Scalable Architecture Pattern
Building AI-Powered Systems with a Knowledge Base – Agent Core
A Personal Starting Point
Over the past months, I’ve been exploring what it really means to build infrastructure for AI. Not just running models, but architecting systems that think, learn, and evolve.
My playground has been Amazon Bedrock, AWS’s managed platform for foundation models and agents. As I built small experiments (summarizers, reviewers, evaluators), a pattern began to appear. A structure that kept proving itself across projects.
I started calling it the Scalable Architecture Pattern.
It’s a practical architectural model for designing Agentic AI systems — systems that use reasoning agents, knowledge, and tool integrations to act intelligently across cloud infrastructure.
What Is the Scalable Architecture Pattern?
It’s a way to design AI-powered systems that can grow; in data, in reasoning, and in integrations; without collapsing under complexity.
At its center sits a Knowledge Base + Agent Core, wrapped in a Hexagonal Architecture (Ports & Adapters).
Think of it as an evolution of traditional software architecture, designed for systems that use models and memory as first-class components.
The Core Idea: Knowledge Base + Agent
| Component | Role in AI Infrastructure |
| Knowledge Base | The memory layer. It holds structured, retrievable facts (documents, code, decisions, etc.), typically stored as embeddings. (In Bedrock, this is an Amazon Bedrock Knowledge Base powered by Titan Text Embeddings v2.) |
| Agent | The reasoning layer. It interprets context, applies persona rules, and performs tasks like summarizing, reviewing, or auditing. (Using Bedrock Agent powered by Claude 3.7 Sonnet v1) |
Together, they form what I like to call the Team Mind, the combination of memory and reasoning that defines intelligent behavior.
The Hexagonal Structure
How the Pattern Works
- The Core Domain encapsulates reasoning and knowledge, independent of any integration.
- Ports define what the Core needs from the world (get_ticket(), search_context(), store_report()), not how it gets it.
- Adapters implement those ports (Jira, Bitbucket, Confluence, etc. – via Lambda function)
- Driving Adapters trigger use cases: scheduled jobs, REST calls, or event hooks
- The pattern ensures everything external is replaceable without changing the brain of the system.
Why This Pattern Enables Agentic AI
We’re now in the Agentic AI era, where AI sytems no longer just respond, they reason and act. The Scalable Architecture Pattern is the missing backbone that makes this possible.
| Agentic AI Concept | How the Pattern Supports It |
| Autonomous Reasoning | Agent executes multi-step tasks within a governed core. |
| Memory & Context | Knowledge Base provides persistent retrieval for long-term understanding. |
| Tool Use | Ports & Adapters connect the core to APIs and infrastructure safely. |
| Reflection & Learning | Outputs and feedback loops can be stored and re-evaluated. |
| Governance & Safety | Guardrails, IAM, and Bedrock’s safety layers enforce policies. |
In short: the pattern turns large models into modular, auditable, and extensible systems; the essence of Agentic AI
Why Amazon Bedrock Is Perfect for This Pattern
As I explored Bedrock, several pieces clicked naturally into this architecture:
| Bedrock Feature | Architectural Role |
| Knowledge Bases (RAG) | The memory layer – retrieval, versioned context |
| Agents for Bedrock | The reasoning layer – persona, orchestration, and tool use |
| Action Groups (Lambda) | The adapters – connect to Jira, Bitbucket, Confluence |
| MCP Integration (future) | Standardized ports for multi-tool ecosystems |
| Guardrails + IAM | Governance and security boundaries |
With these components, the Scalable Architecture Pattern becomes not just theoretical, it’s deployable on AWS today.
Real-World Example: The Technical Summarizer
One of my prototypes applies the pattern directly:
- Core: Bedrock Agent (Claude 3.7 Sonnet v1) + Knowledge Base (Titan Text Embeddings v2).
- Ports: Issue Tracker, Version Control, Publisher.
- Adapters: Jira REST, Bitbucket REST, Slack Webhook (later on with MCP).
- Automation: EventBridge -> Lambda -> InvokeAgent.
Use Cases:
- Daily chapter summaries linked to team context.
- Event-sourcing proposals derived from books + docs.
- Documentation vs Implementation audits.
- Code changes reviewer
Each workflow reuses the same Core, swapping adapters as needed.
Governance Built In
Because reasoning happens through the Knowledge Base, every output includes citations. Guardrails enforce persona and privacy boundaries. IAM policies restrict adapter access. This gives the architecture both scalability and accountability, two things AI systems often lack.
Pattern Benefits
| Principle | Outcome |
| Modularity | Swap tools or models without rewriting logic |
| Extensibility | Add new data sources easily |
| Observability | Trace reasoning and retrieval steps |
| Security | Fine-grained control over adapters |
| Reusability | Same core supports multiple AI use cases |
Why This Matters
Traditional architecture scale throughput.
The Scalable Architecture Pattern scales intelligence.
It gives architects and engineers a way to:
- Treat AI reasoning as a first-class domain.
- Isolate integrations for flexibility and governance.
- Build Agentic AI systems that can evolve safely and continuously.
Closing Reflection
My exploration of Amazon Bedrock started as curiosity (can I automate technical summaries?).
It evolved into a deeper realization: We’re no longer just scaling infrastructure. We’re scaling intelligence.
The Scalable Architecture Pattern is my attempt to give that evolution structure, a way to design AI-powered systems that stay understandable, governable, and endlessly extensible.
References
Applying Hexagonal Architecture in AI Agent Development – Medium
Hexagonal Architecture: Isolate AI Logic Effectively – Sparkco
Understanding Knowledge-Based Agents in AI – Alltius