Coder Legion - Recent posts tagged ai-agent

Can we learn Agentic AI by playing games?

Thu, 29 Oct 2026 05:46:33 +0000

Current educational platforms focus mainly on content

However, the problem with content is that, no matter how good the content is, it's a passive medium for learning.

A proven way to learn is to participate actively.

With that in mind, we are developing an educational game: AI Detective - a story-based game to learn Agentic AI.

We will be releasing it soon.

If you need early access, send a message in a DM or comment.

Let's build a multi-AI agentic system for customer support using CrewAI

Wed, 02 Sep 2026 06:44:13 +0000

Most AI coding tutorials explain what to write. This one explains why every line exists.

In this code walkthrough video, we will build a multi-AI agentic system for customer support from scratch while understanding

every import,
tool calling, prompt,
every Agent component,

step by step.

The explanation style remains the same: humour, simple analogies and real-world examples.

Build an Email Assistant AI Agent with LangChain

Tue, 17 Mar 2026 05:11:09 +0000

Let's build an AI Agent for email assistance using LangGraph.

LangGraph is different from other frameworks like Autogen or CrewAI.

Firstly, the Agent can identify whether the incoming email requires a reply or its just for the information.

If it requires a reply, it will generate the reply and save it in your draft folder.

The agent can also access your calendar and schedule meetings as per your availability.

Let's dive in.

Code Lab - https://lnkd.in/grbe8cQ9

Full Video - https://lnkd.in/guivDQrW

About "AI ML etc."

We have reimagined AI education for senior IT professionals and specifically designed AI courses/ programs for them.

If you have 10+ years of IT experience and would like to learn to lead AI initiatives confidently, this course is for you!!

These courses are production-focused, relevant, hype-free, and short.

Learners from 160+ reputed organisations like Microsoft, Nvidia, Aricent, Infosys, Maersk, Sapient, Oracle, TCS, Genpact, Airtel, Unilever, Vodafone, Jio, Sterlite, Vedanta, iDreamCareer and more have taken our courses and attended our lectures

More details here - https://lnkd.in/grF-8hh8

Hackathon Highlights: Building and Livestreaming an AI Travel Agent

Mon, 12 Jan 2026 19:38:02 +0000

In this week's article, we dive into a sponsored hackathon where we build an AI-powered travel agent app—competing, collaborating, livestreaming, and more! Follow along to learn real-world dev skills!

Hackathon AI Agent Challenge

To kick off 2026 and continue my self-taught developer journey, I’m once again joining Shashi Lo’s Gridiron Survivor team—an apprenticeship program that gives devs hands-on, pro-level experience. This time, we're taking on a hackathon challenge to build an AI agent!

The hackathon features two rival teams competing to develop an AI-powered travel agent app, sponsored by GitKraken, Frontend Mentor, and Zephyr Cloud. Up for grabs: a $1000 cash prize, one-year free memberships, and plenty of swag! This hackathon is underway now and will continue into February (and may extend if additional time is needed).

While it’s a contest, the real win for everyone is the shared learning experience, the sense of team camaraderie, and—my favorite—the chance to keep building an online presence.

On a personal note, I’m especially excited for this hackathon because my nephew David Terzano—fresh out of college with a degree in computer science and a passion for game development—is joining my team! This event gives David and all participants real-world team experience to help close the gap that new devs face when seeking their first roles. I can’t wait to see him jump in, learn with us, and bring fresh ideas to the project!

AI Agent, Travel Agent

The challenge is to build a travel agent app powered by a specialized AI agent that serves as the core of our hackathon project. A successful AI travel agent should efficiently integrate with multiple APIs to provide accurate travel information, automatically suggest itineraries, and personalize travel plans based on user preferences. Our desired outcome includes robust handling of user queries and consistent performance under various use-case scenarios.

Speed: Each team chooses its own tech stack, which enables efficient development and performance optimization to meet specific project demands.
Flexibility: Micro front-end architecture enables modular development, making it easier to update sections of the app independently.
Integration: API integrations are key to the app pulling real-time travel data and resources, ensuring accurate, timely information.
Intelligence: Custom AI models are used to analyze user data and preferences, providing personalized travel suggestions and enhancing the user’s experience.

These features and more highlight the complexity and potential of our AI travel agent app!

Livestream Presentations

To truly level up like the pros, team members will be hosting live presentations. Taking the floor to present, demo, and field questions is a valuable skill in itself, especially when navigating inevitable technical difficulties.

As a first step to engage with our journey, subscribe to the Coding with Shashi YouTube channel and turn on notifications to stay updated on all events. This ensures you won't miss any opportunities to follow along, ask questions, and provide feedback live.

In upcoming articles, I’ll take you behind the scenes of this hackathon, sharing our process, challenges, and key takeaways. You’ll be able to follow our journey step by step—from the initial setup and development to our results and lessons learned. Beyond building an AI agent, there’s a lot happening under the hood, and I can’t wait to cover it all!

Gridiron Survivor Sponsors

A very special thanks to our sponsors!

GitKraken: A popular Git client that provides a graphical interface to manage Git repositories. It is known for its user-friendly design and features that simplify version control, making it easier for developers to collaborate and manage their code.
Frontend Mentor: An online platform that offers front-end coding challenges. It helps developers improve their skills by providing real-world projects to work on and a supportive community for feedback and learning.
Zephyr Cloud: A Module Federation platform that accelerates micro-frontend development by providing instant deployment, intelligent dependency resolution, and seamless version management—all while remaining cloud agnostic, framework agnostic, and bundler agnostic.

Be sure to listen to the HTML All The Things Podcast!

I also write articles for the HTML All The Things Podcast, which you can read on their website: https://www.htmlallthethings.com/.

Be sure to check out HTML All The Things on socials!

I’m excited to announce that I’m now a Torc Ambassador! As part of this global community, I’ll be sharing valuable insights from Torc’s virtual events, lessons from my own tech journey, and real-world perspectives on how teams are hiring and working today. I’ll be active in the Torc community through posts, events, Q&A sessions, and more—so stay tuned for updates and opportunities to connect. Whether you’re looking to advance your tech career or simply stay informed, I hope the insights I share will support and inspire you on your journey!

Torc is an AI-powered platform and vibrant global network that connects skilled professionals with top opportunities and forward-thinking companies. If you’re interested in joining, you can create a free profile using my referral link: https://platform.torc.dev/#/r/b5QZ7k5h/cp

Affiliate & Discount Links!

With CodeMonkey, learning can be all fun and games! CodeMonkey transforms education into an engaging experience, enabling children to evolve from tech consumers to creators. Use CodeMonkey's FREE trial to unlock the incredible potential of young tech creators!

With a structured learning path tailored for various age groups, kids progress from block coding to more advanced topics like data science and artificial intelligence, using languages such as CoffeeScript and Python. The platform includes features for parents and teachers to track progress, making integrating coding into home and classroom settings easy.

Through fun games, hands-on projects, and community interaction, CodeMonkey helps young learners build teamwork skills and receive recognition for their achievements. It fosters a love for coding and prepares children for future career opportunities in an ever-evolving tech landscape.

To learn more about CodeMonkey, you can read my detailed review article!

Affiliate Links:

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Conclusion

A hackathon is a fantastic way to learn web development and build real team experience. This challenge lets us work with cutting-edge technologies and showcase how an AI agent is built from the ground up. Whether you’re a participant or following along, you’ll see how competition, collaboration, and livestreaming all come together—plus pick up valuable, in-demand skills!

We’ll take you behind the scenes as we choose our tech stack, tackle real-world problems, and share the tools and approaches we use. Watch our livestreams to see the action unfold, and join the conversation to connect, ask questions, and get involved. You’ll find inspiration, insights, and practical tips for your own projects.

Curious how it all comes together in real time? Follow our journey for updates, lessons learned, and strategies you can put to use in your own projects—so you gain real value as we build and learn together!

Let’s connect! I’m active on LinkedIn and Twitter.

IBM's AI agents for Oracle Fusion Applications showcase the power of Agent Mesh.

Thu, 16 Oct 2025 21:34:43 +0000

The Agent Mesh: Building the Integration Layer for the AI Renaissance

Artificial Intelligence is no longer a standalone tool—it’s evolving into ecosystems of interconnected systems working together to streamline workflows, solve complex problems, and unlock unprecedented efficiencies. Enter the era of the Agent Mesh: a collaborative network of specialized AI agents designed to bring the AI Renaissance to enterprise workflows.

This concept is no longer just theoretical. Recent developments, like IBM’s announcement of three new AI agents for Oracle Fusion Applications, show how fast we’re moving toward a world where agentic AI systems are transforming the integration layer of modern enterprises.

In this post, we’ll explore:

What the Agent Mesh is and why it matters.
How IBM’s AI agents for Oracle Fusion Applications embody this concept.
What this means for developers building the future of enterprise AI.

What Is the Agent Mesh?

The Agent Mesh is the interconnected layer of AI agents that work collaboratively across systems, automating workflows and enabling smarter integrations. Think of it as a network of specialized bots, each performing targeted tasks while communicating with one another to optimize processes.

This isn’t just automation—it’s collaboration at scale. Agents in the Mesh aren’t siloed; they leverage shared data, coordinate actions, and adapt to user needs in real time. They’re like a team of experts, each with their own specialty, working together seamlessly.

For developers, this means building systems where AI agents act as modules that integrate with broader workflows and data ecosystems. The Agent Mesh is the future of AI-driven enterprise architecture, and it’s coming faster than you think.

IBM’s AI Agents: A Case Study in Agent Mesh

IBM’s recent announcement of three new AI agents for Oracle Fusion Applications is a perfect example of the Agent Mesh in action. Let’s break it down:

The Agents

1️⃣ Intercompany Agent: Automates reviews of intercompany agreements, reducing errors and manual effort.
2️⃣ Smart Sales Order Entry Agent: Simplifies the order-to-cash process by generating required sales order data.
3️⃣ Requisition to Contract Agent: Streamlines procurement workflows by converting purchase requisitions to contract orders.

These agents are built using Oracle AI Agent Studio and are validated within Oracle’s ecosystem, ensuring seamless integration with existing enterprise workflows.

Watsonx Orchestrate: The Multi-Agent Supervisor

What makes these agents truly powerful is their multi-agent coordination enabled by IBM watsonx Orchestrate. Acting as the conductor in this orchestra of bots, watsonx Orchestrate ensures these agents don’t operate in silos but instead collaborate across Oracle and non-Oracle systems.

For developers, this highlights the importance of designing agents that can interoperate with diverse platforms. It’s no longer just about creating a single-use AI tool—it’s about building agents that work together and adapt within a dynamic ecosystem.

Why Developers Should Care

1. The Integration Layer Is Key

As enterprises embrace AI, the integration layer will become the backbone of business operations. Developers will need to build agents that seamlessly connect with APIs, databases, and other tools to create cohesive workflows.

2. Collaboration Is the Future

The Agent Mesh demands that developers think beyond individual tools and focus on how agents interact with each other. This requires expertise in multi-agent systems, governance frameworks, and cross-platform interoperability.

3. The Demand for AI-First Applications Is Growing

As companies like IBM and Oracle continue to push boundaries, the demand for AI-first enterprise applications will skyrocket. Developers who understand how to design, deploy, and manage interconnected agents are poised to become indispensable.

Putting It All Together

The Agent Mesh isn’t just a concept anymore—it’s here, shaping the future of enterprise AI. IBM’s AI agents for Oracle Fusion Applications demonstrate the power of interconnected systems to transform workflows and unlock new efficiencies.

For developers, this is an exciting challenge. Building the Integration Layer for the AI Renaissance means creating systems that are not only intelligent but also collaborative and scalable.

What are your thoughts on Agent Mesh and agentic AI? How are you preparing to build the next generation of AI-driven applications? Let’s discuss in the comments below!

Building you first AI Agent for a Task Organizer with Still.js and Groq

Sat, 19 Jul 2025 16:07:28 +0000

AI is now widely used for solving various problems, especially in the tech industry and among software developers. This article explores a specific use case of AI, focusing on certain key aspects while keeping the end-user in mind.

In summary, the task organizer is a software tool designed to help users manage tasks over various timeframes. While powerful Project/Task Management solutions like Motion and ClickUp exist, here we’ll demonstrate the capabilities of Still.js by discussing and building a small PoC covering a specific and tiny aspect.

AI Agent vs Agentic AI

According to google AI Overview "AI agents are specialized tools designed for specific, well-defined tasks, while Agentic AI represents a broader concept of autonomous, goal-driven systems that adapt to changing situations, and coordinate actions with minimal human oversight"

From Generative AI to Generative UI

This concept involves generating UI dynamically based on user prompt. Different prompts produce different UI components. In our case, we’ll handle it using a client-side approach.

How our Agent will it work essentially?

The user will write a text with the tasks he'll do, specifying what, how and when
Content is submitted to the Agent/LLM, which generates task(s)
UI parses LLM response and decides, how/what predefined component to render/present
User can then ask the agent to mark tasks as completed.

Different points of the design systems are addressed in here, for the implementation there is a “hands-on youtube video” where a tiny implementation is built from scratch. Bellow is the design system depicting an overview of the solution.

We’ll consider essentially 3 main parts, the AI provider, a custom Backend API, and the UI which we describe as follow:

AI Provider supplies intelligent capabilities

Backend provides a robust and secure integration with the AI, also serves the Frontend

Frontend handles user input and displays the AI's results.

In this use case, Still.js features like runtime form generation and centralized form validation are leveraged to manage task completion. The structure includes three components:

Home (main component),
TaskDay (group of tasks),
Task (individual tasks). Tasks report back to the Home component as form, enabling them to be marked as completed.

Groq infrastructure is what we'll use for LLM, however other AI providers like Google Gemini, ChatGPT, Copilot, LLaMa, or even offline/on-prem options like Ollama could also be used.

Prompts are sent from the Still.js enabled UI to the AI engine via chat, mainly as text, but it could support voice or audio.

In production, long-term memory for LLMs or agents often requires connecting to external sources like vector databases, highlighting the need for a strong backend. Our agent will have a short-term memory which will be handle as the bellow design:

Tool use and workflow management are key in Agentic AI, enabling both agency (thinking) and predictability (acting). For some tasks, a robust backend better supports these capabilities. The agent we'll build has moderate predictability.

In the demo, we’re connecting the UI straight to the AI engine for the sake of the size of the hands-on video tutorial, however this is also a valid scenario when using ephemeral API token.

Are you still here? Less talking, more doing, click here and follow the tutorial to build your first AI Agent.

See you there