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Amazon Bedrock Agent Tutorial: Build a Recon Bot in 60 Minutes

  • newhmteam
  • Nov 7
  • 11 min read


Table Of Contents


  • Understanding Amazon Bedrock Agents

  • Prerequisites for Building a Recon Bot

  • Step 1: Setting Up Your Amazon Bedrock Environment

  • Step 2: Defining Your Recon Bot's Purpose and Capabilities

  • Step 3: Creating and Configuring Your Bedrock Agent

  • Step 4: Building Action Groups for Your Recon Bot

  • Step 5: Testing and Iterating Your Recon Bot

  • Step 6: Deploying Your Recon Bot to Production

  • Common Challenges and Solutions

  • Business Applications of Your Recon Bot

  • Conclusion: Beyond the Recon Bot


Amazon Bedrock Agent Tutorial: Build a Recon Bot in 60 Minutes


Enterprises across industries are racing to harness the power of generative AI to transform their operations and create new capabilities. Amazon Bedrock—AWS's fully managed service for foundation models—has emerged as a leading platform for building and scaling generative AI applications. Among its most powerful features are Bedrock Agents, which allow organizations to create purpose-built AI assistants that can perform specific tasks and access company data and systems.


In this tutorial, we'll walk through the process of building a functional 'Recon Bot' using Amazon Bedrock Agents in just 60 minutes. This Recon Bot will be capable of gathering and analyzing information from multiple sources, providing summaries, and delivering actionable insights—a practical application that showcases the immediate business value of generative AI.


As an AWS Premier-tier Partner with recognized Generative AI proficiency, Axrail.ai has implemented numerous Amazon Bedrock solutions for enterprises seeking to make their IT systems more intelligent. This tutorial draws from our real-world implementation experience to help you quickly build and deploy a functional AI agent that delivers immediate value.


Let's dive in and transform how your organization accesses and acts on information with Amazon Bedrock Agents.


Understanding Amazon Bedrock Agents


Amazon Bedrock Agents are AI assistants that you can configure to work with your enterprise systems, data sources, and business processes. Unlike general-purpose chatbots, Bedrock Agents can be programmed to perform specific tasks through API calls, access your organization's knowledge bases, and interact with users in a natural, conversational way.


A Recon Bot is a specific type of agent designed to gather, consolidate, and analyze information from multiple sources. It serves as an intelligence-gathering assistant that can save employees hours of manual research time while providing consistently formatted insights and recommendations.


The key components of a Bedrock Agent include:


  1. Foundation Model: The underlying large language model (LLM) that powers your agent's understanding and generation capabilities

  2. Action Groups: Defined sets of API operations your agent can perform

  3. Knowledge Bases: Connected repositories of information your agent can reference

  4. Prompts: Instructions that guide how your agent responds and behaves


By combining these elements, we'll create a Recon Bot that can perform complex information gathering and analysis tasks on demand.


Prerequisites for Building a Recon Bot


Before we begin building our Recon Bot, ensure you have the following in place:


  1. AWS Account with Bedrock Access: You'll need an AWS account with Amazon Bedrock enabled. If you don't have access yet, request it through the AWS console.

  2. IAM Permissions: Ensure you have the necessary permissions to create and manage Bedrock resources. You'll need access to Amazon Bedrock, Lambda, IAM, and potentially S3 services.

  3. API Endpoints for Information Sources: Identify and prepare the API endpoints your Recon Bot will query. These could include internal company databases, third-party data services, or public APIs.

  4. Clear Use Case Definition: Define exactly what information your Recon Bot will gather and what format the output should take. This clarity will streamline the development process.

  5. Foundation Model Selection: Decide which Amazon Bedrock foundation model you'll use. For Recon Bots, we typically recommend Claude (Anthropic) or Titan (Amazon) models based on their strong reasoning capabilities.


With these prerequisites in place, you're ready to build your Recon Bot.


Step 1: Setting Up Your Amazon Bedrock Environment


Let's begin by configuring your Amazon Bedrock environment:


  1. Access the Amazon Bedrock Console: Log into your AWS console and navigate to Amazon Bedrock.

  2. Model Access: In the Bedrock console, go to "Model access" in the left navigation panel. Request access to the foundation models you plan to use. For a Recon Bot, we recommend enabling Claude 2 (Anthropic) and Amazon Titan Text.

  3. Create IAM Role: Create an IAM role with appropriate permissions for your Bedrock Agent. This role should include:

  4. bedrock:InvokeModel permissions for your selected foundation models

  5. Permissions to invoke any AWS services your agent will interact with

  6. Amazon S3 access if your agent will read or store files

  7. Set Up Agent Execution Environment: Depending on your requirements, you may need to configure a VPC for your agent if it will access private resources.


This setup process typically takes 5-10 minutes. While waiting for model access approval, you can proceed with designing your Recon Bot's capabilities.


Step 2: Defining Your Recon Bot's Purpose and Capabilities


Before diving into configuration, clearly define what your Recon Bot will do. For this tutorial, we'll create a Recon Bot with the following capabilities:


  1. Competitor Intelligence Gathering: Ability to search for and summarize information about specified competitors

  2. Market Trend Analysis: Capability to identify and report on trends in a given industry or product category

  3. News Monitoring: Functionality to gather and summarize recent news about specific topics, companies, or products

  4. Insight Generation: Ability to analyze gathered information and produce actionable insights


To implement these capabilities, our Recon Bot will need to:


  • Connect to search APIs (like Bing Search or Google Search API)

  • Access news aggregation services

  • Connect to internal databases or knowledge bases

  • Have a structured output format for consistency


Let's document these requirements in a simple architecture diagram or flow chart to guide our implementation. The Recon Bot will follow this basic flow:


  1. Receive user query (e.g., "Research competitor X's recent product launches")

  2. Determine required information sources based on query type

  3. Make API calls to gather raw information

  4. Process and synthesize gathered information

  5. Generate structured report with insights

  6. Deliver response to user


With this blueprint in place, we can now implement our Recon Bot in Amazon Bedrock.


Step 3: Creating and Configuring Your Bedrock Agent


Now, let's create our Bedrock Agent:


  1. Navigate to Agents: In the Bedrock console, select "Agents" from the left navigation panel and click "Create Agent".

  2. Basic Information:

  3. Name: "ReconBot"

  4. Description: "An intelligence-gathering agent that researches competitors, markets, and news to provide actionable insights."

  5. IAM Role: Select the role you created earlier

  6. Foundation Model Selection: Choose the model you want to power your agent. For a Recon Bot, we recommend Claude 2 for its strong reasoning and summarization capabilities.

  7. Agent Instructions: This is one of the most critical steps. Your instructions guide how the agent behaves. Use the following instructions as a starting point:


You are ReconBot, an intelligence-gathering agent specializing in research and analysis. Your purpose is to help users gather, analyze, and synthesize information about competitors, market trends, and relevant news.


When responding to requests: 1. Determine what information sources are needed to answer the query comprehensively 2. Gather information from all relevant sources 3. Analyze the gathered information to identify patterns, insights, and actionable recommendations 4. Present your findings in a clear, structured format with sections for Summary, Key Details, Analysis, and Recommendations 5. Always cite your sources at the end of your response


Maintain a professional, analytical tone. Focus on providing objective information and insights rather than opinions. When information is limited or uncertain, clearly state the limitations of your analysis.


  1. Advanced Options: Configure memory settings based on your needs. For a Recon Bot, enabling memory helps maintain context across multiple interactions in a research session.

  2. Review and Create: Review your configuration and create the agent.


Your basic agent is now created, but it needs action groups to perform useful tasks.


Step 4: Building Action Groups for Your Recon Bot


Action groups define the specific capabilities of your Recon Bot. Let's create three essential action groups:


Search Action Group


  1. Create Action Group: From your agent's page, select "Add" in the Action groups section.

  2. Define Action Group:

  3. Name: "SearchActions"

  4. Description: "Actions for searching the web and databases"

  5. Create Function:

  6. Function name: "searchWeb"

  7. Description: "Search the web for specified information"

  8. Sample utterances: "Find information about [topic]", "Research [company]", "Search for [keyword]"

  9. Define Parameters:

  10. Parameter name: "query"

  11. Description: "The search query"

  12. Required: Yes

  13. Type: string

  14. Parameter name: "source" (optional)

  15. Description: "Preferred source of information"

  16. Required: No

  17. Type: string

  18. Allowed values: "news", "academic", "social", "all"

  19. Create API Schema: Define the OpenAPI schema for your search function. This will connect to a Lambda function that performs the actual search using your preferred search API.

  20. Implement Lambda Function: Create a Lambda function that takes the search parameters and calls your preferred search API (Google, Bing, etc.). Ensure the function returns results in a consistent format the agent can process.


Analysis Action Group


  1. Create Action Group: Add another action group named "AnalysisActions".

  2. Define Function:

  3. Function name: "analyzeCompetitor"

  4. Description: "Analyze a competitor based on gathered information"

  5. Sample utterances: "Analyze [company]", "Provide insights on [company]"

  6. Define Parameters:

  7. Parameter name: "companyName"

  8. Description: "The competitor company to analyze"

  9. Required: Yes

  10. Type: string

  11. Parameter name: "aspectsToAnalyze"

  12. Description: "Specific aspects to analyze"

  13. Required: No

  14. Type: array

  15. Items type: string

  16. Allowed values: "products", "pricing", "marketing", "technology", "strategy"

  17. Implement Lambda Function: Create a Lambda function that takes the competitor information and aspects to analyze, then structures an analysis based on information retrieved from your search action and any internal knowledge bases.


Reporting Action Group


  1. Create Action Group: Add a final action group named "ReportingActions".

  2. Define Function:

  3. Function name: "generateReport"

  4. Description: "Generate a comprehensive report on the requested topic"

  5. Sample utterances: "Create report on [topic]", "Generate analysis of [market]"

  6. Define Parameters:

  7. Parameter name: "topic"

  8. Description: "The topic to report on"

  9. Required: Yes

  10. Type: string

  11. Parameter name: "format"

  12. Description: "The format of the report"

  13. Required: No

  14. Type: string

  15. Allowed values: "executive", "detailed", "bullet"

  16. Parameter name: "timeframe"

  17. Description: "The relevant timeframe for the report"

  18. Required: No

  19. Type: string

  20. Default: "recent"

  21. Implement Lambda Function: Create a Lambda function that generates structured reports based on the specified parameters.


After creating these action groups, your Recon Bot now has the fundamental capabilities it needs to gather and analyze information.


Step 5: Testing and Iterating Your Recon Bot


Testing is critical to ensuring your Recon Bot functions as expected:


  1. Open the Test Window: From your agent's page, click "Test" to open the testing interface.

  2. Start with Basic Queries: Begin with simple queries to verify fundamental functionality:

  3. "Find information about Amazon's recent cloud services"

  4. "Analyze Microsoft as a competitor in the cloud space"

  5. "Generate a report on AI trends in the enterprise market"

  6. Review Responses: Evaluate your bot's responses for:

  7. Accuracy of information

  8. Appropriateness of action selection

  9. Quality of analysis and insights

  10. Formatting and readability

  11. Debug and Refine:

  12. Check the execution logs to identify any issues with your Lambda functions

  13. Refine your agent instructions if the responses aren't meeting expectations

  14. Adjust action group definitions if the agent is selecting the wrong actions

  15. Enhance Lambda implementations to improve data quality

  16. Test Edge Cases: Challenge your bot with complex or ambiguous queries to identify limitations:

  17. Queries that span multiple domains

  18. Requests for very recent information

  19. Questions requiring nuanced analysis

  20. Iterate: Based on your testing, refine your agent until it consistently provides high-quality responses. Common improvements include:

  21. Expanding the range of sample utterances

  22. Adding more structured guidance in the agent instructions

  23. Enhancing the data processing in Lambda functions

  24. Adding fallback mechanisms for when information is unavailable


Through this testing and iteration process, which should take 15-20 minutes, you'll develop a Recon Bot that reliably delivers valuable insights.


Step 6: Deploying Your Recon Bot to Production


Once you're satisfied with your Recon Bot's performance, prepare it for production use:


  1. Create an Alias: In the Bedrock console, navigate to your agent and create an alias (e.g., "production").

  2. Configure Throughput: Set appropriate throughput settings based on your expected usage patterns. Start conservative and scale as needed.

  3. Integration Options: Decide how users will interact with your Recon Bot:

  4. Direct through the Bedrock API

  5. Via a chatbot interface in your application

  6. Through integration with collaboration tools like Slack or Teams

  7. Implement Security Measures:

  8. Ensure proper authentication and authorization

  9. Implement user-based access controls if needed

  10. Consider data encryption for sensitive information

  11. Documentation: Create clear documentation for end-users explaining:

  12. What the Recon Bot can do

  13. How to phrase queries for best results

  14. How to interpret the bot's responses

  15. Limitations they should be aware of

  16. Monitoring and Maintenance Plan: Set up CloudWatch alarms to monitor:

  17. Invocation errors

  18. Latency issues

  19. Usage patterns

  20. Cost metrics


Your Recon Bot is now ready for production use, delivering valuable intelligence to your organization with minimal human effort.


Common Challenges and Solutions


As you implement your Recon Bot, you may encounter these common challenges:


Challenge 1: Handling Rate Limits from External APIs


Solution: Implement caching mechanisms in your Lambda functions to reduce the number of API calls. Consider storing frequently requested information in a database and refreshing it on a schedule rather than for every request.


Challenge 2: Ensuring Information Accuracy


Solution: Include verification steps in your Lambda functions that cross-reference information from multiple sources. You can also add confidence scores to the information returned to the agent, allowing it to communicate certainty levels to users.


Challenge 3: Managing Costs


Solution: Implement tiered usage based on user roles, with more detailed research capabilities for users who truly need them. Monitor usage patterns and optimize prompt length and complexity to reduce token usage.


Challenge 4: Handling Ambiguous Queries


Solution: Enhance your agent instructions to include clarification strategies. Train your users to provide structured queries, and consider implementing a query refinement dialogue for ambiguous requests.


Business Applications of Your Recon Bot


Your new Recon Bot can deliver significant value across various business functions:


Competitive Intelligence


Your sales and strategy teams can use the Recon Bot to quickly gather intelligence on competitors, including: - New product launches and features - Pricing changes - Marketing campaigns - Executive team changes


This real-time intelligence helps your team respond quickly to market changes and identify opportunities.


Market Research


Product and marketing teams can leverage the Recon Bot to: - Identify emerging market trends - Track consumer sentiment - Monitor regulatory changes - Analyze market size and growth projections


These insights support data-driven decision making for product development and go-to-market strategies.


Investment Research


Finance teams and investors can use the Recon Bot to: - Research potential investment targets - Track performance metrics of portfolio companies - Monitor market conditions affecting investments - Generate preliminary due diligence reports


By automating the initial research phase, your team can focus on analysis and decision-making rather than information gathering.


Knowledge Management


Integrating your Recon Bot with your organization's Digital Workforce strategy creates a powerful system for institutional knowledge management, ensuring insights are captured, analyzed, and made accessible across your organization.


Conclusion: Beyond the Recon Bot


Congratulations! In just 60 minutes, you've built a powerful Recon Bot using Amazon Bedrock Agents that can transform how your organization gathers and utilizes information. This Recon Bot represents just the beginning of what's possible with Amazon Bedrock and generative AI.


As you become more comfortable with Bedrock Agents, consider these advanced capabilities for your next projects:


  1. Multi-agent systems where specialized agents collaborate to solve complex problems

  2. Hybrid human-AI workflows that combine the strengths of AI agents with human expertise

  3. Integration with enterprise systems like CRM, ERP, and business intelligence platforms

  4. Industry-specific agents tailored to the unique needs of your sector


At Axrail.ai, we've helped organizations across industries implement sophisticated AI agent systems that deliver measurable business outcomes through our Data Analytics and Digital Platform services. Our experience as an AWS Premier-tier Partner with Generative AI proficiency enables us to guide you through the entire implementation journey, from concept to production.


The 60-minute Recon Bot you've built today is just one example of how quickly generative AI can be implemented to deliver real business value. By applying our proven Cloud Migration methodologies and axcelerate framework, your organization can rapidly develop and deploy AI agents that transform operations and create competitive advantage.


Moving Forward with Bedrock Agents


The Amazon Bedrock Agent Recon Bot we've built represents just the beginning of your generative AI journey. As you've seen, with the right approach and expertise, powerful AI solutions can be implemented quickly to deliver immediate business value.


Key takeaways from this tutorial include:


  • Amazon Bedrock Agents provide a powerful framework for building purpose-specific AI assistants

  • A well-designed agent can automate complex information gathering and analysis tasks

  • With proper planning, a functional AI solution can be implemented in as little as 60 minutes

  • The true value comes from integrating these agents into your broader business processes


As you continue exploring Amazon Bedrock, remember that the most successful implementations align technology with specific business objectives. Your Recon Bot should evolve based on user feedback and changing business needs.


By combining the power of foundation models with your organization's unique data and processes, you're positioned to create AI solutions that deliver sustained competitive advantage.


Ready to take your generative AI implementation to the next level? Axrail.ai's team of AWS-certified experts can help you develop and deploy sophisticated AI agents tailored to your specific business needs. With our proven axcelerate framework and comprehensive generative AI expertise, we can accelerate your journey from concept to production.


Contact us today to discuss how we can help transform your IT systems with intelligent, AI-enabled solutions.


 
 
 

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