Agents are end-to-end systems assigned to a specific Goal. These systems operate with varying degrees of autonomy and can be classified based on how they execute tasks:
Workflow-Based Systems: All steps are explicitly defined. The AI simply generates content or executes actions following these strict instructions.
Agentic Workflows: The system is equipped with tools, knowledge bases, and boundary conditions. The AI decides the trajectory of its execution to reach the desired outcome. This often includes Human-in-the-loop (HITL) handoffs or partial autonomy, where the AI seeks clarification when a path is unclear.
Fully Autonomous Agents: The AI navigates the task independently, guided only by high-level safeguards such as budget constraints or iteration limits.
Patterns vs. Techniques
Agentic Patterns refer to the end-to-end blueprint of a process. In contrast, Techniques are the granular capabilities—such as planning, tool-calling, and self-reflection—that make up that blueprint.
The Building Blocks of Agents
LLM (The Brain): Interprets signals and manages the reasoning process to reach the end goal.
Tools: The action-performing interfaces that allow an LLM to execute specific parts of a process.
Retrieval: The system's knowledge center, where the LLM enriches its context regarding the query at hand.