Agentic AI: Reference Architecture, Frameworks & Design Patterns
A Playbook
“Autonomy is not just about intelligence—it’s about intent, memory, and coordination.”
Introduction
We are entering an era where software isn’t just reactive—it’s proactive, collaborative, and increasingly autonomous. This shift is being driven by Agentic AI: systems composed of intelligent agents that can reason, plan, remember, use tools, and interact with each other to accomplish complex goals.
Agentic AI represents a paradigm shift in artificial intelligence—moving from tools that execute instructions to systems that pursue goals with minimal human oversight. At its core is the concept of agency: the capacity for independent reasoning, planning, and action that transcends traditional automation.
For a deeper dive into the meaning of "agent," "agency," and "agentic," See my companion article here.
Four Defining Characteristics of Agentic AI
For an AI system to be considered agentic, it must exhibit four foundational traits:
Autonomous decision-making: The ability to analyze situations and act independently without explicit instructions for each step
Goal-driven behavior: Working toward defined objectives through multi-step task planning and execution
Learning and adaptation: Continuously improving performance by learning from interactions and outcomes
Advanced reasoning: Managing complex workflows by connecting systems, tools, and data across a dynamic environment
Unlike traditional systems that only parse input, agentic systems observe, infer, and act. The diagram below shows how an agent might respond not just with a templated answer—but by routing through a reasoning engine, identifying user intent, invoking a tool, and constructing a targeted response from retrieved data.
This marks a transition from stateless, single-shot AI to persistent, orchestrated, and memory-augmented behavior—where agents don’t just react, they reflect and refine.
Why Agentic AI Now?
While the concept of agents isn’t new, what’s different today is the maturity of the surrounding ecosystem, including
Frameworks like LangChain, LangGraph, AutoGen, and CrewAI (there are a lot more)
Orchestration systems such as AGNTCY (this is also evolving)
Semantic memory layers like Mem0 (in addition to pure graph and vector stores)
Context protocols like MCP
And the convergence of LLMs, APIs, and graph-based knowledge layers
These building blocks allow us to move beyond prototypes and research demos into deployable, composable agent-based systems that can scale in enterprise, product, and research contexts.
Why Orchestration, Memory, and Context Flow Matter
Agentic systems don’t succeed because they’re smart—they succeed because they’re structured.
Orchestration (e.g., AGNTCY) allows multiple agents to coordinate, stay goal-aligned, and apply policies across workflows. Without it, you're just running isolated calls to LLMs.
Memory (e.g., Mem0) gives agents context, recall, and continuity. This is what allows them to learn from outcomes and evolve across tasks.
Context propagation (e.g., MCP) ensures that agents can share task-relevant information without hard-coded dependencies—crucial for composability and statefulness.
These aren't optional components—they're what make agency scalable, composable, and accountable.
What’s Ahead in This Series
This article kicks off a structured deep dive into the architecture, design patterns, security, and implementation pathways of Agentic AI.
Here’s what’s coming next:
Part 2: From RPA to Agentic AI — how we got here, and why automation needed to evolve
Part 3: The Agentic AI Reference Architecture — orchestration, communication, memory, context
Part 4: Security and Governance for Multi-Agent Systems
Part 5: Design Patterns: Planning, Tool Use, and Human-in-the-Loop
Part 6: Choices: How can one choose from the reference architecture
Part 7: The Future of Agentic AI: Quantum, Edge, and Beyond