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Welcome to “Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems.” As we look across the landscape of modern artificial intelligence, we see a clear evolution from simple, reactive programs to sophisticated, autonomous entities capable of understanding context, making decisions, and interacting dynamically with their environment and other systems. These are the intelligent agents and the agentic systems they comprise. The advent of powerful large language models (LLMs) has provided unprecedented capabilities for understanding and generating human-like content such as text and media, serving as the cognitive engine for many of these agents. However, orchestrating these capabilities into systems that can reliably achieve complex goals requires more than just a powerful model. It requires structure, design, and a thoughtful approach to how the agent perceives, plans, acts, and interacts. Think of building intelligent systems as creating a complex work of art or engineering on a canvas. This canvas isn’t a blank visual space, but rather the underlying infrastructure and frameworks that provide the environment and tools for your agents to exist and operate. It’s the foundation upon which you’ll build your intelligent application, managing state, communication, tool access, and the flow of logic. Building effectively on this agentic canvas demands more than just throwing components together. It requires understanding proven techniques – patterns – that address common challenges in designing and implementing agent behavior. Just as architectural patterns guide the construction of a building, or design patterns structure software, agentic design patterns provide reusable solutions for the recurring problems you’ll face when bringing intelligent agents to life on your chosen canvas. At its core, an agentic system is a computational entity designed to perceive its environment (both digital and potentially physical), make informed decisions based on those perceptions and a set of predefined or learned goals, and execute actions to achieve those goals autonomously. Unlike traditional software, which follows rigid, step-by-step instructions, agents exhibit a degree of flexibility and initiative. Imagine you need a system to manage customer inquiries. A traditional system might follow a fixed script. An agentic system, however, could perceive the nuances of a customer’s query, access knowledge bases, interact with other internal systems (like order management), potentially ask clarifying questions, and proactively resolve the issue, perhaps even anticipating future needs. These agents operate on the canvas of your application’s infrastructure, utilizing the services and data available to them. Agentic systems are often characterized by features like autonomy, allowing them to act without constant human oversight; proactiveness, initiating actions towards their goals; and reactiveness, responding effectively to changes in their environment. They are fundamentally goal-oriented, constantly working towards objectives. A critical capability is tool use, enabling them to interact with external APIs, databases, or services – effectively reaching out beyond their immediate canvas. They possess memory, retain information across interactions, and can engage in communication with users, other systems, or even other agents operating on the same or connected canvases. Effectively realizing these characteristics introduces significant complexity. How does the agent maintain state across multiple steps on its canvas? How does it decide when and how to use a tool? How is communication between different agents managed? How do you build resilience into the system to handle unexpected outcomes or errors?