Agentic Design Patterns Every Developer Should Know
The patterns behind effective AI agents are not new — they are borrowed from distributed systems, game theory, and workflow engines. Here are the ones that matter.
I published an open-source collection of agentic design patterns on GitHub because I was tired of seeing the same mistakes in every agent implementation. The patterns are not original—they are borrowed from disciplines that solved similar problems decades ago.
Why Patterns Matter
AI agents are not chatbots with extra steps. They are autonomous systems that make decisions, take actions, and handle failures. Without disciplined patterns, they become unpredictable, expensive, and dangerous.
The Core Patterns
1. ReAct (Reason + Act)
The agent thinks before it acts, observes the result, and adjusts. This is the foundational pattern. If your agent is not doing this, it is not an agent—it is a script.
Microsoft implementation: Semantic Kernel's planner uses ReAct natively. Azure AI's function calling supports this pattern out of the box.
2. Tool Use
The agent has access to external tools (APIs, databases, calculators) and decides which to use based on the task. This is where agents become useful—they can take real actions, not just generate text.
Microsoft implementation: Copilot Studio's connectors, Semantic Kernel plugins, and Azure AI function calling all implement this pattern.
3. Reflection
The agent evaluates its own output before presenting it. "Is this answer correct? Is it complete? Does it address the user's actual question?" Self-critique is the difference between a demo and a production system.
4. Planning
The agent decomposes complex tasks into subtasks, executes them in order, and handles dependencies. This is borrowed directly from task scheduling in distributed systems.
Microsoft implementation: AutoGen's multi-agent orchestration handles complex planning scenarios with multiple specialized agents.
5. Multi-Agent Collaboration
Multiple specialized agents work together on a task. One agent researches, another writes, a third reviews. This mirrors how high-performing human teams work.
Microsoft implementation: AutoGen was built for this. Microsoft's investment in multi-agent frameworks is the most aggressive in the industry.
6. Memory
The agent maintains context across interactions—both short-term (conversation) and long-term (learned preferences, past decisions). Without memory, every interaction starts from zero.
The GitHub Repo
The full collection is open source at github.com/GGCryptoh/agentic-design-patterns. Each pattern includes:
- A plain-language explanation
- A reference implementation
- Anti-patterns to avoid
- Microsoft-specific implementation guidance
Star it, fork it, contribute. The patterns evolve as the tooling matures.