De ChatGPT ao Devin, essa talk pretende elencar algumas IAs que estão cada vez mais aprimoradas, e que vão mudar (ou já estão mudando) drasticamente o cenário de algumas profissões nos próximos anos. Do programador ao jornalista, do profissional de marketing ao criador de vídeos, como utilizar ferramentas de IA ao seu favor e quais profissões correm risco de não existirem mais? É isso quê vamos descobrir, então não perde! 🤖

AI agents are everywhere, but how do you go from demo to dependable? In this talk, we’ll build agents using LangGraph, and take it beyond toy examples into enterprise-grade readiness.

We’ll start with context engineering concepts like: control flow, prompt engineering, prompt management and tool calling. From there, we’ll build an agent step-by-step, enabling contextual reasoning via Retrieval-Augmented Generation (RAG), and integrating tool use for dynamic task execution. We'll also cover agent observability with open source observability tools such as LangFuse to bring full observability into the decision our agents make.

Whether you're exploring AI agents or planning your first production deployment, you'll leave this session with the patterns, tools, and clarity to do it right.

Format: Technical talk with live demos, code and prompt examples

Description:

The role of the developer is fundamentally changing. We're moving from being executors who write every line of code to becoming orchestrators who conduct AI agents to build complex systems. This talk, based on my essay about transitioning from traditional coding to AI orchestration, shares practical insights from a year of experimenting with multi-agent development workflows. 🔗 Essay linked here: https://pivotech.substack.com/p/from-executor-to-orchestrator-my

Through real code examples and live demonstrations, I'll walk through my evolution from using ChatGPT for learning CS50 concepts to orchestrating Claude, Gemini CLI, and NotebookLM to build complete products. You'll discover the three distinct schools of AI development I've identified through hands-on experimentation: the One-Shot method, the Incremental approach, and my hybrid Layering technique.

I'll share the workflows I use to go from customer discovery sessions to deployed applications, including the mistakes, frustrations, and breakthroughs that shaped my approach. We'll explore the "Legacy Codebase Problem" that emerges from AI-generated code, the "Hyper-specificity Paradox" of detailed prompting, and the new skill set required to become an effective AI orchestrator.

Key Takeaways:

Three proven patterns for AI-assisted development and when to use each

Practical orchestration workflows for complex projects

The emerging skillset of the developer-orchestrator

How to maintain technical depth while leveraging AI efficiency

Real-world pitfalls and how to navigate them

Target Audience: Developers looking to evolve their practice in the age of intelligent agents. Minimal level of AI development experience required e.g. prompting Claude

Problem: Traditional AI application development often involves juggling multiple tools, APIs, and frameworks, leading to inefficiencies, complexity, and limited scalability. Developers are stuck in outdated paradigms that struggle to keep up with the growing demands of modern AI solutions.

Solution: The Model Context Protocol (MCP) offers a transformative approach to AI app development—essentially function calling on steroids. MCP represents a paradigm shift by streamlining the integration and management of core functionalities like file systems, databases, and other essential services through its innovative framework. By enabling seamless connections and robust functionality, MCP revolutionizes how developers build and deploy smarter AI-driven applications.

Description: This hands-on workshop empowers JavaScript developers to explore all facets of MCP development. Participants will learn to build MCP clients, set up MCP servers, and leverage existing MCP servers to create robust AI applications. Through practical exercises, you'll discover how MCP simplifies development, boosts efficiency, and delivers smarter AI-powered solutions. You'll also gain insight into how MCP's groundbreaking function-calling model enhances workflows, allowing seamless management of tasks such as file systems, databases, and more.

Key Takeaways: - Master the fundamentals of MCP and its innovative function-calling approach. - Build and configure MCP clients and servers from scratch. - Explore how to utilize existing MCP servers to streamline development tasks. - Gain hands-on experience in transforming traditional AI workflows into scalable, efficient models.