Master the essential theoretical foundations of building effective AI agents. Explore LLM autonomy, tool integration, and the core principles that power intelligent systems.
Welcome to the fundamentals of agentic AI. This comprehensive guide will take you through the theoretical foundations of building effective AI agents, focusing on LLM autonomy and tool integration. While most practical implementations are hands-on, understanding these core concepts is essential for building robust agentic systems.
💡 Key Focus: Today we dive purely into theory, discussing agents and agent architecture to build a solid foundation for practical implementations.
A natural starting point is the fundamental question: what exactly is an agent? Interestingly, there is no universally accepted definition. The term "agent" has become highly popularized and is often used to describe vastly different concepts and implementations.
"AI agents are programs where large language model (LLM) outputs control the workflow."
Essentially, a single output from an LLM decides which tasks are carried out and in what sequence. This crisp definition applies in some contexts but not all scenarios.
Important Note: While this definition is clear and practical, the reality is that "agent" can mean almost anything in the current AI landscape. The key is understanding the underlying principles rather than getting caught up in terminology.
More generally, there are several hallmarks that people use to identify agentic AI solutions. If any one of these five characteristics is present, the system might be called agentic AI:
Solutions involving multiple LLM interactions within a single workflow. For example, systems with sequential LLM calls where each builds upon the previous output.
Often regarded as a litmus test for agentic AI. When LLMs can interact with external tools, APIs, or systems to accomplish tasks beyond text generation.
Environments that allow different LLMs to send information to each other, enabling coordination, orchestration, and collaborative problem-solving.
The presence of a planner or process that coordinates activities, often implemented as an LLM itself, managing workflow and task distribution.
LLMs having the ability to control the order of tasks or choose their own 'adventure,' effectively exercising agency in decision-making processes.
Autonomy captures the essence of agentic AI. It means giving an LLM the ability to decide the sequence and nature of future actions. While this may sound sophisticated or even concerning, it is often just an advanced way of describing the LLM's control over workflow execution.
Anytime an LLM decides how to carry out future actions, it can be considered to have autonomy, which is a fundamental aspect of agentic AI systems.
Anthropic published an insightful post titled "Building Effective Agents," which distinguishes two types of agentic systems that help clarify the terminology ambiguity:
Systems where models and tools are orchestrated through predefined paths with clear structure and sequence.
Systems where models dynamically direct their own processes and tools, maintaining control over task accomplishment.
Framework Benefit: Both fall under "agentic systems," but this distinction helps clarify terminology. Many systems called "agents" might actually be workflows, and understanding this difference is crucial for proper system design.