AI Agents: How to Build Goal-Oriented Problem Solvers

Imagine software that thinks, learns, and solves problems like a human. AI agents are making this possible by redefining automation.

What Are AI Agents?

AI agents are autonomous software systems that simulate human decision-making to solve tasks efficiently and intelligently. Unlike traditional programs, AI agents:

• Perceive the environment through sensors or tools.
• Plan and Decide by analyzing data and selecting optimal actions.
• Learn and Adapt from new information and experiences.
• Act autonomously with minimal human input.

Core Characteristics of AI Agents

AI agents stand out due to the following traits:

• Autonomy: Operates without needing frequent instructions.
• Goal-Oriented: Focused on accomplishing well-defined objectives.
• Adaptability: Improves performance through iterative learning.
• Proactivity and Reactivity: Anticipates tasks while responding to real-time changes.

Why Do We Need An AI Agent?

Modern challenges require intelligent, scalable solutions that traditional software cannot provide. AI agents address these needs by:

• Automating Routine and Complex Tasks: Saves time and resources.
• Managing Large-Scale Data: Processes data faster than humans can.
• Customizing User Experiences: Delivers highly personalized outputs.
• Improving Decision Accuracy: Reduces errors by analyzing patterns.

Applications of AI Agents

Building a Goal-Oriented AI Agent

Let’s create a goal-oriented AI agent that retrieves and summarizes information from the web. This agent will:

• Search: Use DuckDuckGo to find relevant results.
• Process: Analyze and summarize the data with a language model.
• Deliver: Present actionable insights to the user.

I’ll use CrewAI, LangChain, and the DuckDuckGo Search Tool to achieve this.

Code Implementation

Here’s a step-by-step implementation of the AI agent:

1. Import the required libraries

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import os
from crewai import Agent, Task, Crew, LLM, Process
from crewai_tools import tool
from langchain_community.tools import DuckDuckGoSearchRun
from dotenv import load_dotenv

load_dotenv()

duckduckgo_search = DuckDuckGoSearchRun()
api_key = os.getenv("GROQ_API_KEY")
user_query = "Onurhan Demir work title and last company name"

if not api_key:
    raise ValueError("API key is missing.")

2. Define the Search Tool

The search tool allows the agent to query DuckDuckGo for web-based results.

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@tool('DuckDuckGoSearch')
def search_tool(search_query: str):
    """Search the web for information on a given topic"""
    return duckduckgo_search.run(search_query)

3. Initialize the Language Model (LLM)

The LLM interprets the search data and generates a summary.

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llm = LLM(
    model="groq/llama-3.1-8b-instant",
    api_key=api_key
)

4. Define the Agent

I use a Large Language Model (LLM) to generate human-like responses using extensive text data. In this project, the groq/llama-3.1-8b-instant model summarizes search results, improving our AI agent's accuracy.

The agent coordinates tasks and tools to achieve its goal:

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search_agent = Agent(
    role='Expert Researcher and Analyst',
    goal='Provide insightful summaries and generate related research topics using English sources.',
    backstory='A senior researcher proficient in detailed technical analysis.',
    tools=[perform_web_search],
    llm=llm,
    verbose=True,
)

5. Set Up a Task

A task describes what the agent needs to accomplish, including the expected output.

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search_task = Task(
    description=f'Search and summarize information about "{user_query}".',
    agent=search_agent,
    expected_output='A summary of the top 5 findings with actionable insights.'
)

6. Execute the Task

Finally, execute the agent with the Crew framework:

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crew = Crew(
    agents=[search_agent],
    tasks=[search_task],
    process=Process.sequential,
    verbose=True,
)

output = crew.kickoff()
print(output)

If you want to see the full code, you can find it here.

Code Breakdown

Expected Output

Using the earlier implementation, the AI agent can accomplish a task such as:

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user_query = "Onurhan Demir work title and last company name"

• Search: The agent queries DuckDuckGo for the most relevant results about "Onurhan Demir work title and last company name".
• Summarize: The LLM processes the top results to create a concise and actionable summary.
• Deliver: Outputs the summary along with suggested research topics.

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**Summary**:
1. Onurhan Demir is a Software Developer at Insider, specializing in SaaS products. Source: useinsider.com
2. Recently transitioned to a Software Developer role with expertise in frontend technologies. Source: linkedin.com/in/onurhan-demir
3. Notable contributor to open-source projects, focusing on automation tools. Source: github.com/onurhan1337

**Research Topics**:
- Career progression trends in SaaS engineering.
- Emerging tools for frontend developers in 2024.
- The impact of open-source contributions in automation.

Conclusion

Goal-oriented AI agents simplify complex workflows by automating tasks like information retrieval, analysis, and summarization.

By understanding the architecture and processes behind these agents, you unlock their full potential to solve real-world problems effectively.