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agentic ai

Curriculum Index

Current LevelInitiate
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  • 1.1 The Shift from Prompting to Agency
  • 1.2 The Three-Layer Agent Architecture
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Module 1: Foundations of Agentic AI & The PRAL Loop•Lesson 1

1.1 The Shift from Prompting to Agency

### Beyond Static Prompting
In traditional LLM workflows, an application sends a single prompt and receives a single completion. If the output contains errors or lacks data, the process fails.

**Agentic AI** introduces autonomous loops where the system operates as an **agent**:
1. **Perceive:** Ingest signals from environment, API responses, or user objectives.
2. **Reason:** Formulate multi-step strategies and decide which tools to summon.
3. **Act:** Invoke functions, query databases, or execute code.
4. **Learn (Evaluate):** Inspect results, catch exceptions, and self-correct until the goal state is reached.

example.pyPython 3.11
class AgentLoop:
    def __init__(self, objective: str):
        self.objective = objective
        self.state = "INITIALIZED"

    def transition(self, next_state: str):
        self.state = next_state
        return f"STATE_TRANSITION::{self.state}"

agent = AgentLoop("DATA_AUDIT")
print(agent.transition("PRAL_ACTIVE"))
Python 3.11
•
Interactive Challenge
+50 XP
Challenge Objective

Set the variable state to 'PRAL_ACTIVE' and print it.

solution.py
UTF-84 SpacesAuto-saved
cpython3.11:~/workspace
CPython 3.11.8 (WASM)
$python3 -u solution.py
Click "Compile & Run" (or press Ctrl + Enter) to compile AST, verify bytecode, and execute the solution.