Your Simple Guide to How AI Really Works
It feels like Artificial Intelligence (AI) is everywhere, doesn't it? It exploded into mainstream conversation around November 2022 with the launch of ChatGPT 1. Suddenly, millions of people were interacting with AI that could write emails, poems, and even computer code. ChatGPT reached an amazing 100 million users in just two months 2, faster than almost any technology before it. Now we hear about other powerful AI models too, like Google's Gemini and Anthropic's Claude.
But alongside the excitement, there's also a lot of uncertainty and concern. A 2024 study by the Pew Research Center 3 found a big difference in how experts and the public see AI:
- Only 11% of US adults said they were more excited than concerned about AI, compared to 47% of AI experts.
- Over half the public (51%) said they were more concerned than excited, versus just 15% of experts.
- Many people worry AI will harm them personally (43%) rather than benefit them (24%).
Concerns about jobs, inaccurate information, bias, and losing control are common. So, what's really going on? Is AI just fancy software, or something else? How does it actually learn? And what are these "AI Agents" people are starting to talk about?
This post aims to break it all down in simple terms. We'll look under the hood of AI like ChatGPT, understand its limits, explore how AI Agents aim to go further, and address some of those common worries with clear explanations.
Part 1: How AI Learns to Talk
First, let's understand that AI like ChatGPT is very different from the software we normally use, like Microsoft Word or a calculator.
Traditional Software: Think of a calculator. You type 2 + 2, and it always gives you 4. It follows exact instructions written by programmers. Give it the same input, and you get the exact same output every time. It is predictable and based on fixed rules. We call this deterministic.
AI Language Models: Think more like how humans learn a language by listening and reading a lot. Large Language Models (LLMs) are not given exact grammar rules. Instead, they are shown huge amounts of text from the internet, books, and articles. By analyzing this data, they learn patterns and connections between words. When you ask a question, the Large Language Model (LLM) predicts the most likely sequence of words to form a good answer, based on the patterns it learned. This means it might give slightly different answers if asked the same question again. It works on probability and patterns, not fixed rules. We call this probabilistic.
How Does a Large Language Model Actually Learn?
Imagine a giant, complex network of connected points, like tiny digital brain cells. This is called a neural network. Learning happens in a few key stages:
- Pretraining (Building General Knowledge): The AI reads massive amounts of text. As it reads, it constantly tries to predict the next word in sentences. When it makes a guess, it checks if it was right. If wrong, it slightly adjusts its internal connections (like tuning billions of tiny knobs) to make a better prediction next time. This process of predicting and adjusting, repeated billions of times, helps the AI learn grammar, facts (from the text it read), context, and different writing styles.
- Finetuning (Getting Specific): After pretraining, the model might be trained more on specific types of text or tasks to make it better at things like answering questions accurately or following instructions carefully.
- Learning from Human Feedback (RLHF): To make the AI safer and more helpful, humans get involved. People look at different AI answers to the same question and rank them from best to worst. The AI then learns to produce answers more like the ones humans preferred. It is like giving a student helpful feedback.
Putting Knowledge to Use:
Once trained, when you give the AI a prompt (your question or instruction), it uses its tuned network to generate an answer, predicting the most likely words one after another. This process of using the trained model to make predictions is called inference.
Why This Matters for Understanding AI's Limits and Concerns:
Knowing how Large Language Models learn helps explain some of their strange behaviors and why people worry:
- Hallucinations (Making Things Up): Because the AI is predicting likely words, not looking things up in a perfect memory bank, it can sometimes create information that sounds good but is wrong. It connects words based on patterns, not always on facts. The Pew study found 66% of US adults and 70% of experts are highly concerned about people getting inaccurate information from AI.
- Knowledge Cutoff: The AI generally only "knows" about information included in its training data, which has an end date. It often doesn't know about very recent events unless specifically updated or connected to live information (which is where Agents come in!).
- Bias: The AI learns from text written by humans, and human writing contains biases. If the training data has unfair stereotypes or doesn't represent all groups equally, the AI can learn and repeat those biases. Both the public and experts worry about this. Pew found 55% of both groups are highly concerned about bias in AI decisions. Experts also noted that men's and White adults' perspectives are often seen as better represented in AI design than those of women or racial minorities.
Part 2: Introducing AI Agents That Do Things
So, LLMs are great with language, but they have limits. They usually cannot book your flight, manage your work schedule, or update company records by themselves. They are mostly stuck inside the chat window.
LLM Agents are designed to break out of that box. Think of an Agent as combining an LLM "brain" with the ability to use tools and take actions in the digital world, or even the physical world.
An agent can:
- Reason and Plan: Use its LLM brain to understand your goal and figure out the steps needed.
- Use Tools: Interact with other software, websites, or databases. This connection often happens using APIs (Application Programming Interfaces), which act like messengers letting different programs talk to each other. The agent's ability to ask these other programs for information or actions is often called "tool use" or "tool calling". This is the key that lets an agent act.
- Execute Tasks: Carry out the planned steps using those tools.
Where Do These Agents Operate?
AI agents are not just invisible code. They can work in different places:
- On the Web: A browser agent works inside your web browser (like Chrome or Edge) to automate tasks like filling forms, searching across websites, or clicking buttons for you.
- On Your Computer: A computer use agent can interact directly with your computer's operating system (like Windows or macOS) and desktop software, automating tasks involving files, folders, and different applications.
- On Your Phone: Mobile agents could potentially help manage apps, settings, and tasks directly on your smartphone.
- In the Physical World: When an AI agent controls a body, we usually call it a robot or a physical agent. Think of self driving cars, warehouse robots sorting packages, or drones inspecting bridges.
So, the core idea (reasoning, planning, using tools) is the same, but the agent might be clicking web buttons, controlling robot arms, or using "tool calling" to talk to another software system via an API.
Everyday Examples of LLM Agents at Work or School
Let's think about common situations where agents, using their tool calling abilities, could help:
- University Admissions Office: Instead of staff manually checking applications, an AI Agent could potentially: read application essays (using its LLM brain), use a "tool" to check if grades meet requirements in a database, draft personalized status update emails, and use a calendar "tool" to offer interview slots. It would handle routine steps, flagging complex cases for human review.
- Company IT Helpdesk: If your work printer isn't working, an AI Agent could: understand your problem description (LLM), use a "tool" to check a help database for solutions, guide you through troubleshooting steps, maybe use another "tool" to check the printer's status directly, and if needed, create a detailed support ticket for a human technician.
- Online Shopping Support: If your online order is late, an AI Agent could: understand your question (LLM), use a "tool" to check the real time shipping status via an API, check inventory with another "tool" if needed, and draft a specific, helpful reply with current information.
How LLM Agents Help Overcome LLM Weaknesses:
Agents tackle the core LLM limits:
- Hallucinations: By using tools to get real, factual data (like order status), agents rely less on just the LLM's potentially flawed memory.
- Knowledge Cutoff: Tools let agents access up to date information (like current prices or news).
- Inability to Act: "Tool use" allows agents to actually do things in other software (like booking a meeting or updating a customer record).
Part 3: Building LLM Agents
Creating an agent involves several layers of technology, like building blocks:
- Chips: The powerful computer hardware (especially GPUs from companies like NVIDIA) needed for AI calculations.
- Cloud: Services like AWS, Google Cloud, and Azure that let companies rent computing power online affordably.
- The Brain: Foundation Models (LLMs): This is where the language understanding and reasoning power comes from. Agents rely on powerful foundation models, typically accessed via APIs (Application Programming Interfaces). These are the large language models developed by major AI labs, such as OpenAI's GPT models, Google's Gemini, Anthropic's Claude models, Meta's Llama, as well as models from companies like Mistral, xAI (Grok), DeepSeek, and others. The agent uses one of these underlying models as its core reasoning engine.
- Orchestration: This is key for agents! Special software tools (like LangChain or CrewAI) act like a conductor, telling the LLM brain which tools (like APIs for other services) to use and in what order to complete a task. This manages the workflow and connects the brain to its tools.
- Application: The final app or service you actually interact with (the helpdesk chat window, the shopping assistant).
Part 4: Building an LLM Agent with Agno Framework
We have talked about the building blocks of an agent: the AI brain (LLM), tools it can use, and the "Orchestration" layer that connects everything. So how does that orchestration part actually work? Let's look at a simplified example using a real tool called Agno.
Think of Agno as a toolkit for developers that makes it easier to build agents. It helps connect an AI brain (like the specific models we mentioned earlier, such as GPT-4o) to capabilities like web search, memory, or specific knowledge.
Imagine I want to get details about one of my favorite Thai restaurants in San Francisco, Osha Thai Embarcadero. Let's say they're participating in San Francisco Restaurant Week (using example dates April 4th-13th, 2025). Often, for special events like this, restaurants put the unique menu in a PDF file on their website. This PDF contains details I want, but it's not always easy for standard AI or search engines to extract specific information from inside a PDF. This makes it a perfect test case!
We'll use the Agno toolkit and OpenAI's GPT-4o model to build agents with increasing capabilities.
Below is a simplified look at the code involved. Don't worry if you don't understand every line; the key ideas are explained in the text and analysis that follows.
Example 1: Basic LLM (No Tools or Knowledge)
First, let's just use the AI brain (GPT-4o) on its own, like a basic chatbot. We won't give it any web search tools or knowledge about those PDF menus yet.
# Example 1 Code: Setting up a basic agent with just an AI model
from agno.agent import Agent
from agno.models.openai import OpenAIChat
# Create the agent, telling it to use OpenAI's GPT-4o model.
agent_basic = Agent(
model=OpenAIChat(id="gpt-4o"),
markdown=True, # Format responses nicely
)
# Ask some questions about Osha Thai and the event.
print("Asking about Osha Thai participation and dates...")
agent_basic.print_response("Is Osha Thai Embarcadero participating in San Francisco Restaurant Week? If so, what are the dates?")
print("\nAsking about the location...")
agent_basic.print_response("Where is Osha Thai Embarcadero located in San Francisco?")
What Happened? (Looking at the Results)
Here's what this basic agent told us:
Question 1: "Is Osha Thai Embarcadero participating...? If so, what are the dates?"
Response: "I don't have real-time access to current events or live databases, but typically, many popular restaurants in San Francisco... I recommend checking the official website... or the restaurant's social media pages or website..."
- Analysis: This is interesting! The AI is smart enough to know that Restaurant Week participation is current information it probably doesn't have access to. Instead of making something up (hallucinating), it admits its limitation and suggests how we could find the answer. That's responsible AI behavior.
Question 2: "Where is Osha Thai Embarcadero located...?"
Response: "Osha Thai Embarcadero is located at 4 Embarcadero Center, San Francisco, CA 94111..."
- Analysis: This time, it gave a specific, correct answer. The restaurant's address is relatively fixed information that was likely part of the huge amount of text GPT-4o learned from during its training. It didn't need any special tools for this fact.
Takeaway: The basic AI brain has a lot of general knowledge but can't access live, up-to-the-minute information or details not in its training data.
Example 2: Giving the LLM Agent a Web Search Tool
Okay, our basic agent couldn't confirm the Restaurant Week details. Let's upgrade it by giving it a tool to search the web using DuckDuckGo.
# Example 2 Code: Adding a web search tool
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools # Import the tool
# Create the agent like before, but add the tool
agent_with_search = Agent(
model=OpenAIChat(id="gpt-4o"),
description="You are a helpful assistant providing information about San Francisco Restaurant Week.",
# *** The key change: Add the search tool! ***
tools=[DuckDuckGoTools()],
show_tool_calls=True, # Helps us see when the tool is used
markdown=True
)
# Ask questions that might need current web info
print("Asking for reviews during Restaurant Week...")
agent_with_search.print_response("Are there any reviews specifically about Osha Thai Embarcadero's Restaurant Week menu from this year or previous years?", stream=True)
print("\nAsking about the overall event...")
agent_with_search.print_response("Can you tell me more about the history or purpose of San Francisco Restaurant Week?", stream=True)
What Happened? (Looking at the Results)
Let's examine the output for the first question ("Are there any reviews specifically about Osha Thai Embarcadero’s Restaurant Week menu from this year or previous years?"):
┏━ Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ • duckduckgo_search(query=Osha Thai Embarcadero Restaurant Week menu review) ┃
┃ • duckduckgo_search(query=Osha Thai Embarcadero Restaurant Week review 2023) ┃
┃ • duckduckgo_search(query=Osha Thai Embarcadero Restaurant Week review 2022) ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (8.1s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Here are some resources related to reviews and information about Osha Thai Embarcadero ┃
┃ and their participation in Restaurant Week: ┃
┃ ┃
┃ 1 Osha Thai Restaurant Week Page ┃
┃ • Official listing with prix-fixe menu details from April 4th to 13th. ┃
┃ ┃
┃ 2 General Reviews and Info ┃
┃ • Yelp: Useful for ambiance and food quality feedback. ┃
┃ • OpenTable: Helpful for reservations and general reviews. ┃
┃ ┃
┃ 3 Past Review Insights ┃
┃ • No reviews focused only on Restaurant Week, but general reviews were available. ┃
┃ ┃
┃ Recommendation: Check Yelp, OpenTable, or the restaurant’s site for the most current feedback.┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
- Analysis: Big difference! See the
Tool Callssection? The agent used its DuckDuckGo tool multiple times to search across recent years. It didn't find direct Restaurant Week reviews, but it returned useful links and context. That’s effective use of fallback when precise answers aren’t available.
Now for the second question ("Can you tell me more about the history or purpose of San Francisco Restaurant Week?"):
┏━ Tool Call ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ • duckduckgo_search(query=San Francisco Restaurant Week history and purpose) ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (3.6s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ San Francisco Restaurant Week is an event to celebrate and showcase the city's diverse ┃
┃ food scene. It promotes local restaurants with special prix-fixe menus. ┃
┃ ┃
┃ • Purpose: Encourage people to explore local cuisine and support neighborhood spots. ┃
┃ • History: It’s a recurring event in both spring and fall, though deep historical info ┃
┃ was limited in the search results. ┃
┃ ┃
┃ More details can be found on the official San Francisco Restaurant Week website. ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
- Analysis: The agent summarized web results concisely, pointing out both purpose and frequency. It also flagged that some historical detail was missing, showing good reasoning about what was found.
Takeaway: Adding a simple web search tool, makes the agent much more capable of finding current or specific information online. The orchestration layer helps it decide when to use the tool.
Example 3: Giving the LLM Agent Specific Knowledge (RAG)
Web search is great, but what about the specific dishes on those Restaurant Week PDF menus? A general web search might struggle to pinpoint "What are the lunch main courses?" from inside a PDF.
Here, we can directly teach the agent by giving it access to those specific documents using a technique called RAG (Retrieval-Augmented Generation). Simply put, we point the agent to the PDF menu URLs, and Agno helps process them into a special knowledge base the agent can search first before trying the web or just answering from memory.
# Example 3 Code: Giving the agent specific PDF knowledge (RAG)
# (Simplified view focusing on the knowledge part)
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.embedder.openai import OpenAIEmbedder
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb, SearchType
# Set up the agent with model, RAG knowledge, instructions, and a fallback tool
agent_with_knowledge = Agent(
model=OpenAIChat(id="gpt-4o"),
description="You are an expert on Osha Thai Embarcadero's special Restaurant Week menus.",
# Instructions guide the agent on using its knowledge
instructions=[
"First, search your knowledge base (the Osha Thai Restaurant Week PDF menus) to answer questions about specific dishes, courses, options, or prices on those menus.",
"If the question is about general restaurant info [...] use the web search tool.",
"Always prefer information directly from the provided PDF menus when answering about menu content."
],
# *** The key part: Pointing to the PDF knowledge! ***
knowledge=PDFUrlKnowledgeBase(
urls=[ # The actual URLs for the lunch and dinner menus
"https://oshathai.com/wp-content/uploads/2025/03/2025-RestWeek-OshaEmc-2course.pdf",
"https://oshathai.com/wp-content/uploads/2025/03/2025-RestWeek-OshaEmc-3course.pdf"
],
vector_db=LanceDb( # Store the processed info here
uri="tmp/lancedb_osha_openai",
table_name="osha_rw_menu_openai",
embedder=OpenAIEmbedder(id="text-embedding-3-small"), # Use AI to understand PDF text
),
),
tools=[DuckDuckGoTools()], # Keep web search as a backup
show_tool_calls=True,
markdown=True
)
# Load the knowledge base (processes the PDFs)
print("Loading knowledge base...")
if agent_with_knowledge.knowledge is not None:
agent_with_knowledge.knowledge.load()
print("Knowledge base loaded.")
# Ask questions targeting the PDF content
print("\nAsking about the lunch menu (expects RAG)...")
agent_with_knowledge.print_response("What are the main course options on the Osha Thai Embarcadero Restaurant Week LUNCH menu?", stream=True)
print("\nAsking about the dinner menu dessert (expects RAG)...")
agent_with_knowledge.print_response("Is dessert included in the Restaurant Week DINNER menu at Osha Thai Embarcadero? If so, what is it?", stream=True)
# Ask a question NOT likely in the PDFs to test fallback
print("\nAsking about parking (expects Web Search)...")
agent_with_knowledge.print_response("Is there parking available near Osha Thai Embarcadero?", stream=True)
What Happened? (Looking at the Results)
- Question 1: "What are the main course options on the... LUNCH menu?"
┏━ Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ • search_knowledge_base(query=Osha Thai Embarcadero Restaurant Week LUNCH menu main course options)┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (5.4s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ The main course options on the Osha Thai Embarcadero Restaurant Week LUNCH menu are: ┃
┃ • Panang Beef Curry (GF) ┃
┃ • Pineapple Fried Rice (V) ┃
┃ • Drunken Noodles Chicken ┃
┃ • “Ka-Pow” Spicy Thai Basil ┃
┃ • Spicy Eggplant (VG) ┃
┃ These dishes provide a variety of flavors and cater to different dietary preferences, from ┃
┃ gluten-free to vegan options. ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Analysis: Success! The
Tool Callsnow showsearch_knowledge_base. This shows how the agent uses a RAG (Retrieval-Augmented Generation) approach. It doesn't just rely on its memory. Instead, it actively searches a document collection for accurate answers. This means it wasn't guessing. It pulled real answers from the source menu.Takeaway: The orchestration layer (Agno) helped the agent decide to search rather than hallucinate. This is a great example of how adding retrieval makes agents more useful for real-world tasks like answering menu questions with accuracy.
Question 2: "Is dessert included in the... DINNER menu? If so, what is it?"
┏━ Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ • search_knowledge_base(query=Restaurant Week dinner menu dessert Osha Thai Embarcadero) ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (3.3s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Yes, dessert is included in the Restaurant Week dinner menu at Osha Thai Embarcadero. The dessert ┃
┃ offered is **Mango & Sticky Rice**. ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Analysis: Same result. It used the
search_knowledge_basetool and accurately extracted the dessert information ("Mango & Sticky Rice") from the dinner menu PDF.Takeaway: This proves agents can be used for document Q&A, like PDFs and internal files. With the right setup, they go beyond general knowledge and answer with exact facts.
Question 3: "Is there parking available near Osha Thai Embarcadero?"
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Is there parking available near Osha Thai Embarcadero? ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ • duckduckgo_search(query=Osha Thai Embarcadero parking options) ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (3.8s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Yes, parking is available near Osha Thai Embarcadero in the Embarcadero Center's┃
┃ secure parking facility. You can find parking with a validation ticket at the ┃
┃ Embarcadero 4 parking garage, with its entrance off Drum Street. There are also ┃
┃ street parking options available in the vicinity. ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
- Analysis: This shows the power of combining knowledge and tools. The agent knew parking info wasn't in the menu PDFs (its primary knowledge source). Following its instructions, it intelligently switched to using its backup tool:
duckduckgo_search. It successfully found the relevant parking information online. - Takeaway: When you combine tools like RAG and Web Search, agents become multi-modal problem solvers. They know when to use your internal data vs the open web.
Orchestration tools like Agno often allow developers to add more advanced capabilities easily:
- Knowledge: Developers can give an agent specific documents to read (like uploading a PDF manual or company guidelines). The agent can then search this private knowledge base before searching the general web.
- Memory: Agents can be given memory to remember past parts of your conversation, making interactions feel more natural.
- Agent Teams: For complex tasks, developers can create teams of specialized agents. For example, one agent might be expert at web searching, while another is expert at analyzing financial data. A "manager" agent can coordinate the team to answer a complex question.
How Would You Interact With Such an Agent?
Okay, developers can use tools like Agno to build these agents. But how would you, as a user, interact with one? Usually, through a User Interface (UI), often looking like a chat window.
- Developer Tools: Agno provides a sample web UI called "Playground" where developers can directly chat with and test the agents they build locally on their computers.
- Custom Web Apps: More commonly, developers build custom web applications. Imagine a simple website with a chat box. When you type a message, the website sends it to the agent running behind the scenes (maybe built using Agno and hosted on a server). The agent processes the request (using its brain and tools) and sends the answer back to the website to display to you.
- Hosting: These custom web UIs need to be hosted somewhere to be accessible online. Platforms like Vercel are popular choices for hosting such web applications, making it easy for anyone to access the agent through their web browser.
This was just a quick peek using one specific tool, Agno, as an example. Many other orchestration tools exist, but the core idea is the same: they act as the vital link connecting the powerful AI brain (LLM) to the tools, knowledge, and memory needed to perform tasks effectively and reliably in the real world. They are essential for building the helpful AI agents we have been discussing.
Part 5: AI's Future and Our Big Questions
The potential is exciting, but the public concerns found by Pew Research are important:
- Jobs: Will agents take our jobs? The public (64%) is much more worried about job loss than experts (39%). Some jobs might change or be reduced, while new jobs (like the AI Engineer who builds and manages these agents) will likely emerge. Being open about how AI is used at work is crucial.
- Control and Regulation: People want more say. Over half of both the public (55%) and experts (57%) want more control over how AI affects their lives. There's also widespread worry (around 60% of both groups) that government rules for AI won't be strict enough. People lack confidence in both government and companies to manage AI responsibly. This shows a clear need for careful oversight.
- Bias and Fairness: As we saw, AI can learn human biases from its training data. Making AI fair requires effort: using diverse data, testing for bias, building diverse development teams, and keeping humans involved in important decisions.
AI agents have huge potential to help us. But they are not magic, and they are not perfect. Understanding how they work (their strengths and weaknesses) is the first step.
Addressing people's valid concerns requires companies to be transparent, governments to create thoughtful rules, developers to focus on fairness, and society to ensure humans stay in control, especially for critical tasks. This is not just about technology; it is about deciding together how we want to use it.
We started with the excitement about ChatGPT and moved on to understanding the "probabilistic" brains of LLMs and how they learn from vast amounts of text. We explored their limitations, like making things up or inheriting biases, which helps explain some common concerns.
Then we met AI Agents, the next step where AI uses "tools" to take action in various environments, from your web browser to the physical world. We saw how they could help in everyday tasks like university admissions or customer support, while also acknowledging the challenges around jobs, control, and fairness highlighted by research like Pew's.
The path forward involves using AI's power responsibly. By understanding AI better (both its amazing potential and its real limitations) we can all participate more wisely in shaping its future, moving beyond just excitement or fear towards informed discussion and thoughtful choices.