Agentic AI Explained: Building Autonomous Systems That Plan, Act, and Learn

Everything we’ve covered so far—calling LLMs, building RAG systems, using frameworks—follows a pattern: human asks, AI responds. One turn, done.

Agentic AI breaks that pattern. Instead of responding once, agents can plan multi-step approaches, use tools, reflect on their progress, and autonomously work toward goals. This is the frontier of practical AI in 2025.

I’ll be honest: this space is evolving fast, and there’s a lot of hype. Let me separate what actually works from what’s still research.

Series Navigation: Part 1: GenAI Intro → Part 2: LLMs → Part 3: Frameworks → Part 4: Agentic AI (You are here) → Part 5: Building Agents → Part 6: Enterprise

What Makes an AI “Agentic”?

An AI agent has these characteristics:

1. Goal-oriented: Given a goal, not just a question
2. Autonomous planning: Breaks goals into steps without explicit instructions
3. Tool use: Can execute actions—search, code, API calls, file operations
4. Observation and adaptation: Sees results and adjusts approach
5. Memory: Maintains context across multiple steps

Simple chain: “Summarize this document” → [Summary]

Agent: “Research competitor pricing and create a comparison report” → [Plans steps] → [Searches web] → [Extracts data] → [Compares] → [Generates report]

The Agent Architecture

Most agent systems follow a similar pattern:

┌─────────────────────────────────────────────────────────────────┐
│                         AGENT LOOP                              │
│                                                                 │
│   ┌─────────┐    ┌─────────┐    ┌─────────┐    ┌─────────┐     │
│   │  THINK  │ →  │  PLAN   │ →  │   ACT   │ →  │ OBSERVE │     │
│   └─────────┘    └─────────┘    └─────────┘    └─────────┘     │
│        ↑                                            │           │
│        └────────────────────────────────────────────┘           │
│                     (Loop until done)                           │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ↓
    ┌─────────────────────────────────────────────────────────┐
    │                        TOOLS                            │
    │  [Search] [Code Exec] [APIs] [Files] [Database] [...]   │
    └─────────────────────────────────────────────────────────┘

The ReAct Pattern: Reasoning + Acting

ReAct (Reasoning and Acting) is the foundational agent pattern. The model alternates between thinking about what to do and taking actions.

# ReAct pattern - manual implementation
from openai import OpenAI
import json

client = OpenAI()

# Define available tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for current information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"}
                },
                "required": ["query"]
            }
        }
    },
    {
        "type": "function", 
        "function": {
            "name": "calculate",
            "description": "Perform mathematical calculations",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {"type": "string", "description": "Math expression"}
                },
                "required": ["expression"]
            }
        }
    }
]

def search_web(query: str) -> str:
    # In production, call actual search API
    return f"Search results for '{query}': [Simulated results]"

def calculate(expression: str) -> str:
    try:
        result = eval(expression)  # Use safer evaluation in production
        return str(result)
    except Exception as e:
        return f"Error: {e}"

def execute_tool(name: str, args: dict) -> str:
    """Execute a tool and return result."""
    if name == "search_web":
        return search_web(args["query"])
    elif name == "calculate":
        return calculate(args["expression"])
    return "Unknown tool"

def run_agent(goal: str, max_iterations: int = 10):
    """Run a ReAct agent loop."""
    
    messages = [
        {
            "role": "system",
            "content": """You are a helpful assistant that can use tools to accomplish goals.
            Think step by step. Use tools when needed. State your reasoning."""
        },
        {"role": "user", "content": goal}
    ]
    
    for i in range(max_iterations):
        print(f"\n--- Iteration {i+1} ---")
        
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=tools,
            tool_choice="auto"
        )
        
        message = response.choices[0].message
        
        # Check if done (no tool calls)
        if not message.tool_calls:
            print(f"Agent response: {message.content}")
            return message.content
        
        # Execute tool calls
        messages.append(message)
        
        for tool_call in message.tool_calls:
            func_name = tool_call.function.name
            func_args = json.loads(tool_call.function.arguments)
            
            print(f"Calling: {func_name}({func_args})")
            result = execute_tool(func_name, func_args)
            print(f"Result: {result}")
            
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": result
            })
    
    return "Max iterations reached"

# Example
run_agent("What is 15% of the current US minimum wage times 40 hours?")

Agent Architectures That Work

1. Single Agent with Tools

The simplest pattern. One LLM with access to tools.

Good for: Focused tasks—research, data analysis, coding assistants

Limitations: Can get stuck, may not handle very complex multi-domain tasks

2. Multi-Agent Systems

Multiple specialized agents that collaborate.

# Multi-agent pattern - specialist agents
class ResearchAgent:
    """Agent specialized in information gathering."""
    
    system_prompt = """You are a research specialist. 
    Search for information and summarize findings.
    Be thorough but concise."""
    
    tools = [search_tool, scrape_tool]

class AnalystAgent:
    """Agent specialized in data analysis."""
    
    system_prompt = """You are a data analyst.
    Analyze data, find patterns, create insights.
    Be quantitative and precise."""
    
    tools = [calculate_tool, chart_tool]

class WriterAgent:
    """Agent specialized in content creation."""
    
    system_prompt = """You are a technical writer.
    Create clear, engaging content from research and analysis.
    Structure information logically."""
    
    tools = [format_tool]

class OrchestratorAgent:
    """Coordinates other agents."""
    
    def execute_task(self, goal: str):
        # 1. Research phase
        research_results = self.research_agent.run(
            f"Research: {goal}"
        )
        
        # 2. Analysis phase
        analysis = self.analyst_agent.run(
            f"Analyze this research: {research_results}"
        )
        
        # 3. Writing phase
        final_output = self.writer_agent.run(
            f"Create report from: {analysis}"
        )
        
        return final_output

3. Hierarchical Agents

A manager agent delegates to worker agents.

                    ┌──────────────────┐
                    │  Manager Agent   │
                    │  (Plans & Routes)│
                    └────────┬─────────┘
                             │
          ┌──────────────────┼──────────────────┐
          ↓                  ↓                  ↓
    ┌───────────┐     ┌───────────┐     ┌───────────┐
    │  Worker 1 │     │  Worker 2 │     │  Worker 3 │
    │ (Research)│     │ (Coding)  │     │ (Writing) │
    └───────────┘     └───────────┘     └───────────┘

Planning Strategies

Plan-and-Execute

# Plan-and-Execute: Create full plan first, then execute
def plan_and_execute(goal: str):
    """First plan all steps, then execute them."""
    
    # Planning phase
    plan_response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": """Create a detailed plan to accomplish the goal.
                Return as JSON: {"steps": ["step1", "step2", ...]}"""
            },
            {"role": "user", "content": f"Goal: {goal}"}
        ],
        response_format={"type": "json_object"}
    )
    
    plan = json.loads(plan_response.choices[0].message.content)
    print(f"Plan: {plan['steps']}")
    
    # Execution phase
    results = []
    for step in plan["steps"]:
        result = execute_step(step, results)
        results.append({"step": step, "result": result})
        print(f"Completed: {step}")
    
    return results

ReWOO: Reasoning Without Observation

# ReWOO - Plan with placeholders, execute in batch
def rewoo_agent(goal: str):
    """
    ReWOO pattern: 
    1. Create full plan with variable placeholders
    2. Execute all steps
    3. Substitute results
    """
    
    plan_prompt = f"""
    Goal: {goal}
    
    Create a plan using these tools: search, calculate, summarize
    Use #E1, #E2, etc. as placeholders for results.
    
    Format:
    Plan: [description]
    #E1 = tool[input]
    #E2 = tool[input using #E1 if needed]
    ...
    """
    
    # Get plan with placeholders
    plan = get_plan(plan_prompt)
    
    # Execute all tool calls
    evidence = {}
    for step in plan.steps:
        # Replace any placeholders with actual evidence
        resolved_input = resolve_placeholders(step.input, evidence)
        result = execute_tool(step.tool, resolved_input)
        evidence[step.var] = result
    
    # Final synthesis with all evidence
    return synthesize(goal, evidence)

Tool Design Principles

Good tools make good agents. Here’s what I’ve learned:

Tool Design Best Practices

• Single responsibility: One tool, one job. “search_web” not “search_and_summarize”
• Clear descriptions: Models choose tools based on descriptions. Be precise.
• Structured outputs: Return structured data the model can parse
• Error handling: Return meaningful errors, not stack traces
• Idempotency: Same input → same output (where possible)
• Bounds: Limit what tools can do. Don’t give agents unlimited filesystem access.

# Well-designed tool example
def create_tool_definition():
    return {
        "type": "function",
        "function": {
            "name": "query_database",
            "description": """Execute a read-only SQL query against the analytics database.
            Only SELECT queries are allowed. Returns up to 100 rows.
            Tables: users, orders, products, events.""",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "SQL SELECT query"
                    },
                    "limit": {
                        "type": "integer",
                        "description": "Max rows to return (default 100, max 1000)"
                    }
                },
                "required": ["query"]
            }
        }
    }

def query_database(query: str, limit: int = 100) -> dict:
    """Execute query with safety checks."""
    
    # Safety checks
    query_upper = query.upper().strip()
    if not query_upper.startswith("SELECT"):
        return {"error": "Only SELECT queries allowed", "success": False}
    
    forbidden = ["DROP", "DELETE", "UPDATE", "INSERT", "ALTER"]
    for word in forbidden:
        if word in query_upper:
            return {"error": f"{word} not allowed", "success": False}
    
    # Execute with limits
    try:
        limit = min(limit, 1000)
        results = db.execute(f"{query} LIMIT {limit}")
        return {
            "success": True,
            "row_count": len(results),
            "data": results
        }
    except Exception as e:
        return {"error": str(e), "success": False}

Memory Systems

Agents need memory to work on complex tasks over time.

# Memory-augmented agent
class MemoryAgent:
    def __init__(self):
        self.working_memory = []  # Current conversation
        self.vector_store = ChromaDB()  # Long-term memory
        
    def remember(self, content: str, metadata: dict):
        """Store in long-term memory."""
        self.vector_store.add(content, metadata)
    
    def recall(self, query: str, k: int = 5) -> list:
        """Retrieve relevant memories."""
        return self.vector_store.search(query, k)
    
    def run(self, user_input: str):
        # 1. Recall relevant memories
        memories = self.recall(user_input)
        memory_context = "\n".join([m.content for m in memories])
        
        # 2. Build context with memories
        messages = [
            {"role": "system", "content": f"Relevant context:\n{memory_context}"},
            *self.working_memory,
            {"role": "user", "content": user_input}
        ]
        
        # 3. Generate response
        response = self.llm.generate(messages)
        
        # 4. Update memories
        self.working_memory.append({"role": "user", "content": user_input})
        self.working_memory.append({"role": "assistant", "content": response})
        
        # 5. Optionally persist important info
        if self.is_important(response):
            self.remember(response, {"type": "interaction", "timestamp": now()})
        
        return response

Current Limitations (Honest Assessment)

Where Agents Still Struggle

• Long-horizon tasks: Error accumulates. 10+ step plans often fail.
• Recovery: When stuck, agents often loop rather than backtrack.
• Cost: Agent loops can be expensive—many LLM calls per task.
• Latency: Sequential tool use means slow overall execution.
• Reliability: Non-deterministic. Same task may succeed or fail.
• Safety: Autonomous actions require careful sandboxing.

What Actually Works in Production

Based on what I’ve deployed:

1. Constrained agents: Limited tool sets, focused domains, clear boundaries
2. Human-in-the-loop: Agent proposes, human approves critical actions
3. Hybrid approaches: Deterministic workflows with agentic components
4. Short chains: 3-5 steps max without human checkpoint
5. Specialized agents: One agent per domain, not one agent for everything

Key Takeaways

• Agentic AI adds autonomy—agents plan, act, and adapt
• ReAct is the foundational pattern: reason, act, observe, repeat
• Tool design is crucial—clear, bounded, well-described tools
• Memory enables complex, multi-session tasks
• Start constrained—expand agent capabilities gradually
• Expect failures—build in human oversight for critical actions

What’s Next

In Part 5, we’ll get hands-on and build a complete AI agent—a code review assistant that can analyze PRs, suggest improvements, and explain its reasoning. Real code, real patterns.

References & Further Reading

• ReAct Paper – ReAct: Synergizing Reasoning and Acting in LLMs
• AutoGPT – github.com/AutoGPT
• OpenAI Function Calling – platform.openai.com
• LangChain Agents – python.langchain.com
• The Landscape of Emerging AI Agent Architectures – arxiv.org

Building agents? I’d love to hear about your architecture choices. Connect on GitHub or comment below.