Sarvar Nadaf

Posted on Nov 20

Amazon Q Custom Agents: The Complete Guide

#aws #ai #genai #beginners

👋 Hey there, tech enthusiasts!

I'm Sarvar, a Cloud Architect with a passion for transforming complex technological challenges into elegant solutions. With extensive experience spanning Cloud Operations (AWS & Azure), Data Operations, Analytics, DevOps, and Generative AI, I've had the privilege of architecting solutions for global enterprises that drive real business impact. Through this article series, I'm excited to share practical insights, best practices, and hands-on experiences from my journey in the tech world. Whether you're a seasoned professional or just starting out, I aim to break down complex concepts into digestible pieces that you can apply in your projects.

Let's dive in and explore the fascinating world of cloud technology together! 🚀

Imagine Sarah, a DevOps engineer at a growing startup. Every day, she receives dozens of questions from developers:

“What's the status of our production deployment?”
“Can you check the database performance metrics?”
“How much are we spending on EC2 this month?”

Sarah spends hours manually checking AWS consoles, running CLI commands, and compiling reports.

What if Sarah could create an intelligent assistant that automatically handles these requests?

Enter Amazon Q Custom Agents: AI-powered assistants that can understand natural language, access your AWS resources, and provide intelligent responses.

Today, we'll explore how to build these powerful agents, covering everything from basic concepts to advanced integrations.

What We're Covering Today

Understanding Amazon Q Custom Agents
Types of Custom Agents
What is a Knowledge Base?
What is an Index?
Advanced Indexing Strategies
Storage & Integration Options for Knowledge Bases
Custom Agent Deployment Types
How Amazon Q Custom Agent Architecture Works
Pricing Breakdown
Conclusion

Understanding Amazon Q Custom Agents

Amazon Q Custom Agents are intelligent, model-driven assistants designed to adapt to your organization’s unique workflows and data. They combine large language models with your internal knowledge sources and AWS services to deliver accurate, contextual, and actionable responses.

The core structure of an Amazon Q Business Application is organized as follows:

Amazon Q Business Application
└── Custom Agent
    ├── Instructions (System Prompts)
    ├── Guardrails (Content Filtering)
    ├── Knowledge Bases
    │   └── Amazon Bedrock Knowledge Base
    │       ├── Data Source (S3)
    │       ├── Vector Store (OpenSearch Serverless)
    │       └── Embedding Model (Bedrock)
    └── Action Groups
        ├── Lambda Functions
        └── OpenAPI Schema Definitions

Types of Custom Agents

1. Knowledge Base Agents

What it does: Answers questions using documents, wikis, and internal knowledge sources.
Best for: FAQ systems, documentation queries, policy lookups.
Real-time example: “What’s our company’s vacation policy?” → Searches HR documents and returns accurate details.

2. Action Agents

What it does: Executes tasks through API calls and AWS service integrations.
Best for: Operational tasks, resource provisioning, automated workflows.
Real-time example: “Scale up our production environment” → Increases EC2 capacity and updates load balancers.

3. Hybrid Agents

What it does: Combines information retrieval with task execution.
Best for: Complex workflows requiring both insights and actions.
Real-time example: “Check database performance and optimize if needed” → Retrieves metrics, analyzes them, and applies tuning.

What is a Knowledge Base?

A Knowledge Base is the brain of a custom agent a centralized repository that serves as its memory, containing all the documents, procedures, and structured information needed to answer queries accurately.

Key Components

Documents: PDFs, Word files, text content, wikis, web pages
Structured Data: Databases, APIs, config files
Real-time Data: Monitoring systems, AWS service metrics, logs

What is an Index?

An Index is a structured system that converts your Knowledge Base content into searchable vector representations, allowing the agent to retrieve relevant information quickly and accurately.

How Indexing Works

Document Processing – Splits documents into meaningful chunks
Vectorization – Converts text into numerical embeddings
Semantic Search – Understands context instead of relying on keywords
Retrieval – Returns the most relevant content based on user queries

Example Index Structure

AWS Documentation Index
├── EC2 Best Practices (Vector: [0.2, 0.8, 0.1...])
├── S3 Security Guidelines (Vector: [0.5, 0.3, 0.9...])
├── Cost Optimization Tips (Vector: [0.1, 0.7, 0.4...])
└── Troubleshooting Guides (Vector: [0.8, 0.2, 0.6...])

Advanced Indexing Strategies

1. Vector Embedding Models

Amazon Titan Text Embeddings: Optimized for English, up to 8,192 tokens
Cohere Embed: Multilingual support for diverse content
Custom Models: Fine-tuned vectors for domain-specific knowledge

2. Chunking Strategies

Fixed-size chunking: 500–1000 tokens
Semantic chunking: Breaks at logical document boundaries
Overlapping chunks: 10–20% overlap for better continuity
Hierarchical chunking: Varies chunk size based on content type

3. Retrieval Methods

Semantic Search: Context-aware retrieval using vector similarity
Hybrid Search: Combines keyword + semantic search
Metadata Filtering: Filters results based on attributes (tags, authors, etc.)
Re-ranking: Reorders retrieved documents for maximum relevance

Storage & Integration Options for Knowledge Bases

Storage & Integration Options define where your knowledge is stored and how it connects to your custom agent, enabling seamless access to documents, databases, and third-party applications for accurate responses. Below are commonly used storage and integration choices for building robust knowledge bases.

Primary Storage Solutions

1. Amazon S3

Use Case: Document storage, archives, data lakes
Benefits: Scalable, cost-effective, easy AWS integration
Best For: Large document libraries and media files
Pricing: ~$0.023/GB per month

2. Amazon OpenSearch

Use Case: Real-time search, indexing, analytics
Benefits: Fast retrieval, advanced search, near real-time indexing
Best For: Frequently updated or search-intensive environments
Pricing: Starts at ~$0.088/hour (t3.small.search)

3. Amazon RDS / DynamoDB

Use Case: Structured or transactional data
Benefits: High performance, fully managed, reliable
Best For: Profiles, catalogs, operational metrics
Pricing:
- RDS: From ~$0.017/hour
- DynamoDB: ~$0.25 per million reads

Integration Options

Direct Integrations

SharePoint Online
Salesforce
ServiceNow
Confluence
Jira
Microsoft Teams
Google Workspace
Slack

Custom Integrations

REST APIs
Database connectors
File system crawlers
Real-time streams (Kinesis, EventBridge)
Git repositories

Custom Agent Deployment Types

1. Single-Region Deployment

Use Case: Simple, cost-efficient setups
Benefits: Low latency, minimal complexity
Considerations: Single point of failure

2. Multi-Region Deployment

Use Case: Global user base, disaster recovery
Benefits: High availability, reduced latency globally
Considerations: Sync overhead, added cost

3. Hybrid Deployment

Use Case: Compliance or sensitive data
Benefits: Local control + cloud scalability
Considerations: Connectivity, governance requirements

How Amazon Q Custom Agent Architecture Works

Let’s understand how an Amazon Q Custom Agent works in real time. The following high-level architecture shows the simple flow of how a request is processed from start to finish. When a user asks a question, the Amazon Q Agent interprets the request and determines whether it needs to retrieve information or perform an action. If the request is informational, the agent searches the connected Knowledge Base, retrieves the most relevant documents, and generates a clear response for the user. If the request requires an operation such as creating a resource or retrieving system data the agent triggers a Lambda function or OpenAPI action to execute the task on AWS services. Once the action is completed, the result is returned to the agent, which then converts it into a user-friendly answer.

┌────────────────────────┐
│      User Question     │
└─────────────┬──────────┘
              │
              ▼
┌────────────────────────┐
│      Amazon Q Agent    │
└─────────────┬──────────┘
              │
              ▼
┌────────────────────────┐
│      Processing Logic  │
└─────────────┬──────────┘
              │
              ▼
┌────────────────────────┐        ┌────────────────────────┐
│     Document Search    │ ◄ ──── │     Knowledge Base     │
└─────────────┬──────────┘        └────────────────────────┘
              │
              ▼
┌────────────────────────┐
│    Response Generation │
└─────────────┬──────────┘
              │
       ┌──────┴─────────────────────────────┐
       ▼                                    ▼
┌────────────────────────┐    ┌────────────────────────┐
│       User Answer      │    │    Action Execution    │
└────────────────────────┘    └─────────────┬──────────┘
                                            │
                                            ▼
                               ┌────────────────────────┐
                               │      AWS Services      │
                               └────────────────────────┘

This simple flow helps illustrate how the agent intelligently switches between knowledge retrieval and operational execution, allowing it to both answer questions and perform real-world tasks seamlessly.

Pricing Breakdown

Important: All these prices were collected using the AWS Pricing Calculator MCP Server integrated with Amazon Q Pro. If this sounds interesting, follow this guide: Link

Amazon Q Business Pricing

Pro Tier: $20 per user per month
Lite Tier: $3 per user per month
Includes: Agent interactions, knowledge base queries, basic actions

Knowledge Base Components

Document Processing: $0.10 per 1,000 documents
Vector Storage: $0.30 per GB per month
Query Processing: $0.004 per query

Storage Costs (Monthly)

S3 Standard: $0.023 per GB
OpenSearch: $0.088/hour (t3.small) ≈ $63/month
RDS: Starting at $12/month (db.t3.micro)

Integration Costs

API Gateway: $3.50 per million API calls
Lambda: $0.20 per 1 million requests
CloudWatch: $0.30 per GB ingested

Advanced Features and Integrations

1. Multi-Modal Capabilities

Text Processing: Documents, emails, chat logs
Image Analysis: Diagrams, screenshots, charts
Voice Integration: Speech-to-text for voice-based queries

2. Security and Compliance

Identity Integration: AWS IAM, Active Directory, SAML
Data Encryption: Encryption at rest and in transit
Access Controls: Role-based permissions, data-level filtering
Audit Logging: Full interaction history for compliance

3. Enterprise Integrations

Slack / Microsoft Teams: Direct chat-based interactions
ServiceNow: Ticket creation, updates, and workflow automation
Jira: Issue tracking and project updates
Salesforce: Access to customer and sales data

Best Practices for Custom Agent Development

4. Design Principles

Start Simple: Begin with a basic knowledge base; add actions later
User-Centric: Align conversations with real user workflows
Iterative Improvement: Refine responses based on user behavior
Security First: Implement proper access controls early

5. Performance Optimization

Chunk Size: Optimize chunking for better document retrieval
Index Strategy: Use indexing that fits your content patterns
Caching: Cache responses for frequently asked questions
Monitoring: Track latency, usage, and user satisfaction

6. Content Management

Regular Updates: Keep documents and knowledge up to date
Version Control: Track content changes through Git or similar tools
Quality Assurance: Test accuracy of responses regularly
Feedback Loop: Collect user feedback for continuous improvement

Conclusion: Amazon Q Custom Agents offer a transformative approach to knowledge management and task automation by combining large language models with your organization’s specific context to reduce manual work and improve accuracy. Whether you’re creating a simple FAQ bot or a complex operational assistant, success depends on understanding user needs, carefully curating your knowledge base, and continuously enhancing the agent’s capabilities. The investment quickly pays off through reduced support efforts, faster problem resolution, and increased productivity. As organizations generate more data and face growing operational complexity, intelligent agents will become essential for maintaining a competitive advantage.

The future of work is collaborative intelligence between humans and AI agents - and with Amazon Q, that future is available today.

Stay tuned for my dev.to article series on AWS Custom Agents. I’m planning to build and showcase the custom agents mentioned in this article, and I’ll continue sharing updates and new implementations as the series progresses.