Agentic AI: The Rise of Autonomous Systems That Think, Plan, and Act

# Agentic AI: The Rise of Autonomous Systems That Think, Plan, and Act

### Author: Son Nguyen

> Date: 2025-09-22

Last month, I gave my AI agent a seemingly simple task: "Research quantum computing startups and write a brief about the most promising ones." What happened next fundamentally changed how I think about artificial intelligence.

Instead of returning a generic list scraped from the web, the agent did something remarkable. It started by questioning my criteria for "promising" - was I looking at funding, technical innovation, or market readiness? It then created a research plan, gathered data from multiple sources, cross-referenced conflicting information, and even reached out to analyze recent patent filings. After three hours of autonomous work, it delivered not just a report, but a nuanced analysis with investment recommendations based on my previous interests.

This wasn't a chatbot regurgitating information. This was an _agent_ - a system that could think, plan, and act independently. Welcome to the era of Agentic AI.

---

## From Assistants to Agents: A Fundamental Shift

The difference between traditional AI assistants and agentic systems isn't just incremental - it's fundamental. Think of it this way: ChatGPT is like a brilliant colleague you can consult. An agentic AI system is like hiring an entire team that works while you sleep.

Traditional AI assistants excel at answering questions. You prompt, they respond. It's a conversation, but you're always driving. Agentic AI flips this dynamic. You set an objective, and the agent figures out how to achieve it - planning its approach, using tools, learning from mistakes, and adapting its strategy.

I first experienced this shift while building a research agent for a venture capital firm. The initial requirement was simple: automate the due diligence process. But what we built was something far more profound - an agent that could:

- Autonomously identify relevant companies based on investment thesis
- Investigate their technology, team, and market position
- Generate detailed competitive analyses
- Flag potential risks and opportunities
- Even draft initial investment memos

The key insight? We didn't program these specific behaviors. We gave the agent goals, tools, and the ability to reason about how to use them. The sophisticated behaviors emerged from these simple principles.

---

## The Architecture of Autonomy

Building truly agentic systems requires rethinking how we structure AI applications. After months of experimentation with various approaches, I've identified several key patterns that separate true agents from simple automation.

### The Planning Paradox

Early in my journey with agentic systems, I made a classic mistake: over-planning. I built an agent that would create elaborate 20-step plans before taking any action. It was impressive to watch it think, but it rarely completed tasks successfully. Reality is messy, and plans need to adapt.

The breakthrough came when I switched to what I call "intentional wandering" - the agent maintains a clear goal but makes decisions step-by-step, constantly reassessing based on what it learns. Like a detective following leads rather than a robot executing instructions.

Here's a simplified example of how this works in practice:

```
# Instead of rigid planning:
plan = ["Step 1: Research topic", "Step 2: Write draft", "Step 3: Edit"]

# Adaptive reasoning:
while not goal_achieved:
context = assess_current_situation()
next_action = reason_about_best_action(context, goal)
result = execute_action(next_action)
learn_from_result(result)
```

This approach seems less efficient on paper, but it's remarkably robust in practice. The agent can recover from errors, incorporate new information, and even discover better approaches than originally conceived.

#### Code: Minimal step-wise planner with LangGraph (Python)

```
# pip install langgraph langchain langchain-openai langgraph-checkpoint-sqlite
from typing import TypedDict, List
from langgraph.graph import StateGraph, START, END, MessagesState
from langgraph.prebuilt import ToolNode
from langchain.chat_models import init_chat_model
from langgraph.checkpoint.sqlite import SqliteSaver

# --- State ---
class MyState(MessagesState):
goal: str

# --- Tools (toy) ---
def search(query: str) -> str:
return f"Top snippets for '{query}' ..."

def write_outline(topic: str) -> str:
return f"- Intro to {topic}\n- Key players\n- Risks\n- Outlook"

tools = [
{
"name": "search",
"description": "Web search for factual lookup",
"input_schema": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]},
"function": search,
},
{
"name": "write_outline",
"description": "Create a brief outline for a topic",
"input_schema": {"type": "object", "properties": {"topic": {"type": "string"}}, "required": ["topic"]},
"function": write_outline,