Agentic AI - architecture approach

I’ve been delivering more and more agentic AI presentations to customers lately, and I’m noticing a trend: people are rushing straight into implementation without understanding the foundational architecture principles. Last few weeks I took a step back to focus on what agentic AI really is—and remember that it’s built on patterns we already know.

At the core of every software agent is a cognitive cycle that is often described as the perceive, reason, act loop, what Makes Agentic AI Different?

agentic AI is an extension the existing software agents paradigm, but with a crucial difference: the flow of execution is determined by a Large Language Model (LLM). This isn’t just another AI model doing classification or prediction—it’s a system that can reason through complex, multi-step problems and make autonomous decisions.

I like this framework of thinking of an evolution from:

  • Traditional AI: Model inference only
  • Software Agents: Rule-based decisions with moderate autonomy
  • Agentic AI: LLM-driven reasoning with high autonomy and goal-directed behavior

The Architecture Patterns We Already Know

agentic AI leverages coordination patterns that have been around for decades in distributed systems. We’re not reinventing the wheel—we’re applying proven architectural approaches:

  • 🔄 Hierarchical (Manager-Worker): An orchestrator agent delegates to specialized workers. Perfect for complex investigations where you need code analysis, log analysis, and testing agents working together.
  • 🤝 Peer-to-Peer (Collaborative): Agents communicate directly with each other. Think supply chain optimization with inventory, procurement, finance, and logistics agents collaborating in real-time.
  • ⚡ Sequential (Pipeline): Output flows from one agent to the next. Content moderation systems are a great example—intake → analysis → context → decision.
  • 🎯 Broadcast (Competitive): Multiple agents tackle the same problem independently, then we select the best solution or combine elements. Ideal for creative problem-solving scenarios.

Have You Seen This Rush to Implementation?

I’m curious—are you seeing this same pattern in your environment? Teams getting excited about the possibilities but skipping the architectural fundamentals? The technology is powerful, but like any distributed system, success comes down to solid design principles, clear communication patterns, and understanding when to use which coordination approach.

To dive deeper into the architectural patterns and best practices, I recommend the read of the comprehensive guidance available at https://aws.amazon.com/prescriptive-guidance/agentic-ai/