Agentic Foundations

Agentic Foundations

Building a reliable agent requires more than just a prompt; it requires a deep understanding of cognitive architecture and the primitives that allow an LLM to function as a “reasoning engine.”

🏛️ Theoretical Frameworks

• What is an Agent? Exploring the spectrum of agency—from simple scripts to fully autonomous entities.

• Cognitive Architecture How to structure the ‘brain’ of an agent using memory, planning, and sensory modules.

⚙️ Modern Primitives

• Inference-Time Compute Understanding why “thinking longer” (Search, Verifiers, and Rollouts) leads to better agentic performance.

• Modern LLM Primitives A look at function calling, structured output (JSON mode), and token-level control.

Core Learning Objectives

By the end of this module, you should be able to:

1. Distinguish between a Chain and an Agent.
2. Understand the trade-offs between Pre-training power and Inference-time reasoning.
3. Design a basic state-machine-based architecture for a task-oriented assistant.

Next Steps

After mastering these foundations, you can explore how these components are assembled in Agent Architecture.