This content originally appeared on Level Up Coding - Medium and was authored by Huzefa Hamdard
How to master Agentic AI and build agents to work for us
I still remember the moment when I first heard about AI agents. I said this is mind blowing. And now Agentic AI which is next step in the future.
With Agentic AI we just have to tell the goal, what we want to achieve and leave rest to the AI. Before, with AI agents we have to give instructions for each task but now it is next level, AI will take care of everything.
Agentic AI systems are much like human employees, rather than traditional software or AI systems, in that instead of focusing on singular tasks, agentic AI programs can handle many responsibilities and perform diverse functions.
Discovering Agentic AI’s Potential
The more I explored, the more I realized how versatile Agentic AI is. Tools like PhiData and GroqCloud make it easy for developers to build powerful applications. This opens up endless possibilities for customization and scalability.
What Makes Agentic AI Unique?
Every feature of Agentic AI is for solving modern challenges:
1. Autonomy: I was amazed to learn how these systems could operate without human intervention. They make real-time decisions based on the goals and how we train it.
2. Adaptability: Agentic AI thrives on unpredictability. It continuously learns and evolves, handling new data and unforeseen circumstances with ease.
3. Collaboration: This is the most impressive aspect. As we human tend to work in collaboration these Agentic AI can also work together. When we use it in multi-agent setup, they can collaborate and work seamlessly to achieve complex goals.
4. Versatility: With platforms like GroqCloud, I could see how users can select models that best fit their needs, whether it’s OpenAI’s GPT or open-source frameworks like LLaMA.
Quick Comparison: Agentic AI vs. Traditional AI Agents
My First Experiment with PhiData
Excited to dive in, I started experimenting with PhiData, a framework designed for orchestrating multi-agent systems. Setting it up was surprisingly very easy and straightforward:
Step 1: Install PhiData
pip install phidata
Step 2: Create and Configure Agents
I defined an agent to collect knowledge about the latest AI trends:
from phi.agent import Agent
knowledge_agent = Agent(
name="Knowledge Collector",
role="Collect data on the latest AI trends.",
model=Groq(id="llama-3.3-70b-versatile"),
)
web_agent = Agent(
name="Web Agent",
role="Search the web for information",
model=OpenAIChat(id="gpt-4o"),
tools=[DuckDuckGo()],
instructions=["Always include sources"],
show_tool_calls=True,
markdown=True,
)
Step 3: Collaboration between agents (Multi-Agent Workflows)
It was thrilling to see how agents could collaborate to generate insights:
agent_team = Agent(
team=[knowledge_agent, web_agent],
instructions=["Always include sources", "Use tables to display data"],
show_tool_calls=True,
markdown=True,
)
agent_team.print_response("Summarize the latest advancements in AI.", stream=True)Exploring GroqCloud’s Flexibility
Next, I turned to GroqCloud, a platform that offers scalable hosting for AI models. Its seamless integration with PhiData made the experience even better.
Step 1: Set Up an Account
Signing up was simple, and configuring the workspace took minutes.
Step 2: Deploy a Model
I deployed OpenAI’s GPT model to answer a question about Agentic AI:
from phi.model.groq import Groq
groq_model = Groq(id="openai-gpt-4")
response = groq_model.run("Explain the difference between Agentic AI and traditional AI agents.")
print(response)
Step 3: Use Multiple Models
Combining different models led to even more innovative solutions:
ai_agent = Agent(
name="AI Assistant",
role="Provide insights on AI technologies.",
model="openai-gpt-4"
)
open_source_agent = Agent(
name="Open Source Assistant",
role="Suggest open-source tools for AI development.",
model="llama-2-13b"
)
Real-World Applications
As I explored more, I realized the transformative potential of Agentic AI across industries:
- Healthcare: Autonomous systems for diagnosis and treatment.
- Finance: Adaptive trading strategies that evolve with market trends.
- Robotics: Smarter navigation for autonomous robots in dynamic environments.
- Smart Cities: Optimizing traffic flow and managing resources efficiently.
- Multi-Agent Collaboration: Coordinating teams of AI agents to solve complex problems.
Orchestrating Multi-Agent Systems
Here’s an example I tried, orchestrating multiple agents with GroqCloud models:
from phi.agent import Agent
# Define agents
data_agent = Agent(
name="Data Collector",
role="Collect information on Agentic AI.",
model="openai-gpt-4"
)
analysis_agent = Agent(
name="Data Analyst",
role="Analyze collected data for trends.",
model="llama-2-13b"
)
# Orchestrate agents
agent_team = Agent(
team=[data_agent, analysis_agent],
instructions=["Always include sources", "Use tables to display data"],
show_tool_calls=True,
markdown=True,
)response = agent_team.run("Provide a comprehensive report on Agentic AI.")
print(response)
How You Can Start Today
- Dive into PhiData Documentation: Learn how to create and orchestrate agents.
- Explore GroqCloud Models: Choose from OpenAI GPT, LLaMA, or other options.
- Experiment: Begin with small projects and gradually scale up.
- Combine Strengths: Leverage GroqCloud’s scalability with PhiData’s orchestration tools.
Reflecting on my journey, it’s clear that Agentic AI isn’t just a technological advancement, it is a new paradigm. Tools like PhiData and GroqCloud make it possible to build systems that think, adapt, and collaborate. Whether you’re a developer, a researcher, or just curious about the future, now is the perfect time to explore what Agentic AI has to offer.
For more stories and insights on cutting-edge AI technologies, subscribe to my blog: TechWithHuz.
2025 Era of Agentic AI was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.
This content originally appeared on Level Up Coding - Medium and was authored by Huzefa Hamdard
Huzefa Hamdard | Sciencx (2025-01-15T16:50:05+00:00) 2025 Era of Agentic AI. Retrieved from https://www.scien.cx/2025/01/15/2025-era-of-agentic-ai/
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