Agentic AI: Turning understanding into practice (Episode 5)
Construire son premier agent avec Mistral AI from Nano Tips to make the most of Agentic AI by Omar Souissi
The first four episodes gave you the theory. Now it's time to act.
We explored how AI evolved from prediction to autonomy, we learned about different types of agents, we discovered the Model Context Protocol that connects agents to real tools, and we discussed what this means for the future of work.
But theory without practice is just information. To really benefit from agentic AI, you need to see the opportunities and know how to use them.
Why the LLM brain matters so much
At the heart of every agentic system is the Large Language Model. This is the brain that makes everything work. But the LLM doesn't just generate text. In agentic AI, it plays many different roles:
Perception: The LLM reads and understands data from different sources. It can analyze emails, documents, images, time series data, or customer feedback. It sees patterns that humans might miss.
Decision-making: Based on what it perceives, the LLM decides what to do next. Should it fetch more data? Should it call a specific tool? Should it ask for human help? These decisions happen in real-time, making the agent truly autonomous.
Reasoning across domains: The same LLM can work with financial data in the morning and computer vision tasks in the afternoon. It can analyze sales trends, process images, or understand natural language. This flexibility is what makes modern agents so powerful.
The LLM is not just one component. It's the intelligence that ties everything together.
Orchestration: Making agents work together
A single agent can do useful work. But real power comes when multiple agents collaborate.
This is orchestration. One agent handles data collection. Another does analysis. A third makes decisions. A fourth communicates results. They work together like a team, each doing what it does best.
But someone needs to coordinate this team. That's the orchestrator, often another AI agent that:
* Assigns tasks to the right agents
* Manages the flow of information
* Handles errors when something goes wrong
* Ensures everyone works toward the same goal
Good orchestration turns simple agents into powerful systems.
Communication: How agents talk to each other
For orchestration to work, agents need to communicate clearly.
In traditional software, programs pass data in fixed formats. But AI agents are different. They use natural language, structured messages, and shared memory to exchange information.
This creates new possibilities:
* Agents can explain their reasoning to each other
* They can negotiate and compromise
* They can share context and learn from each other's mistakes
* They can work together even if they weren't specifically designed to
The Model Context Protocol we discussed in Episode 3 makes this communication standardized and reliable.
Opening to external tools: The real game changer
An agent that only thinks is limited. An agent that can act in the real world is transformative.
This is where external tools come in. Through protocols like MCP, agents can:
* Access databases and retrieve information
* Send emails and messages
* Update spreadsheets and documents
* Call APIs and web services
* Control other software and systems
* Make purchases or schedule appointments
The key insight: you don't need to build these connections yourself. Modern agentic platforms already connect to thousands of tools. Your job is to recognize which tools solve your specific problems.
From theory to practice: A practical course
I created a LinkedIn Learning course to bridge this gap between understanding and doing: Astuces nano pour tirer profit de l'IA agentique.
The course uses short videos to show real opportunities. Each video focuses on one practical scenario where agentic AI creates real value. The format is simple: watch a quick lesson, see the opportunity, try it yourself.
Here's what the course covers:
Understanding perception and decision: How LLMs analyze different types of data and make smart choices. You'll see examples with time series forecasting, image analysis, and document processing.
Setting up communication: The patterns that make agent collaboration work. You'll learn when agents should share information and when they should work independently.
Connecting to external tools: Practical demonstrations of agents using real tools: databases, APIs, automation platforms, and productivity apps. You'll see which tools matter most and how to connect them quickly.
Recognizing opportunities: The most important skill is knowing where agentic AI can help. The course shows you how to look at your daily work and spot the patterns where agents can make a difference.
This isn't about theory. It's about seeing possibilities and knowing how to capture them.
Starting today
You don't need to wait for permission or perfect conditions. You can start experimenting with agentic AI right now:
Pick one repetitive task you do every week. Ask yourself: could an agent handle this? If you're not sure, that's okay. Try anyway.
Use existing platforms. Tools like Claude with computer use, n8n, or Zapier already have agentic capabilities built in. You don't need to code from scratch.
Start small. Don't try to automate your entire workflow on day one. Automate one small piece. Then add another. Each small win builds your confidence.
Think about the full cycle. Remember: perception → decision → action → communication. When you design an agentic solution, consider all four parts.
Share what you learn. When you discover something that works, tell your colleagues. Teaching others helps you understand better.
What makes this moment special
We're at an interesting point in AI history. The technology is powerful enough to be genuinely useful, but still accessible enough that anyone can learn it.
Five years ago, building agentic systems required deep technical expertise. Today, you can create powerful agent workflows with visual tools and natural language instructions.
But this window won't stay open forever. As agentic AI becomes mainstream, the early advantage goes to people who start learning now.
The tools are ready. The opportunities are real. What you do next is up to you.
Continue learning:
- Astuces nano pour tirer profit de l'IA agentique – Short practical videos showing real opportunities
- Previous episodes of this series for the conceptual foundation
- Try Claude.ai or similar platforms to experiment with agentic capabilities
This series started with theory. It ends with an invitation to practice. The best way to understand agentic AI is to use it. Start small. Try things. Build something.
** Start small.
** Try things.
** Build something.
The future belongs to people who act, not just people who understand.
Welcome to your agentic journey.
Labels: Artificial intelligence, Technology, Motivation, Learning
