Unlike a traditional RAG application that simply retrieves documents and generates responses, this architecture uses Amazon AgentCore to enable intelligent reasoning, planning, and tool orchestration while leveraging Amazon Bedrock Knowledge Bases to provide managed retrieval from enterprise knowledge.
The solution separates the architecture into three logical layers:
- Knowledge Layer – Manages enterprise knowledge and semantic retrieval.
- Agent Runtime – Performs reasoning, planning, and orchestration.
- Action Layer (Agent Tools) – Executes business operations through enterprise systems.
Cross-cutting security, governance, and observability services provide enterprise-grade operational controls.
Architecture Components
1. Knowledge Layer
The Knowledge Layer is responsible for transforming enterprise data into searchable knowledge that can be consumed by foundation models.
Enterprise Data Sources
Enterprise knowledge may originate from multiple sources including:
- Amazon S3
- Documents (PDF, DOCX, PPTX, TXT)
- Internal web portals
- Databases
- Enterprise SaaS applications
- Business APIs
These repositories contain the information that grounds the responses generated by the AI agent.
Amazon S3
Amazon S3 serves as the centralized storage layer for enterprise documents and unstructured content. It provides durable, scalable storage and acts as the primary ingestion source for Amazon Bedrock Knowledge Bases.
Amazon Bedrock Knowledge Base
Amazon Bedrock Knowledge Bases provides a fully managed Retrieval-Augmented Generation (RAG) service that automates the end-to-end knowledge ingestion and retrieval workflow.
Key capabilities include:
- Automatic document ingestion
- Incremental synchronization
- Document chunking
- Metadata extraction
- Embedding generation
- Semantic retrieval
- Context retrieval and reranking
By abstracting these operations, developers can focus on application logic rather than building and maintaining custom ingestion pipelines.
Amazon OpenSearch Serverless (Vector Store)
Amazon OpenSearch Serverless provides the vector database used by Amazon Bedrock Knowledge Bases to store embeddings and perform similarity search.
Its primary responsibilities include:
- Vector indexing
- k-Nearest Neighbor (k-NN) search
- Metadata filtering
- High-performance semantic retrieval
The application interacts with Amazon Bedrock Knowledge Bases rather than directly querying the vector store. OpenSearch Serverless functions as the managed storage engine that enables semantic search at scale.
2. Agent Runtime
The Agent Runtime represents the intelligent decision-making layer of the architecture.
Instead of simply sending prompts to an LLM, Amazon AgentCore enables autonomous execution of multi-step workflows.
Amazon AgentCore
Amazon AgentCore serves as the central orchestration engine responsible for:
- Reasoning
- Task planning
- Workflow orchestration
- Conversation memory
- Tool selection
- Tool execution
- Multi-step decision making
AgentCore determines how a request should be fulfilled by deciding whether enterprise knowledge, external tools, or business systems are required.
For example, a user request such as:
“Summarize yesterday’s support tickets and notify the operations team.”
may result in the following execution plan:
- Retrieve support procedures from the Knowledge Base.
- Query the ticketing system.
- Generate a summary using Amazon Bedrock.
- Invoke an email notification tool.
- Return the completion status to the user.
This planning capability distinguishes Agentic AI from traditional chatbot architectures.
Retrieval-Augmented Generation (RAG)
When enterprise knowledge is required, Amazon AgentCore invokes Amazon Bedrock Knowledge Bases.
The retrieval workflow consists of:
- Converting the user query into embeddings.
- Searching the vector store.
- Retrieving the most relevant document chunks.
- Passing the retrieved context to the foundation model.
This ensures that responses are grounded in enterprise knowledge rather than relying solely on the model’s pre-trained information.
Amazon Bedrock
Amazon Bedrock provides managed access to foundation models from multiple providers.
Supported models may include:
- Anthropic Claude
- Amazon Nova
- Meta Llama
- Mistral AI
The foundation model receives:
- User prompt
- Retrieved enterprise context
- Outputs from business tools
It then generates a context-aware response for the user.
3. Agent Tools
Not every user request can be satisfied through document retrieval alone. Enterprise agents often need to perform actions across business systems.
Agent Tools provide these action capabilities.
Unlike the Knowledge Layer, which supplies information, Agent Tools enable the agent to execute operations.
Typical tools include:
AWS Lambda
Executes custom business logic such as:
- Order processing
- Workflow automation
- Document generation
- Report creation
External APIs
AgentCore can securely invoke REST or GraphQL APIs exposed through Amazon API Gateway or third-party services.
Examples include:
- CRM systems
- ERP platforms
- ITSM solutions
- Payment services
Database Query Tool
A controlled database access layer enables agents to query operational databases such as Amazon Aurora without exposing unrestricted SQL access.
Typical use cases include:
- Customer lookup
- Inventory queries
- Financial reporting
- Order status
Code Interpreter / Sandbox
Allows the agent to execute isolated code for tasks such as:
- Data analysis
- Calculations
- File processing
- Chart generation
Enterprise Tools
Additional integrations may include:
- Email services
- Notification platforms
- Ticketing systems
- Search systems
- Internal business applications
These tools allow agents to transition from answering questions to completing end-to-end business tasks.
Governance and Responsible AI
Amazon AgentCore Policy
Amazon AgentCore Policy governs how agents interact with enterprise systems.
It defines:
- Identity and authentication
- Role-based permissions
- Tool authorization
- Resource-level access
- Execution policies
For example, an HR agent may access employee systems while a Finance agent can invoke accounting services, ensuring least-privilege access.
Amazon Bedrock Guardrails
Amazon Bedrock Guardrails helps organizations enforce responsible AI policies across all interactions.
Capabilities include:
- Content moderation
- Personally identifiable information (PII) protection
- Prompt injection detection
- Harmful content filtering
- Topic restrictions
- Safety policy enforcement
Guardrails are applied before and after model inference to improve the safety and compliance of AI-generated responses.
Security
The architecture incorporates AWS security services to protect enterprise workloads.
- AWS Identity and Access Management (IAM) manages authentication and authorization.
- AWS Key Management Service (AWS KMS) encrypts data at rest and in transit.
- Amazon Virtual Private Cloud (Amazon VPC) provides network isolation.
- AWS CloudTrail records API activity for auditing and compliance.
Observability and Operations
Operational visibility is achieved using native AWS monitoring services.
- Amazon CloudWatch collects metrics, logs, dashboards, and alarms.
- AWS X-Ray traces distributed requests across components.
- Amazon AgentCore Observability provides insight into agent execution, including reasoning paths, tool invocations, latency, token consumption, and failures.
These services simplify troubleshooting, performance optimization, and operational governance.
End-to-End Request Flow
- Enterprise documents are stored in Amazon S3.
- Amazon Bedrock Knowledge Bases ingests, chunks, embeds, and indexes the content into Amazon OpenSearch Serverless.
- A user submits a request to Amazon AgentCore.
- AgentCore analyzes the request and determines whether knowledge retrieval, tool execution, or both are required.
- If enterprise knowledge is needed, AgentCore retrieves relevant context from Amazon Bedrock Knowledge Bases.
- If business actions are required, AgentCore invokes the appropriate Agent Tools, such as AWS Lambda, enterprise APIs, or a database query tool.
- Amazon Bedrock generates a grounded response using the retrieved context and any tool outputs.
- Amazon Bedrock Guardrails validates the interaction against organizational AI safety policies.
- Amazon AgentCore returns a response, optionally including citations to enterprise knowledge sources.
Key Architectural Benefits
- Managed RAG – Amazon Bedrock Knowledge Bases automates ingestion, embedding generation, synchronization, and semantic retrieval.
- Intelligent orchestration – Amazon AgentCore enables autonomous reasoning, planning, and multi-step workflow execution.
- Enterprise integration – Agent Tools allow AI agents to interact securely with business systems and perform real-world actions.
- Responsible AI – Amazon Bedrock Guardrails and Amazon AgentCore Policy enforce governance, security, and compliance.
- Operational excellence – Native AWS observability services provide comprehensive monitoring and diagnostics.
- Scalability – Fully managed AWS services reduce operational overhead while supporting enterprise-scale workloads.
This architecture is well suited for enterprise use cases such as IT service management, customer support, employee assistants, knowledge management, financial operations, healthcare, legal research, and other AI-driven workflows that require both trusted knowledge retrieval and autonomous task execution.