From Prompt to Report: Building Your First Autonomous AI Agent with GPT-OSS

Ready to move beyond simple chatbots and build something that acts? The era of passive language models that merely respond to queries is evolving into a new, dynamic paradigm: the age of autonomous AI agents. These are systems that can reason, plan, and execute complex, multi-step tasks to achieve a specific goal. This article is your definitive, hands-on guide to creating a fully autonomous AI agent using the unique and formidable power of GPT-OSS. We will take you step-by-step through the process of building a practical Autonomous Web Research Assistant. This agent will be able to take a single research topic, independently browse the live internet, extract crucial information from multiple sources, and compile a comprehensive, structured report—all without direct human intervention. This isn't just theory; it's a deep dive into the practical application of the agentic capabilities that establish GPT-OSS as a true titan in the open-source world. Get ready to build your first AI agent.

In this comprehensive tutorial, you will master the core concepts of agentic AI and translate them into functional Python code. You will learn to:

• Design and structure a true "agentic workflow" from scratch.
• Leverage GPT-OSS's native Function Calling to give your agent the ability to interact with the web.
• Master the art of prompting for complex, multi-step tasks.
• Utilize GPT-OSS's transparent Chain-of-Thought (CoT) to easily debug your agent's reasoning process, a game-changing advantage for building reliable AI.

Table of Contents

• The Agentic Shift: Why Autonomous AI is the Next Frontier
• Architectural Blueprint: Designing Our GPT-OSS Research Agent
• The Complete Implementation: Let's Build with Python and GPT-OSS
• Putting It to the Test: A Live Walkthrough with GPT-OSS
• Best Practices and Advanced Techniques for Agent Development
• Conclusion: The Future is Agentic, and It Starts with You

The Agentic Shift: Why Autonomous AI is the Next Frontier

For the past several years, the world has been captivated by the conversational abilities of Large Language Models (LLMs). We've witnessed them write poetry, debug code, and answer questions with astounding fluency. However, this is only the first chapter. The next major leap in artificial intelligence lies in transforming these models from passive knowledge repositories into active, goal-oriented agents that can operate in the world.

Beyond Chatbots: Defining the AI Agent

So, what truly separates an "AI Agent" from a standard "chatbot"? The distinction is fundamental and can be understood through a few key attributes:

• Goal-Oriented: An agent is given a high-level objective, not just a single prompt. Its purpose is to achieve that objective, even if it requires dozens of intermediate steps.
• Autonomous Planning: An agent can break down its goal into a sequence of smaller, manageable tasks. It can reason about the best course of action and adapt its plan as new information becomes available.
• Tool Use: This is perhaps the most critical differentiator. An agent can interact with the outside world through a predefined set of tools. These could be anything from a web search API, a code interpreter, a database query engine, or even an interface to control a smart home.
• Memory and Context: An agent maintains a memory of its past actions, observations, and conclusions. This contextual awareness is vital for making informed decisions throughout its operational loop.

A helpful mental model for this is the OODA Loop, a concept from military strategy: Observe, Orient, Decide, and Act. An AI agent continuously observes its environment (e.g., the output from a tool), orients itself based on this new information and its overall goal, decides on the next action to take, and then acts by executing a tool. This cyclical process continues until the primary goal is achieved.

The Core Components of Our Autonomous Agent

To build our Autonomous Web Research Assistant, we need to conceptualize its core components, which will later translate directly into our Python code:

1. The Orchestrator (The "Brain"): This is the central LLM that drives the entire operation. It is responsible for planning, reasoning, and deciding which actions to take. For our project, the orchestrator will be a powerful GPT-OSS model, chosen specifically for its superior reasoning and instruction-following capabilities.
2. The Toolset (The "Hands"): These are the external functions that the orchestrator can call to interact with the world. Our agent will start with two essential tools: one for performing web searches and another for scraping the content of a webpage.
3. Memory (The "Notebook"): This is the mechanism for retaining context. In our implementation, the memory will be the conversation history itself—a continuously updated log of user requests, the agent's thoughts, tool calls, and tool outputs. This running log provides the agent with the full context of its ongoing mission.

Why GPT-OSS is Uniquely Suited for Building Agents

While many LLMs can perform components of this workflow, GPT-OSS was engineered from the ground up with features that make it an exceptional choice for building robust and reliable agents. As explored in our detailed model comparison, its advantages are profound:

• Native, Reliable Function Calling: GPT-OSS is fine-tuned to understand when and how to call external tools. It generates structured, machine-readable requests (like JSON) with high accuracy, minimizing the errors that plague many other models when trying to integrate with external systems.
• Unparalleled Instruction Following: Agentic workflows rely on the model adhering strictly to a complex set of instructions defined in a system prompt. GPT-OSS excels at this, faithfully following rules, output formats, and constraints, which is critical for predictable behavior.
• Transparent Chain-of-Thought (CoT): This is the killer feature for agent development. Thanks to the Harmony Chat Format, GPT-OSS can expose its entire internal reasoning process—its analysis—before it decides on an action. This provides an unprecedented, auditable trail of its logic. When an agent behaves unexpectedly, you don't have to guess why; you can simply read its thoughts. This turns the frustrating task of debugging a "black box" into a straightforward process of analysis and prompt refinement.

Architectural Blueprint: Designing Our GPT-OSS Research Agent

Before writing a single line of code, it's essential to design the architecture of our agent. A clear blueprint will guide our implementation and ensure all components work together harmoniously.

The Mission: Defining the Agent's Goal

The entire process begins with a single, high-level user request. This is the mission objective. For our purposes, a typical mission might be:

"Research the impact of Mixture of Experts (MoE) architecture on LLM efficiency and compile a detailed report. The report should cover the core concepts, key benefits, notable examples of MoE models, and any potential drawbacks."

The agent's sole purpose is to take this prompt and produce a final report that satisfies all its requirements.

The Agent's Workflow: A Step-by-Step Breakdown

Our agent will follow a cyclical, intelligent workflow to accomplish its mission. This process can be broken down into five key phases:

1. Deconstruction & Planning: Upon receiving the mission, the Orchestrator (GPT-OSS) first analyzes the request and forms a high-level plan. Its internal monologue might be: "I need to understand MoE. My first step is to perform a broad web search to get an overview and identify key sources."
2. Tool Selection & Execution: Based on its plan, the agent selects the most appropriate tool from its toolbelt. In the first step, this would be the perform_web_search tool. It then generates the necessary arguments (the search query) and executes the tool call.
3. Information Synthesis & Re-Planning: The agent receives the output from the tool (e.g., a list of search results). It then synthesizes this new information and decides on the next step. For example: "These search results mention 'Switch Transformers' and a 'Google AI Blog' post. The blog post seems authoritative. I will now use the scrape_website_content tool on that URL."
4. Iteration: The agent repeats Phases 2 and 3, continuously searching, scraping, and synthesizing information. It might go down rabbit holes, find new keywords, and perform new searches, gradually building a comprehensive body of knowledge about the topic. It continues this loop until it concludes that it has gathered sufficient information to write the report.
5. Final Report Generation: Once the research phase is complete, the agent enters the final phase. It synthesizes all the information it has gathered from its memory and generates a structured, well-written report that fulfills the original user request.

The Toolbelt: Defining Our Agent's Capabilities

Our agent's effectiveness is directly tied to the power of its tools. For this tutorial, we will equip it with two fundamental capabilities for interacting with the internet. These will be implemented as Python functions and described to GPT-OSS in a specific format so it knows how to use them.

• perform_web_search(query: str) -> str: This function will take a search query string as input. It will then use a third-party API, such as the SerpApi service, to execute the search and return a formatted string containing the top search results, including titles, links, and snippets.
• scrape_website_content(url: str) -> str: This function will take a single URL as input. It will use Python libraries like requests and BeautifulSoup to fetch the HTML content of the webpage, parse it to remove boilerplate (like ads, navigation bars, and footers), and return the clean, main textual content of the article. This provides the agent with the raw information it needs to learn.

With this clear architectural plan, we are now ready to translate our design into a complete, working Python implementation.

The Complete Implementation: Let's Build with Python and GPT-OSS

This section is the heart of our guide. We will walk through every line of code required to build our ResearchAgent, explaining the purpose and logic behind each component.

Setting Up Your Development Environment

First, let's prepare our Python environment. You will need to install a few libraries. You can do this using pip:

pip install openai requests beautifulsoup4 python-dotenv serpapi-google-search-results

• openai: The official Python client for interacting with OpenAI-compatible APIs. We will use this to communicate with our GPT-OSS model endpoint.
• requests: A standard library for making HTTP requests, which we'll use to fetch webpage content.
• beautifulsoup4: A powerful library for parsing HTML and XML documents. This is the key to cleaning up scraped web content.
• python-dotenv: A utility to manage environment variables, perfect for storing API keys securely.
• serpapi-google-search-results: The official Python client for SerpApi, which simplifies the process of getting Google search results.

You will also need to sign up for a free account at SerpApi to get an API key. Once you have it, create a file named .env in your project directory and add your key like this:

SERPAPI_API_KEY="your_serpapi_api_key_here"
OPENAI_API_KEY="your_openai_or_gptoss_api_key_here"
OPENAI_BASE_URL="your_gptoss_api_base_url_here" # e.g., https://api.gptoss.ai/v1

For this tutorial, we assume you have access to a GPT-OSS model endpoint. If you're running the model locally, you can follow our guide to local setup and point the OPENAI_BASE_URL to your local server. Alternatively, you can easily test your agent by interacting with the models directly on the gptoss.ai platform.

The Toolbelt: Implementing Our Functions

Let's start by creating a file named tools.py and implementing the two functions we designed.

# tools.py
import os
import requests
from bs4 import BeautifulSoup
from serpapi import GoogleSearch
from dotenv import load_dotenv

load_dotenv()

def perform_web_search(query: str) -> str:
    """
    Performs a Google search for the given query using SerpApi and returns a formatted string of results.
    """
    print(f"INFO: Performing web search for: '{query}'")
    try:
        params = {
            "api_key": os.getenv("SERPAPI_API_KEY"),
            "engine": "google",
            "q": query,
            "google_domain": "google.com",
            "gl": "us",
            "hl": "en"
        }
        search = GoogleSearch(params)
        results = search.get_dict()

        # Process and format the results
        output_string = ""
        if "organic_results" in results:
            for result in results["organic_results"][:5]: # Limit to top 5 results
                output_string += f"Title: {result.get('title', 'N/A')}\n"
                output_string += f"Link: {result.get('link', 'N/A')}\n"
                output_string += f"Snippet: {result.get('snippet', 'N/A')}\n---\n"
        
        return output_string if output_string else "No results found."

    except Exception as e:
        return f"Error performing search: {e}"

def scrape_website_content(url: str) -> str:
    """
    Scrapes the main textual content from a given URL.
    """
    print(f"INFO: Scraping website content from: '{url}'")
    try:
        headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
        }
        response = requests.get(url, headers=headers, timeout=15)
        response.raise_for_status() # Raise an exception for bad status codes

        soup = BeautifulSoup(response.text, 'html.parser')

        # A simple but effective way to get main content: remove script, style, nav, footer
        for element in soup(["script", "style", "nav", "footer", "header"]):
            element.decompose()

        text = soup.get_text(separator='\n', strip=True)
        # Limit the content to a reasonable length to avoid excessive token usage
        return " ".join(text.split())[:8000]

    except requests.RequestException as e:
        return f"Error fetching URL: {e}"
    except Exception as e:
        return f"Error scraping website: {e}"

The Heart of the Agent: The ResearchAgent Class

Now, let's create the main file, agent.py. This file will contain the ResearchAgent class, which acts as the orchestrator.

# agent.py
import os
import json
from openai import OpenAI
from dotenv import load_dotenv
from tools import perform_web_search, scrape_website_content

load_dotenv()

class ResearchAgent:
    def __init__(self, model="gpt-oss-120b"):
        self.client = OpenAI(
            api_key=os.getenv("OPENAI_API_KEY"),
            base_url=os.getenv("OPENAI_BASE_URL"),
        )
        self.model = model
        self.messages = []
        self.available_tools = {
            "perform_web_search": perform_web_search,
            "scrape_website_content": scrape_website_content,
        }
        self.tool_definitions = [
            {
                "type": "function",
                "function": {
                    "name": "perform_web_search",
                    "description": "Performs a Google search for a given query to find relevant websites.",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "query": {
                                "type": "string",
                                "description": "The search query to use."
                            }
                        },
                        "required": ["query"]
                    }
                }
            },
            {
                "type": "function",
                "function": {
                    "name": "scrape_website_content",
                    "description": "Scrapes the main textual content from a given URL. Use this to 'read' a webpage.",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "url": {
                                "type": "string",
                                "description": "The URL of the website to scrape."
                            }
                        },
                        "required": ["url"]
                    }
                }
            }
        ]

    def _get_system_prompt(self):
        return """
You are a world-class AI research assistant. Your goal is to provide a comprehensive, well-structured, and accurate report in response to a user's research request.

Here is your workflow:
1.  **Deconstruct & Plan:** Analyze the user's request and create a step-by-step research plan.
2.  **Execute Plan:** Use the available tools to gather information. You must iterate, reflect on the gathered information, and decide on the next steps.
3.  **Synthesize & Report:** Once you have gathered sufficient information, synthesize it into a final report. Do not provide the report until you are confident your research is complete.

**Rules:**
- You MUST use the `perform_web_search` tool first to identify relevant sources. Do not make up URLs.
- After searching, use the `scrape_website_content` tool to read the content of the most promising URLs.
- Critically evaluate the information you find. If sources conflict, note the discrepancy.
- You can perform multiple searches and scrape multiple websites to build a comprehensive understanding.
- When you believe you have enough information, conclude your research by providing a final, detailed report to the user. Do not ask "should I start writing the report?". Just write it.
- Your final output must be only the report itself, formatted in Markdown. Do not include your internal monologue or tool usage in the final report.
"""

    def run(self, user_prompt: str, max_iterations: int = 10):
        self.messages = [
            {"role": "system", "content": self._get_system_prompt()},
            {"role": "user", "content": user_prompt}
        ]

        for i in range(max_iterations):
            print(f"\n----- Iteration {i+1} -----")
            
            response = self.client.chat.completions.create(
                model=self.model,
                messages=self.messages,
                tools=self.tool_definitions,
                tool_choice="auto"
            )

            response_message = response.choices[0].message
            self.messages.append(response_message)
            
            tool_calls = response_message.tool_calls

            if tool_calls:
                for tool_call in tool_calls:
                    function_name = tool_call.function.name
                    function_to_call = self.available_tools.get(function_name)
                    
                    if not function_to_call:
                        # This should ideally not happen if the model is well-behaved
                        print(f"ERROR: Model tried to call unknown function '{function_name}'")
                        continue

                    try:
                        function_args = json.loads(tool_call.function.arguments)
                        print(f"MODEL: Calling function '{function_name}' with args {function_args}")
                        
                        function_response = function_to_call(**function_args)
                        
                        self.messages.append({
                            "tool_call_id": tool_call.id,
                            "role": "tool",
                            "name": function_name,
                            "content": function_response,
                        })
                    except json.JSONDecodeError:
                        print(f"ERROR: Could not decode function arguments: {tool_call.function.arguments}")
                    except Exception as e:
                        print(f"ERROR: Exception during tool call: {e}")

            else:
                # No tool call, assume it's the final answer
                final_answer = response_message.content
                print("\n----- Final Report -----")
                print(final_answer)
                return final_answer
        
        print("\n----- Max Iterations Reached -----")
        return "The agent reached the maximum number of iterations without providing a final report."

if __name__ == '__main__':
    agent = ResearchAgent()
    request = "Research the impact of Mixture of Experts (MoE) architecture on LLM efficiency and compile a detailed report. The report should cover the core concepts, key benefits, notable examples of MoE models, and any potential drawbacks."
    agent.run(request)

Let's break down this crucial class:

• __init__: The constructor sets up the OpenAI client, defines the model to be used, and, most importantly, creates the tool_definitions. This JSON-like structure is how we describe our Python functions to GPT-OSS. It includes the function name, a description of what it does, and a schema for its parameters. This is the foundation of Function Calling.
• _get_system_prompt: This method contains the "master prompt" or "constitution" for our agent. It's a detailed set of instructions that tells the agent its persona, its goal, its workflow, and the rules it must follow. A well-crafted system prompt is the single most important factor in an agent's success.
• run: This is the main execution loop. It takes the user's initial prompt and iterates up to max_iterations. In each iteration, it sends the entire message history to the model. It then checks if the model's response contains a tool_calls request.
  • If yes, it executes the requested tool, captures the output, and appends it back to the message history with a special "role": "tool". This informs the model of the result of its action.
  • If no, it assumes the model has finished its research and is providing the final report. It prints the report and terminates the loop.

Putting It to the Test: A Live Walkthrough with GPT-OSS

Now that the code is complete, let's run it and trace the agent's execution. When we run python agent.py, we are giving our agent the mission to research MoE architecture. What follows is a simulated but realistic trace of the agent's internal monologue and actions, showcasing the power of GPT-OSS's transparent reasoning.

The Initial Prompt: Researching "Mixture of Experts"

We start the agent with our mission: Research the impact of Mixture of Experts (MoE) architecture...

Deconstructing the Agent's Thought Process (The CoT Advantage)

If we were to inspect the raw output from the GPT-OSS model at each step (which its Harmony Chat Format allows), we would see a clear separation between its internal reasoning (analysis channel) and its actions (tool_calls). Here is what that process looks like:

----- Iteration 1 -----

• GPT-OSS Internal Thought (CoT): "The user wants a detailed report on Mixture of Experts (MoE) in LLMs. My first step is to get a general understanding of the topic. I will perform a web search to find introductory articles, key papers, and definitions."
• MODEL: Calling function 'perform_web_search' with args {'query': 'Mixture of Experts (MoE) in Large Language Models'}
• Our tools.py script executes the search and returns a list of URLs and snippets.

----- Iteration 2 -----

• GPT-OSS Internal Thought (CoT): "The search results provided several promising links, including one from Hugging Face and another from Google AI's blog. The Google AI link, 'Switch Transformers: Toward a More Efficient Path for Language Models', seems highly relevant and authoritative. I will now scrape the content of this page to extract detailed information."
• MODEL: Calling function 'scrape_website_content' with args {'url': 'https://ai.googleblog.com/2021/01/switch-transformers-toward-more.html'}
• Our tools.py script scrapes the webpage and returns the clean text content.

----- Iteration 3 -----

• GPT-OSS Internal Thought (CoT): "The scraped content from the Google blog was very informative. It mentions the core benefit of MoE is conditional computation, which reduces computational cost. It also named the 'Switch Transformer' as a key model. To deepen my understanding, I should now search specifically for how the 'Switch Transformer' works and what makes it different."
• MODEL: Calling function 'perform_web_search' with args {'query': 'Switch Transformer architecture explained'}
• The agent performs another, more targeted search.

----- Iteration 4 -----

• GPT-OSS Internal Thought (CoT): "The new search results point to articles that explain the 'routing' mechanism in MoE models. This seems to be a critical concept. I have gathered information on the definition of MoE, its benefits (efficiency), a key example (Switch Transformer), and a core mechanism (routing). I believe I have enough information to construct a comprehensive report that addresses all parts of the user's original request."
• (The model now generates the final report instead of calling another tool.)

The Final Output: The Generated Report

Finally, the agent outputs its synthesized findings as a clean, Markdown-formatted report.

[Image: A visually appealing mockup of the final generated report. It should look like a clean document with a clear title, headings for each section (Core Concepts, Key Benefits, etc.), and well-formatted text, showcasing the quality of the end product.] Alt: A mockup of the final Markdown report generated by the GPT-OSS autonomous research agent, showing a well-structured and detailed analysis.

----- Final Report -----

The Impact of Mixture of Experts (MoE) on Large Language Model Efficiency

1. Core Concepts

The Mixture of Experts (MoE) architecture represents a significant paradigm shift in designing Large Language Models (LLMs). Unlike traditional "dense" models where every parameter is activated for every input token, MoE models utilize a strategy called conditional computation.

An MoE layer is composed of two main parts:

• A large number of smaller neural networks called "experts."
• A "gating network" or "router" that dynamically decides which few experts are best suited to process the current input token.

For any given token, only a small subset of experts are activated, while the rest remain dormant. This allows MoE models to have a massive total number of parameters (leading to greater model capacity and knowledge) while maintaining a relatively low computational cost during inference.

2. Key Benefits

• Computational Efficiency: The primary advantage is a drastic reduction in the number of floating-point operations (FLOPs) required per inference, as only a fraction of the model is used for each token.
• Scalability: MoE allows for the creation of models with trillions of parameters, a scale that would be computationally prohibitive for dense models.
• Specialization: Each expert can theoretically specialize in processing specific types of information or linguistic patterns, potentially leading to more nuanced understanding.

3. Notable Examples

• Switch Transformer (Google AI): One of the most influential MoE models, the Switch Transformer demonstrated the ability to scale a model to over a trillion parameters while being significantly faster than similarly sized dense models.
• Mixtral 8x7B (Mistral AI): A popular open-source model that uses 8 experts, with the router selecting 2 for each token. It achieves performance comparable to much larger dense models.

4. Potential Drawbacks

• Training Complexity: Training MoE models can be more complex and requires careful tuning of the routing algorithm to ensure a balanced load across experts.
• Memory Requirements: While inference FLOPs are low, the entire model with all its experts must still be loaded into memory, which can be substantial.

This entire process, from a single prompt to a detailed report, was accomplished autonomously by our GPT-OSS agent.

Best Practices and Advanced Techniques for Agent Development

Building a simple agent is the first step. Creating a truly robust, reliable, and production-ready agent requires mastering several advanced concepts. For more expert guides, you can always explore our AI blog.

Prompt Engineering for Agents: The Master Prompt

The system prompt is the agent's constitution. A small change here can have a massive impact on its behavior. A good master prompt should be highly detailed and include:

• Persona: "You are a world-class AI research assistant."
• Goal: "Your goal is to provide a comprehensive... report."
• Workflow: Explicitly list the steps the agent should follow.
• Rules & Constraints: Define strict rules, such as "You MUST use the perform_web_search tool first." This prevents the agent from hallucinating URLs or getting stuck.
• Output Format: Specify the exact format for the final output.

Error Handling and Agent Resilience

Real-world tools fail. Websites go down, APIs return errors, and content might be un-parseable. A robust agent must be ableto handle these failures gracefully. You can improve resilience by:

• Returning Error Messages: Your tool functions (scrape_website_content) should catch exceptions and return a descriptive error message as a string.
• Informing the Model: When you append the tool output to the message history, include this error message. The model will see that its last action failed and can then decide on a new course of action, such as trying a different URL or re-phrasing its search query.

Managing Cost, Tokens, and Preventing Infinite Loops

Autonomous agents can run for many iterations, which can lead to high token consumption and potential infinite loops if it gets stuck. To manage this:

• Set a Max Iteration Limit: As we did in our code, always have a hard stop to prevent run-away processes.
• Token Limit Monitoring: For production systems, monitor the total token count of the message history and stop the agent if it exceeds a certain budget.
• Context Summarization: For very long-running tasks, an advanced technique is to use a second LLM call to periodically summarize the conversation history, compressing the information to keep the context window manageable.

Expanding the Toolset

The true power of agents comes from their tools. You can make your agent vastly more capable by adding new functions to its toolbelt:

• Code Interpreter: A tool that can write and execute Python code in a sandboxed environment for data analysis, calculations, or visualization.
• Database Access: A tool to query a SQL or NoSQL database to retrieve structured information.
• File System Access: Tools that allow the agent to read and write local files, enabling it to work with documents on a user's machine.

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Related Reading

• The Ultimate Guide to GPT-OSS: A Deep Dive into OpenAI's Open-Source Revolution: Before building with it, understand what makes GPT-OSS a landmark release. This guide covers its architecture, licensing, and strategic importance.
• Clash of the Titans: GPT-OSS vs. Llama 3 vs. Mistral — An In-Depth Benchmark: See how GPT-OSS stacks up against other leading open-source models in real-world tests of reasoning, instruction following, and code quality.
• GPT-OSS: Use it for Free & Learn to Run it Locally: Your complete guide to getting started with GPT-OSS, whether you want to try it instantly online or set it up on your own hardware.

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Conclusion: The Future is Agentic, and It Starts with You

In this guide, we have journeyed from the theoretical concept of an AI agent to a fully functional, autonomous web researcher. We have seen how the unique strengths of GPT-OSS—its reliable tool use, strict adherence to instructions, and unparalleled transparency—make it the ideal foundation for building this next generation of intelligent systems. This project is more than just a technical exercise; it's a gateway to a new way of thinking about and interacting with artificial intelligence.

The tools and techniques you've learned here are the building blocks for creating far more sophisticated applications. Imagine agents that can manage your calendar, automate your business workflows, or conduct scientific research. The possibilities are limited only by our imagination. The agentic shift is here, and with open, powerful, and controllable models like GPT-OSS, the power to build this future is now in your hands.

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