AI Agents Unplugged: For Real, No Magic, No Fuss

AI Agents Unplugged: Building Real Agents Without the Hype

Intro

If you’ve been following the AI space lately, you’ve probably heard the word “agents” thrown around everywhere. AI agents this, autonomous agents that—it’s easy to get lost in the hype. But what exactly are AI agents, and more importantly, how can you actually build one yourself?

In this post, I’m going to demystify AI agents by showing you how to build real, working agents using tools like LangChain, LangFlow, and the emerging Model Context Protocol (MCP). No magic, no hand-waving—just practical code and honest insights about what works and what doesn’t.

This is based on a live demo I recently gave, where I built agents from scratch in real-time. If you prefer video, check out the recording on my YouTube channel. But if you’re like me and prefer to read, follow along with code examples, and take your time—you’re in the right place!

What Are AI Agents Anyway?

Let’s start with the basics. You might be thinking: “Aren’t agents just fancy chatbots?” Not quite.

Agents vs. Prompts

Think of it this way:

Prompts are like giving someone a single instruction:

• “Tell me about the weather in Paris.”
• “Write me a poem about cats.”
• “Translate this text to Spanish.”

They’re one-shot interactions. You ask, the AI responds, done.

Agents, on the other hand, are autonomous workers that can:

• Plan a sequence of steps to accomplish a goal
• Use tools (like calculators, databases, APIs, or even code interpreters)
• Observe outcomes and self-correct when things go wrong
• Iterate until they complete the task

Here’s a real-world analogy: A prompt is like asking someone for directions. An agent is like giving someone a destination and watching them figure out the route, deal with traffic, take detours, and actually get there.

A Simple Example

Let’s say you ask: “What’s the GDP of France divided by its population, and how does that compare to Germany?”

With a prompt, the AI might give you an educated guess or outdated information.

With an agent, here’s what happens:

1. The agent realizes it needs current data
2. It searches the web for France’s GDP and population
3. It does the same for Germany
4. It uses a calculator tool to do the division
5. It compares the results and gives you a well-reasoned answer

See the difference? The agent plans, acts, and uses tools to get you a reliable answer.

The Agentic Landscape: A Crowded Space

Before we dive into building agents, let’s acknowledge the elephant in the room: there are a lot of agent frameworks out there.

The crowded landscape of agent frameworks—and these are just the popular ones!

You’ve got LangChain, LangGraph, AutoGen, CrewAI, Agent Zero, LlamaIndex Agents, Semantic Kernel, and dozens more. It’s overwhelming!

For this tutorial, I’m focusing on the LangChain ecosystem because:

1. It’s mature and battle-tested
2. It has great documentation and community support
3. It plays well with other tools
4. It’s actively maintained

But the concepts you’ll learn apply to most agent frameworks.

Common Agent Patterns

There are three main patterns you’ll see when building agents. Let’s break them down:

1. ReAct: The Simple Loop

ReAct stands for Reasoning + Acting. It’s the simplest and most common agent pattern.

Here’s how it works:

1. Reason: "I need to find X. The best tool for this is Y."
2. Act: Use tool Y
3. Observe: Look at the result
4. Reason: "Based on this result, my next step is Z."
5. Act: Use tool Z
6. Observe: Look at the result
7. Repeat until done

Best for: Simple tasks with a few tools, like answering questions that require 1-3 tool calls.

Limitations: Can get stuck in loops, doesn’t plan ahead, can be inefficient for complex tasks.

Guardrails you need:

• Maximum step limits (to prevent infinite loops)
• Token budgets (to control costs)
• Error handling for parsing failures

2. Planner/Executor: Think First, Act Later

With this pattern, the agent creates a complete plan before executing anything.

1. Planning Phase: "To answer this, I need to: (a) Get data from API, (b) Process it, (c) Store results"
2. Execution Phase: Execute step (a), then (b), then (c)
3. Review Phase: "Did it work? If not, revise the plan."

Best for: Complex workflows where you want to see the plan before execution, or where steps depend on each other.

Limitations: Slower, more expensive (planning requires LLM calls), can fail if the plan is wrong.

Use cases: Multi-step data processing, research tasks, content generation pipelines.

3. Graph/Multi-Agent: The State Machine

This is the most flexible pattern. You define a graph where:

• Nodes represent skills or agents
• Edges represent routing logic
• State flows through the graph

graph LR A[START] --> B{route question} B -->|Contains math/CHF| C[calc_node] B -->|Otherwise| D[retrieve_node] C --> E[answer_node] D --> E E --> H[human_review] H --> I{route_feedback} I -->|reject| D I -->|accept| F[END] %% Styling (optional) classDef calc fill:#f9f,stroke:#333,stroke-width:2px,color:#000 classDef retv fill:#ccf,stroke:#333,stroke-width:2px,color:#000 classDef ans fill:#cfc,stroke:#333,stroke-width:2px,color:#000 classDef human fill:#ffc,stroke:#333,stroke-width:2px,color:#000 class C calc class D retv class E ans class H human

Best for: Complex workflows with conditional logic, human-in-the-loop, or multi-agent collaboration.

Why it’s powerful:

• Easy to test individual nodes
• Observable (you can see exactly where the agent is)
• Version-controlled (the graph is just code)
• Supports human review and interrupts

Tool to use: LangGraph (we’ll cover this later)

LangChain vs. LangFlow: Code or Visual?

One question I get a lot: “Should I use LangChain or LangFlow?”

The answer is: both, but at different stages.

Feature	LangChain	LangFlow
Paradigm	Code-first (Python/JavaScript)	Visual (Drag & Drop)
Best for	Production, testing, CI/CD	Prototyping, demos, collaboration
Core Concept	LCEL (LangChain Expression Language)	Visual DAGs, REST APIs
Output	Services, libraries, containers	Exportable JSON flows
Version Control	Git-friendly	JSON files (can be versioned)
Debugging	Full control, breakpoints	Visual flow inspection

My Recommended Workflow

1. Start with LangFlow for rapid prototyping
   • Drag and drop components
   • Test different configurations quickly
   • Share visual flows with non-technical stakeholders
   • Export to JSON when you’re happy
2. Move to LangChain for production
   • Convert your flow to code
   • Add proper error handling
   • Write unit tests
   • Set up CI/CD
   • Add observability (LangSmith)

Pro tip: You can also call LangFlow flows from LangChain via REST API. Best of both worlds!

LangChain vs. LangGraph: When to Use Each

This is another common source of confusion. Here’s the quick guide:

LangChain is for:

• Simple chains: prompt → LLM → output
• Basic tool usage
• Quick prototypes
• Linear workflows

LangGraph is for:

• Complex routing logic
• Stateful conversations
• Human-in-the-loop workflows
• Multi-agent systems
• Cycles and loops (agents that can retry)

LangChain for linear flows, LangGraph for complex state machines

Think of LangChain as your Swiss Army knife for simple tasks, and LangGraph as your power tool for building robust, production-grade agent systems.

Enter MCP: The Future of Agent Security

Now let’s talk about something really important: security.

When you give an agent access to tools—especially tools that can read files, execute code, or make API calls—you need a way to control what it can and cannot do.

That’s where Model Context Protocol (MCP) comes in.

What Is MCP?

MCP is a proposed standard (started by Anthropic) for agents to safely discover and use tools. Think of it as an “API layer” for LLMs.

Here’s how it works:

1. Discovery: An agent requests a manifest of available tools
2. Authentication: The agent gets credentials (if authorized)
3. Invocation: The agent calls tools through the MCP server
4. Auditing: All calls are logged for security review

Why MCP Matters

Without MCP, your agent might have access to everything. With MCP:

• ✅ Role-based access control (RBAC): Different agents can have different permissions
• ✅ Observable: You can see exactly what tools were called and by whom
• ✅ Secure: Credentials are never exposed to the LLM
• ✅ Interoperable: Other tools and agents can use the same MCP servers

MCP in Action

Here’s a simple example of an MCP server that provides a “safe” calculator tool:

# mcp_safe_server.py
from mcp import MCPServer, Tool

server = MCPServer()

@server.tool("calculator")
def safe_calculator(expression: str) -> float:
    """
    A calculator that only allows basic math operations.
    No eval(), no code execution—just safe math.
    """
    # Validate input
    allowed_chars = "0123456789+-*/()."
    if not all(c in allowed_chars or c.isspace() for c in expression):
        raise ValueError("Invalid characters in expression")
    
    # Use a safe evaluation method
    try:
        result = eval(expression, {"__builtins__": {}}, {})
        return float(result)
    except Exception as e:
        raise ValueError(f"Invalid expression: {e}")

if __name__ == "__main__":
    server.run()

Then in your agent code, you connect to the MCP server instead of giving the agent direct access to dangerous functions.

Risks and Guardrails: The Reality Check

The MCP ecosystem is growing fast—check out these community servers!

Let’s talk honestly about the risks of AI agents. This is the part that marketing materials often skip, but it’s critical if you want to deploy agents in production.

Risk	What Can Go Wrong	How to Mitigate
Prompt Injection	A user tricks the agent into doing something it shouldn’t	Input validation, sandboxing, separate system prompts
Data Leakage	Agent accidentally exposes sensitive data	RBAC, MCP, careful prompt design, filter outputs
Infinite Loops	Agent gets stuck repeating the same actions	Step limits (max 10-20 iterations), timeouts
Runaway Costs	Agent makes hundreds of expensive API calls	Token budgets, circuit breakers, cost alerts
Hallucinations	Agent makes up facts or tool results	Grounding with retrieval, fact-checking, citations
Tool Abuse	Agent uses tools in unintended ways	Allowlists, rate limiting, audit logs

Real Example: The Loop That Wouldn’t Stop

I once built an agent that was supposed to search for information and summarize it. Sounds simple, right?

What happened: The agent decided the first search result wasn’t good enough. So it searched again. And again. And again. It burned through my API quota in 10 minutes searching for increasingly obscure variations of the same query.

The fix: I added a hard limit of 5 tool calls per conversation. Problem solved.

The lesson: Guardrails are not optional. Build them in from day one.

Hands-On: Building Your First Agent

Alright, enough theory. Let’s build something!

Prerequisites

Before we start, make sure you have:

• Python 3.8 or higher (I recommend 3.10+)
• An OpenAI API key (or you can use Ollama for local models—see my previous post on running LLMs at home)
• Basic familiarity with Python and virtual environments

If you want to follow along with the complete code, check out my GitHub repo: github.com/zioalex/Agents_unplugged_for_real_no_magic_no_fuss

Installation

Let’s set up our environment:

# Create a virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install required packages
pip install langchain langchain-openai langchain-community langflow

# If using Ollama (local models)
# Follow my guide at: https://ai4you.sh/posts/videoblog/My-own-LLM-at-home/

Configuration

Create a config.json file:

{
  "OPENAI_API_BASE": "https://api.openai.com/v1",
  "API_KEY": "your-api-key-here",
  "LANGFLOW_API_KEY": "optional-langflow-key"
}

Security note: Never commit API keys to Git! Add config.json to your .gitignore.

Demo 1: A Simple ReAct Agent with Real-World Use Case

Let’s build a practical agent that helps answer insurance policy questions. We’ll use:

• A search tool to find relevant policy documents
• A calculator tool for safe arithmetic (no eval() risks!)

First, let’s set up our tools and tiny policy corpus:

import json, os, re, ast, operator

# Tiny policy corpus
POLICIES = [
    {"id": "TravelPlus-2024", "text": "TravelPlus provides coverage for business travel including delayed flights, lost baggage, and emergency medical expenses up to CHF 10,000. Personal electronics are covered if they are lost due to theft, with a deductible of CHF 200. Pure misplacement is not covered."},
    {"id": "DeviceCare-Pro", "text": "DeviceCare-Pro covers accidental damage to smartphones and laptops used for work. Loss or theft is covered only when a police report is filed within 48 hours. Maximum payout CHF 1,500 per device, 2 incidents per policy year."},
    {"id": "FleetAssist", "text": "FleetAssist covers rented vehicles during company travel. It excludes personal property inside the vehicle unless explicitly endorsed."},
]

def simple_search(query: str, top_k: int = 3):
    """Simple keyword-based search"""
    terms = [t.lower() for t in re.findall(r"\w+", query)]
    scored = []
    for doc in POLICIES:
        text = doc["text"].lower()
        score = sum(text.count(t) for t in set(terms))
        if score > 0:
            scored.append((score, doc))
    scored.sort(key=lambda x: x[0], reverse=True)
    return [doc for _, doc in scored[:top_k]]

def search_policies(query: str) -> str:
    """Search policies and return JSON"""
    hits = simple_search(query, top_k=3)
    return json.dumps({"results": [{"id": h["id"], "snippet": h["text"][:280]} for h in hits]})

# Safe calculator using AST (no eval!)
OPS = {
    ast.Add: operator.add, ast.Sub: operator.sub, 
    ast.Mult: operator.mul, ast.Div: operator.truediv,
    ast.Pow: operator.pow, ast.USub: operator.neg, 
    ast.Mod: operator.mod, ast.FloorDiv: operator.floordiv
}

def _eval(node):
    if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
        return node.value
    if isinstance(node, ast.BinOp):
        return OPS[type(node.op)](_eval(node.left), _eval(node.right))
    if isinstance(node, ast.UnaryOp):
        return OPS[type(node.op)](_eval(node.operand))
    raise ValueError("Unsupported expression")

def calculator(expression: str) -> str:
    """Safe calculator - no code execution risks"""
    try:
        node = ast.parse(expression, mode='eval').body
        return str(_eval(node))
    except Exception as e:
        return f"Error: {e}"

Now let’s create our ReAct agent:

from langchain.tools import Tool
from langchain.agents import initialize_agent, AgentType
from langchain_openai import ChatOpenAI

# Initialize LLM
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.2)

# Define tools
tools = [
    Tool(
        name="search_policies", 
        func=search_policies, 
        description="Search a tiny policy corpus. Returns JSON."
    ),
    Tool(
        name="calculator", 
        func=calculator, 
        description="Evaluate arithmetic like '2*(1500-200)'. Returns a number as text."
    )
]

# Build the agent (ReAct pattern)
agent = initialize_agent(
    tools=tools, 
    llm=llm, 
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True, 
    handle_parsing_errors=True
)

# Test questions
demo_questions = [
    "My work smartphone was stolen on a business trip. Is it covered, and what are the conditions?",
    "If a laptop worth CHF 1,800 is accidentally damaged twice in a year under DeviceCare-Pro, what's the max total payout after any deductibles?",
    "Does FleetAssist cover personal belongings inside a rental car?"
]

# Run the agent
for question in demo_questions:
    result = agent.invoke(question)
    print(f"\nQ: {question}")
    print(f"A: {result['output']}")

What’s happening here?

1. The agent receives the question
2. It reasons: “I need policy information about smartphone theft”
3. It uses the search_policies tool to find relevant documents
4. For the calculator question, it extracts the math expression and uses the calculator tool
5. It combines the results and formats a clear answer

Important: Notice we’re using AST parsing instead of eval() for the calculator. This prevents code injection attacks!

Demo 2: Calling a LangFlow Flow

Now let’s integrate with LangFlow. Say you’ve built a complex flow visually in LangFlow and want to call it from your code.

First, get your flow ID from LangFlow and set up your API key. Here’s how to call it:

# demo_2_langflow_integration.py
import requests
import json

LANGFLOW_API_KEY = os.environ.get("LANGFLOW_API_KEY")

def run_langflow(flow_id: str, user_input: str, base_url: str = "http://127.0.0.1:7860/api/v1"):
    """Calls a LangFlow flow via REST API."""
    url = f"{base_url}/run/{flow_id}"
    payload = {
        "input_value": user_input,
        "output_type": "chat",
        "input_type": "chat"
    }
    headers = {
        "x-api-key": LANGFLOW_API_KEY
    }
    
    r = requests.post(url, json=payload, headers=headers, timeout=60)
    r.raise_for_status()
    data = r.json()
    
    try:
        # Extract the message text from the nested response
        return data["outputs"][0]["outputs"][0]["results"]["message"]["text"]
    except Exception:
        # If structure is different, return the full JSON
        return json.dumps(data, indent=2)

# Example usage
result = run_langflow(
    "55e5414b-966a-48dc-8bc4-bfb2e3168c72",
    "Hello from the notebook! Tell me what I can do with this integration between LangChain and LangFlow"
)
print(result)

Why is this powerful?

Your non-technical team can design flows visually in LangFlow, and your engineers can integrate them into production code with just a REST call. Best of both worlds!

Demo 3: Securing Tools with MCP

Finally, let’s add MCP for secure tool access. Here’s a real MCP server using FastMCP with proper security guardrails:

# safe_mcp_server.py
from __future__ import annotations
from mcp.server.fastmcp import FastMCP
import re, ast, operator, json

# Safe calculator implementation (same as before)
OPS = {
    ast.Add: operator.add, ast.Sub: operator.sub,
    ast.Mult: operator.mul, ast.Div: operator.truediv,
    ast.Pow: operator.pow, ast.USub: operator.neg,
    ast.Mod: operator.mod, ast.FloorDiv: operator.floordiv
}

def _eval(node):
    if isinstance(node, ast.Constant): return node.value
    if isinstance(node, ast.BinOp): 
        return OPS[type(node.op)](_eval(node.left), _eval(node.right))
    if isinstance(node, ast.UnaryOp): 
        return OPS[type(node.op)](_eval(node.operand))
    raise ValueError("Unsupported expression")

def safe_calculator(expression: str) -> str:
    node = ast.parse(expression, mode='eval').body
    return str(_eval(node))

# Policy corpus (same as before)
POLICIES = [
    {"id": "TravelPlus-2024", "text": "TravelPlus provides coverage for business travel including delayed flights, lost baggage, and emergency medical expenses up to CHF 10,000. Personal electronics are covered if they are lost due to theft, with a deductible of CHF 200. Pure misplacement is not covered."},
    {"id": "DeviceCare-Pro", "text": "DeviceCare-Pro covers accidental damage to smartphones and laptops used for work. Loss or theft is covered only when a police report is filed within 48 hours. Maximum payout CHF 1,500 per device, 2 incidents per policy year."},
    {"id": "FleetAssist", "text": "FleetAssist covers rented vehicles during company travel. It excludes personal property inside the vehicle unless explicitly endorsed."},
]

def simple_search(query: str, top_k: int = 3):
    terms = [t.lower() for t in re.findall(r"\w+", query)]
    scored = []
    for doc in POLICIES:
        text = doc["text"].lower()
        score = sum(text.count(t) for t in set(terms))
        if score > 0:
            scored.append((score, doc))
    scored.sort(key=lambda x: x[0], reverse=True)
    return [doc for _, doc in scored[:top_k]]

# Initialize MCP server
mcp = FastMCP("SR-Policies", stateless_http=True, host="127.0.0.1", port=3001)

# Security constraints
MAX_Q = 200  # Max query length
BANNED = re.compile(r"(?i)(rm\s|-rf|\bimport\b|__|eval\(|exec\()")  # Block dangerous patterns

@mcp.tool()
def secure_search_policies(query: str) -> dict:
    """Search policies with input validation"""
    if not query or len(query) > MAX_Q or BANNED.search(query or ""):
        return {"error": "query_rejected"}
    hits = simple_search(query, top_k=3)
    return {"results": [{"id": h["id"], "snippet": h["text"][:280]} for h in hits]}

@mcp.tool()
def safe_calc(expression: str) -> str:
    """Safe calculator with input validation"""
    if len(expression or "") > 100 or BANNED.search(expression or ""):
        return "error: expression_rejected"
    try:
        return safe_calculator(expression)
    except Exception as e:
        return f"error: {e}"

if __name__ == "__main__":
    mcp.run(transport="streamable-http")

What’s different here?

1. Input validation: We check query length and ban dangerous patterns like import, eval(), exec()
2. Sandboxed execution: The calculator uses AST parsing—no arbitrary code execution
3. Observable: All tool calls go through the MCP server and can be logged
4. Stateless HTTP: Tools are exposed via HTTP, making them accessible to any agent

Instead of the agent executing code directly on your machine, it goes through the MCP server. The server validates inputs, logs all calls, and enforces access controls.

Running the server:

python safe_mcp_server.py

Now your agent can connect to http://127.0.0.1:3001 and use these tools safely!

Prompt Engineering: The Secret Sauce

Here’s something they don’t tell you: 90% of agent quality comes from prompt engineering, not the framework you choose.

A well-crafted prompt can make a simple ReAct agent outperform a complex multi-agent system with a bad prompt.

Common prompting techniques—Chain of Thought and ReAct are the foundations

Key Techniques

1. Zero-Shot: Just ask, no examples
   • Good for: Simple, well-defined tasks
   • Example: “Calculate 25 * 17”
2. Few-Shot: Provide examples
   • Good for: Tasks with specific format requirements
   • Example: Show 2-3 examples of good summaries before asking for a summary
3. Chain-of-Thought (CoT): Ask the model to “think step by step”
   • Good for: Math, reasoning, multi-step tasks
   • Example: “Let’s solve this step by step: First, …”
4. ReAct: Combine reasoning with actions
   • Good for: Agent workflows
   • Example: “Thought: I need to… Action: [tool] Observation: [result]”

My Prompt Template

Here’s a prompt template I use for most agents:

AGENT_PROMPT = """You are a helpful AI agent with access to tools.

Your task: {task}

Instructions:
1. Break down the task into clear steps
2. Use tools when necessary—don't guess!
3. If a tool returns an error, try a different approach
4. Always explain your reasoning
5. Format your final answer clearly

Available tools:
{tools}

Let's solve this step by step.
"""

Why this works:

• Clear task definition
• Explicit instructions to use tools
• Error handling guidance
• Encourages explanation (helpful for debugging)

For more prompt engineering techniques, check out promptingguide.ai.

LangSmith: Your Agent Observatory

Once your agent is working, you need to observe it in production. That’s where LangSmith comes in.

LangSmith gives you X-ray vision into your agent’s decision-making

What LangSmith Does

• Trace every step: See every LLM call, tool invocation, and decision
• Debug failures: Understand why an agent failed or got stuck
• Optimize costs: Identify expensive calls and optimize prompts
• Monitor quality: Track output quality over time

Quick Setup

import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "your-langsmith-key"
os.environ["LANGCHAIN_PROJECT"] = "my-agent-project"

# Now all LangChain calls are automatically traced!

Pro tip: Use different projects for dev, staging, and production so you can compare metrics.

Deprecation Alert: Stay Updated!

One important note: The AI agent space is evolving rapidly. What works today might be deprecated tomorrow.

Real deprecation notice from LangChain—things change fast!

My advice:

1. Pin your dependency versions in production
2. Subscribe to changelogs and release notes
3. Test upgrades in a separate environment first
4. Don’t chase every new feature—stability matters

When I started writing this post, I used AgentExecutor. By the time I finished, LangChain recommended create_react_agent instead. That’s the pace of change we’re dealing with!

Common Pitfalls (And How to Avoid Them)

Let me save you some headaches by sharing mistakes I’ve made:

1. Over-Engineering Too Soon

Mistake: I built a complex multi-agent graph system for a simple Q&A bot.

Lesson: Start with ReAct. Only add complexity when you hit limitations.

2. No Token Budgets

Mistake: I let an agent run wild and got a $200 bill.

Lesson: Set max_tokens and max_iterations from day one.

3. Trusting LLM Output Blindly

Mistake: I assumed the agent would always format output correctly.

Lesson: Always validate and parse agent outputs. They will surprise you.

4. Ignoring Error Handling

Mistake: My agent crashed on the first tool error.

Lesson: Wrap every tool call in try-except and teach the agent to handle failures gracefully.

5. No Logging

Mistake: I had no idea what my agent was doing in production.

Lesson: Log everything—inputs, outputs, tool calls, errors. LangSmith helps here.

Real-World Use Cases

Let’s ground this in reality. Here are agents I’ve actually built or seen in production:

Customer Support Agent

• Tools: Knowledge base search, ticket creation, sentiment analysis
• Pattern: ReAct with human escalation
• Success metric: 60% of tickets resolved without human intervention

Code Review Agent

• Tools: Git diff reader, linter, test runner, documentation search
• Pattern: Planner/Executor
• Success metric: Catches 80% of style issues before human review

Research Agent

• Tools: Web search, PDF reader, note taker, citation formatter
• Pattern: Graph with human checkpoints
• Success metric: Reduces research time by 50%

Data Pipeline Agent

• Tools: SQL query executor, data validator, Slack notifier
• Pattern: LangGraph with error recovery
• Success metric: 95% of daily reports automated

What’s Next? Advanced Topics

We’ve covered a lot, but there’s always more to learn! Here are topics for future posts:

1. Fine-tuning agents for specific domains
2. Multi-agent collaboration (agents that work together)
3. Memory systems (giving agents long-term memory)
4. Evaluation frameworks (how to measure agent performance)
5. Deployment strategies (Docker, Kubernetes, serverless)

Let me know in the comments what you’d like to see next!

Closing Thoughts

Let’s cut through the hype: Agents are not intelligent. They’re not going to replace software engineers (yet). They’re not magic.

What they are:

• Stateful programs that use LLMs for decision-making
• Tool-using systems that can accomplish complex tasks
• The next layer of abstraction in software development

Think of agents as a new kind of API—one that takes natural language as input and orchestrates multiple services to accomplish a goal.

My recommendations:

1. Start simple with ReAct agents
2. Add complexity only when needed (graphs, multi-agent)
3. Observability and guardrails are NOT optional
4. Iterate based on real usage, not theory

Agents are powerful, but they require careful engineering. Treat them like any other production system: test thoroughly, monitor closely, and always have a human override.

Try It Yourself

All the code from this post is available on GitHub: 🔗 github.com/zioalex/Agents_unplugged_for_real_no_magic_no_fuss

Clone it, run the notebooks, break things, and learn!

git clone https://github.com/zioalex/Agents_unplugged_for_real_no_magic_no_fuss.git
cd Agents_unplugged_for_real_no_magic_no_fuss
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# Update config.json with your API keys
# Then run the notebooks!

Links & Resources

• Prompting Guide: promptingguide.ai/techniques - Comprehensive guide to all prompting techniques
• LangChain Docs: python.langchain.com - Official documentation
• LangFlow: langflow.org - Visual agent builder
• LangSmith: smith.langchain.com - Agent observability platform
• MCP Spec: modelcontextprotocol.io - Model Context Protocol documentation
• My Previous Post: Running LLMs at Home with Ollama
• Thoughtful Critique: AI and Anti-Intelligence - A balanced perspective on AI limitations

What Does It Mean?

ReAct

Reasoning + Acting. An agent pattern where the agent alternates between thinking about what to do and actually doing it, observing the results after each action.

LCEL

LangChain Expression Language. A declarative way to compose LangChain components, similar to how you chain Unix commands with pipes.

MCP

Model Context Protocol. A proposed standard for secure agent-to-tool communication, similar to how OAuth works for API authorization.

Planner/Executor

An agent pattern where planning and execution are separate phases. The agent first creates a complete plan, then executes it step by step.

Graph Agent

An agent built as a state machine, where nodes represent skills and edges represent routing logic. More complex but more controllable than simple loops.

Token Budget

A limit on how many tokens (words/chunks) an agent can use. Essential for controlling costs when using paid APIs.

Hallucination

When an LLM generates plausible-sounding but false information. Agents can hallucinate tool outputs or facts they don’t actually know.

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Happy agent building! 🤖

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