Cheatsheet: AI Agent Architecture and Workflow Patterns
Explore the foundational components of AI agents and how they interact within different architecture patterns. Learn to distinguish AI agentic workflows and apply four essential workflow design patterns to build scalable, modular, and adaptive AI systems that perform complex tasks effectively.
By the end of this lesson, you will be able to:
Identify the three core components of every AI agent and explain how they interact.
Distinguish between an agent's architecture pattern and its agentic workflow.
Describe the four most common agentic workflow design patterns and when to use each.
Explain why modularity in agent design is a practical engineering advantage.
The core components of an AI agent
Before designing a system, you need to understand its parts. Across all the case studies in this course, from MACRS and Eureka to WebVoyager and ChainBuddy, a consistent pattern emerges. No matter how sophisticated the application, every AI agent is built from the same three fundamental components: instructions, tools, and a model.
Understanding these building blocks is the foundation of AI agent architecture. More importantly, understanding them as modular, independently configurable elements is what makes modern agent design practical and scalable.
Instructions
Instructions are the context, constraints, and behavioral directives that shape what the agent is and how it acts. These are typically embedded in the system prompt, a block of text the agent receives before any user interaction begins, and they define the agent's persona, the scope of its responsibilities, its tone, and its guardrails.
Well-written instructions do more than describe a role. They define the agent's decision-making boundaries: what it is allowed to do, what it must escalate to a human, and how it should behave when it encounters ambiguity. In production systems, instructions often encode compliance requirements, safety policies, and escalation procedures alongside more straightforward behavioral guidance.
Consider the difference between telling an agent to be helpful versus instructing it to answer customer questions about billing only, escalate refund requests over $500 to a human agent, and never discuss competitor pricing. The second set of instructions produces a far more predictable, trustworthy system. Precision in instruction design is one of the highest-leverage activities in agent development.
Tools
Tools are the external capabilities that allow the agent to take action beyond generating text. Without tools, an LLM-powered agent can reason and plan, but it cannot actually affect anything in the world. Tools are what transform a language model from a conversational partner into an active system.
Tools can take many forms depending on the agent's purpose. A customer support agent might have access to a CRM lookup tool, a knowledge base search tool, and a ticket-creation API. A coding agent might use a code execution sandbox, a file system reader, and a web search tool. A multimodal web agent like WebVoyager, as studied in Chapter 7, uses browser interaction tools, click, scroll, type, and screenshot, to navigate real websites autonomously. ...