business Logic: The specific logic and rules that the agent follows to process user queries.
  • Amazon Bedrock Knowledge Base: A repository containing policy documents, compliance documents, and business documents.
  • OpenAPI Schema: Defines the API operations that the agent can invoke.
  • Agency API Endpoint: Provides access to agency-specific data and services.
  • Amazon Bedrock Guardrails: Mechanisms to enforce security and compliance policies.

Basic interaction

  1.  User prompt: The AWS Lambda Client sends a prompt to the Amazon Bedrock Agent.
  2.  Agent processing: The agent breaks down the task into a logical sequence using advanced reasoning. It interacts with necessary APIs to fulfill the request, deciding whether to proceed or gather more information.
  3.  Guardrails evaluation: The input prompt gets checked against the configured guardrails for compliance. If the input fails the evaluation, a blocked message gets returned, and the process terminates.
  4.  Data retrieval and execution: If the input passes the guardrails evaluation, the agent invokes relevant Lambda functions and queries the Knowledge Base (if necessary) to complete the tasks.
  5.  Response generation: After completing the tasks, the agent crafts a response. This response then gets checked against the guardrails for compliance. If the response fails the evaluation, it gets overridden with a blocked message or sensitive information gets masked.
  6.  User response: The compliant response gets delivered back to the AWS Lambda Client.

Figure 1. Architectural diagram of the solution described in this post. The major components are Amazon Bedrock, AWS Lambda, and Powertools for AWS Lambda.

To dive a bit deeper into this architecture, let’s examine an example Lambda function that demonstrates these components working together in a practical implementation.

Technical implementation

Here’s an example of implementing a Lambda function using Powertools for AWS Lambda to retrieve weather data:

# Initialize Powertools for AWS logging and tracing
logger = Logger()
tracer = Tracer(service="WeatherForecastAgent")

# BedrockAgentResolver handles Lambda and Bedrock Agent integration
app = BedrockAgentResolver()

# Define endpoint for Bedrock Agent weather forecast calls
@app.get("/forecast", description="Retrieve current weather forecast at a station.")
@tracer.capture_method
def get_weather(station_id: str = Query(..., description="The id of the weather observation station.")) -> dict:

        # Log API call
        logger.info(
            f"Retrieving weather data for station: {station_id}"
        )

        # Get latest observation for the weather station
        base_url = "https://<your-api-endpoint>"
        url = f"{base_url}/{station_id}/observations/latest"
        response = requests.get(url, timeout=30)
        response.raise_for_status()
        
        # Parse API response
        data = response.json()
        
        # Extract weather info
        temperature = data['properties']['temperature']['value']
        description = data['properties']['textDescription']

        # Log retrieved data
        logger.info(
            f"Weather for station {station_id}: Temp: {temperature}, Desc: {description}"
        )

        # Add X-Ray annotation for trace filtering
        tracer.put_annotation(key="station_id", value=f"{station_id}")

        return {"statusCode": 200, "body": data}

# Main Lambda handler with logging and tracing
@logger.inject_lambda_context(log_event=True)
@tracer.capture_lambda_handler
def lambda_handler(event: dict, context: LambdaContext) -> dict:
    return app.resolve(event, context)

if __name__ == "__main__":
    # Print OpenAPI schema for Bedrock Agent configuration
    print(app.get_openapi_json_schema())
Python

In this example:

  • The Logger and Tracer from Powertools for AWS Lambda enable comprehensive observability through logging and tracing of the function’s execution.
  • The BedrockAgentResolver handles the routing between the Amazon Bedrock Agent and Lambda function, providing streamlined API integration.
  • Structured logging and AWS X-Ray annotations are implemented to track API calls and enable detailed trace analysis.
  • Powertools provides the capability to generate an OpenAPI schema, which developers can use during Bedrock Agent setup to define their API interface.

The following figure illustrates a sample interaction with the Amazon Bedrock Agent, demonstrating how it processes a user prompt and retrieves real-time data. The agent’s ability to integrate with external APIs and generate timely responses is important for enhancing public sector productivity and service delivery.

Figure 2. Example invocation of the Amazon Bedrock Agent.

As part of ensuring comprehensive observability, the example Lambda function utilizes Amazon CloudWatch for logging. CloudWatch captures detailed log events that provide insights into the function’s execution, helping developers diagnose issues and monitor performance.

Figure 3. Example Amazon CloudWatch log events.

To further enhance observability, the Lambda function integrates with AWS X-Ray for distributed tracing. X-Ray provides a visual map of the function’s execution, allowing developers to trace requests as they flow through the system, identify bottlenecks, and optimize performance.

Figure 4. Example AWS X-Ray Trace Map.

This example highlights several value-added capabilities of Powertools for AWS Lambda when integrated with Amazon Bedrock Agents. The combination of built-in observability, automated schema generation, and standardized error handling accelerates development cycles while maintaining production-grade reliability. These features allow development teams to rapidly prototype and deploy AI-enabled applications with confidence.

Conclusion

By leveraging Amazon Bedrock Agents and Powertools for AWS Lambda, public sector agencies can unlock the full potential of generative AI and LLMs, driving innovation and improving service delivery for citizens. These tools enhance LLM capabilities by enabling access to real-time data and significantly increase developer velocity. This allows agencies to focus on delivering value to citizens rather than dealing with infrastructure complexities. The result is a more agile, responsive, and effective public sector that can better meet the needs of its constituents.

Next steps

To review the technical solution for this blog, see the GitHub repo for this post. For further reading and resources, explore the following links.

Resources

Bill Screen

Bill Screen

Bill is a senior solutions architect for Worldwide Public Sector (WWPS) at Amazon Web Services (AWS). He’s passionate about helping customers achieve their business objectives with artificial intelligence and machine learning (AI/ML) solutions.

Chris Rach

Chris Rach

Chris is a senior solutions architect for Amazon Web Services (AWS) Worldwide Public Sector. He empowers public sector builders to transform their missions through innovative cloud solutions. His passion lies in accelerating customers' artificial intelligence and machine learning (AI/ML) adoption journeys, turning complex challenges into impactful outcomes that better serve citizens and communities.