Agentic AI: The game changer already transforming how we work (Episode 1)

Artificial Intelligence has gone through several revolutions, and the next one is happening now.

We’ve shifted from prediction, where algorithms forecast outcomes, to generation, where models create text, images, and code. Now, we’re entering the age of automation and autonomy, where intelligent systems can plan, act, and learn on their own.

That’s the promise and power of Agentic AI.

If predictive AI focused on insight and generative AI focused on creativity, then Agentic AI emphasizes decision-making and action. It’s no longer just a tool that answers; it’s a collaborator that thinks.

The brain behind the agent

At the heart of every Agentic AI system is a Large Language Model (LLM) that acts as the brain. It interprets goals, reasons about context, and organizes the next best actions.

Other components act as the senses and hands. They collect data, carry out actions, and send results back to the model. Together, they create a closed cognitive loop, giving AI agents a sense of situational awareness.

The Agentic flow: perception, reasoning, action, learning

Agentic AI works through a continuous and adaptive cycle:

• Perception : sensing and analyzing data from the environment.
• Reasoning : the LLM evaluates objectives, plans steps, and makes decisions.
• Action : the agent carries out those plans using digital or physical tools.
• Learning : the system observes outcomes, adjusts strategies, and improves.

This flow transforms static AI into a living, evolving system capable of managing complex, changing environments.

The ecosystem powering Agentic AI

Building and coordinating autonomous agents is now possible thanks to a fast-growing set of tools:

• LangChain : connects LLMs to APIs, data sources, and logic blocks, allowing for context-aware reasoning and dynamic tool use.
• LangGraph : builds on LangChain with a graph-based structure that organizes agentic workflows, enabling loops, branching logic, and multi-agent coordination.
• Zapier : connects agents to thousands of real-world applications, including email, Slack, spreadsheets, and CRM systems.
• n8n : an open-source option for secure and customizable automation flows, giving developers full transparency and control.

These platforms create the infrastructure that lets the LLM “brain” interact smartly with its environment, perceiving, reasoning, and acting in real time.

Why It’s a game changer

We are already seeing the effects across various industries:

• Manufacturing : predictive agents identify and fix issues before they disrupt production.
• E-commerce : autonomous recommender agents create tailored experiences on the fly.
• Energy : exploration agents optimize drilling operations and resource use.

Benchmarks show a leap: agentic frameworks can raise model performance from around 67% to over 90% on complex reasoning tasks.

That’s not evolution; it’s transformation.

A new era of intelligent collaboration

As these systems gain autonomy, responsibility and governance become crucial. Agentic AI should not replace human intelligence but enhance it, creating a new partnership between humans and digital minds.

What’s next ?

This post marks the start of a detailed exploration into the world of Agentic AI. In upcoming articles, we’ll cover:

• The different levels of reasoning that make agents truly intelligent from reflexive reactions to strategic thinking.
• How agents communicate with tools through the Model Context Protocol (MCP).
• How agents collaborate with one another.

Each layer will show how autonomy, communication, and learning combine to shape the next generation of intelligent systems.

Here is a Link to a minimal example of how to use Mistral with Python and LangChain

So stay tuned; the era of Agentic AI is not on the way. It’s already here, changing how we create, decide, and act.

Pydantic: Your new data bodyguard

Picture this: you order a burger online. You’re expecting something juicy and delicious... but the delivery guy hands you a necktie instead 😵

Without Pydantic, that’s pretty much daily life for our Python functions. You expect an int, but you get a str that looks like a number or worse, None. The code crashes at runtime, and you waste hours debugging TypeErrors or, even worse, silent bugs.

Pydantic is the strict bodyguard standing at the door of your function, API, or data pipeline. It says:
“Show me what you’ve got. I’ll check it, convert it if I can, and hand it back in exactly the format you expect.”

Why is it so brilliant? A quick example says more than a thousand words (compatible with Pydantic v2):

In the attached example, Pydantic has:

• Validated types, name is a string, score is a positive integer.
• Parsed the string "1995-04-12" into a native Python date object automatically.
• Guaranteed that your data is safe and matches your expectations. If score had been -10, it would’ve raised a clear, immediate error, saving you from a potential bug.

Why should every Pythonista know it?

• 🕒 Time Saver: No more miles of if isinstance(...) checks. Validation becomes declarative.
• ✅ Confidence: You can fully trust the shape and type of your data once it’s passed through the Pydantic gate.
• 🌍 Universal Pivot: It’s everywhere, the standard for FastAPI, essential for configuration, data parsing, and beyond.

It’s not just a library, it’s a shift in mindset: declare your data shape, and let the machine handle the grunt work.

P.S. If you’re passionate about data quality and cleaning (because having a “bodyguard” is great, but preparing your data upstream is even better 😉), check out my course on #LinkedInLearning:
👉 https://lnkd.in/eXegxieF

Do you already use Pydantic? What’s your favorite feature or your best tip to get the most out of it?

#Python #Pydantic #Development #BestPractices #CodeQuality #FastAPI #DataEngineering #DataCleaning #DataQuality

Lean Six Sigma: The best ally for a successful agentic AI rollout?

AI is moving at lightning speed, and one of its most promising developments is agentic AI: systems that can plan and act autonomously, often across multiple steps. Exciting, right? But enthusiasm without discipline can be risky. Budgets explode, errors multiply, systems become fragile. This is where Lean Six Sigma (LSS) comes in.

What exactly is agentic AI?

Unlike traditional AI that simply responds to instructions, agentic AI acts like an autonomous actor. Imagine an assistant capable of reorganizing schedules, approving decisions, or optimizing complex workflows without immediate human intervention.

Impressive, but potentially dangerous if processes aren’t clearly defined. A single autonomous decision can create a domino effect of mistakes. Lean Six Sigma provides the structure needed to prevent this.

Lean Six Sigma: more than just approach

Lean Six Sigma is a mindset of continuous improvement and a toolbox full of techniques to map processes, eliminate waste, measure performance, and improve quality.

For illustration, we’ll reference DMAIC (Define, Measure, Analyze, Improve, Control), a widely used and easy-to-follow framework. But remember, Lean Six Sigma also includes Value Stream Mapping, SIPOC, Kaizen, 5S, poka-yoke, FMEA, control charts, and more. DMAIC is just one way to structure improvement within the broader LSS mindset.

Why managers love Lean Six Sigma

• Clarity and structure: helps organize work and visualize impact.
• Small changes, big effects: targeted improvements compound to create real value.
• Applicable everywhere: industry, services, healthcare, construction... any organization has processes and variability.
• Change management made easy: teams see tangible results, which encourages adoption.
• Proven track record: countless successes in industrial, hospital, and financial projects.

How LSS supports agentic AI

So how does Lean Six Sigma help protect and optimize agentic AI projects? Let’s break it down:

1. Choosing the right problems

LSS ensures AI projects focus on measurable customer value, not flashy but low-impact initiatives.

2. Ensuring data quality

Good AI needs good data. Six Sigma tools identify inconsistencies and clean inputs before model training.

3. Reducing variability

Lean Six Sigma exposes inconsistent practices and process gaps, producing more reliable AI outputs.

4. Safe pilot design

Kaizen events and controlled pilots allow experimentation in a low-risk environment, with human-in-the-loop checks.

5. Risk and compliance management

FMEA and control plans anticipate agent failures and define safeguards before scaling up.

6. Driving adoption

Clear communication and visible wins build trust, ensuring the solution is used effectively.

7. Continuous monitoring and control

Dashboards, SOPs, and indicators detect drift or errors quickly, triggering corrective actions before they escalate.

8. Scaling what works

LSS encourages standardization and knowledge capture, turning successful pilots into repeatable, organization-wide practices.

To conclude

Before launching your next AI initiative, take time to map processes and apply the Lean Six Sigma mindset. The discipline you bring now will pay off many times over: safer systems, measurable gains, and lasting value.

✨ The Mindset to move forward ✨ الخير فيما اختاره الله ✨ A blessing in disguise

Sometimes, we have a perfectly drawn plan. A well-organized roadmap, clear objectives, and the conviction that everything will unfold as expected.

And then, a setback, a door that closes, an opportunity that disappears.
In those moments, we have two choices:

• Get upset, feel discouraged, sometimes even lose confidence and forget everything that was going well.
• Or take another perspective: “الخير فيما اختاره الله” — the good lies in what God has chosen for us.
  A non-believer would say: “Everything happens for a reason.”

This conviction changes everything. It turns frustration into gratitude, and uncertainty into trust.
It reminds us that behind every detour, there may be a better destination.

👉 The key is therefore not to resist change, but to embrace it as an opportunity to grow.

👉 What seemed like a loss can become a redirection toward something more right, more fulfilling, more aligned with who we are.

I particularly like the story of the king and his minister to illustrate this idea:

“ One day, while walking with his minister, the king cut his finger while handling his sword. Furious, he showed his wound to the minister. The latter calmly replied: « Your Majesty, this may be for the best. خير إن شاء الله » The king, offended, had him imprisoned.

Shortly afterward, the king went hunting and fell into the hands of tribes who still practiced human sacrifices. But upon seeing his wounded finger, they refused to offer him, as he was not ‘perfect.’

The king returned safe and sound, understood the wisdom of his minister, and freed him with apologies. The minister then said: « Even my imprisonment was for the best, because if I had been with you, I, who was not wounded, would have been sacrificed. » ”

It is therefore essential to be convinced that every detour is not the end of the path — it is simply a new course.
And sometimes, it is exactly what we needed, even if we do not yet see it.

Revolutionizing app development with LlamaCoder

In today's fast-paced tech landscape, the way we build applications is evolving at an unprecedented rate. At the forefront of this revolution is LlamaCoder, an AI-powered code generator that’s changing the game. With its latest version, Llama 3.1, developers can create full-stack applications from a simple prompt. This breakthrough not only redefines app development but also opens doors to a world where coding becomes more accessible, faster, and more efficient.

The power of a single prompt:

Imagine describing your dream app in just a few sentences and watching it come to life. That’s the power of LlamaCoder. With 45 billion parameters powering Llama 3.1, this tool deciphers complex instructions with remarkable precision, turning your concepts into fully functional applications—no matter how intricate or ambitious. It's a new era where the barrier between ideas and implementation melts away.

Redefining how we build:

In the past, developing an app meant dedicating countless hours to writing code, troubleshooting bugs, and testing functionality. With LlamaCoder, those days are numbered. Now, anyone—from seasoned professionals to beginners with a bright idea—can build apps without getting lost in technical details. By automating repetitive and routine coding tasks, LlamaCoder allows developers to focus on creativity, innovation, and solving the bigger problems that matter.

The Llama 3.1 advantage

What sets Llama 3.1 apart is its unmatched ability to generate high-quality code quickly and accurately. Backed by cutting-edge advancements in machine learning and natural language processing, LlamaCoder delivers on its promise of efficiency without sacrificing quality. The enhanced user experience is another standout feature—intuitive interactions guide developers every step of the way, making the process seamless and enjoyable.

Empowering the next wave of innovators

LlamaCoder is more than just a tool; it’s an invitation to a new generation of creators. By simplifying the development process, it encourages aspiring developers to dive into the world of coding without fear of complexity. At the same time, experienced developers can push the boundaries of what’s possible, testing new ideas and building innovative solutions with speed and confidence.

The rise of AI-powered code generators like LlamaCoder marks a pivotal moment in app development. The launch of Llama 3.1 represents a bold step toward a more inclusive and efficient future, where anyone can bring their vision to life. Whether you’re a veteran coder or just starting out, LlamaCoder unlocks limitless possibilities—try it and see where your imagination can take you.