Summary In this crossover episode, Max Beauchemin explores how multiplayer, multi‑agent engineering is transforming the way individuals and teams build data and AI systems. He digs into the shifting boundary between data and AI engineering, the rise of “context as code,” and how just‑in‑time retrieval via MCP and CLIs lets agents gather what they need without bloating context windows. Max shares hard‑won practices from going “AI‑first” for most tasks, where humans focus on orchestration and taste, and the new bottlenecks that appear — code review, QA, async coordination — when execution accelerates 2–10x. He also dives deep into Agor, his open‑source agent orchestration platform: a spatial, multiplayer workspace that manages Git worktrees and live dev environments, templatizes prompts by workflow zones, supports session forking and sub‑sessions, and exposes an internal MCP so agents can schedule, monitor, and even coordinate other agents.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Maxime Beauchemin about the impact of multi-player multi-agent engineering on individual and team velocity for building better data systemsInterview IntroductionHow did you get involved in the area of data management?Can you start by giving an overview of the types of work that you are relying on AI development agents for?As you bring agents into the mix for software engineering, what are the bottlenecks that start to show up?In my own experience there are a finite number of agents that I can manage in parallel. How does Agor help to increase that limit?How does making multi-agent management a multi-player experience change the dynamics of how you apply agentic engineering workflows?Contact Info LinkedInLinks AgorApache AirflowApache SupersetPresetClaude CodeCodexPlaywright MCPTmuxGit WorktreesOpencode.aiGitHub CodespacesOnaThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Explore how AI agents communicate, making decisions and sharing data that would otherwise stay locked in separate systems. You’ll see a high-level overview of the architecture behind these agent-to-agent interactions and the practical protocols that enable them to coordinate effectively. Learn how agentic systems solve real problems, from debugging complex applications to automating entire development workflows. Discover how this technology can reduce context-switching and cognitive load.

As enterprises move from simple prompts to complex, autonomous workflows, the challenge shifts to effectively integrating multiple components—such as different LLMs, external tools, and memory—into reliable, production-grade Agentic Applications. In this session, we’ll introduce FloTorch, a framework designed to simplify the development, management, and deployment of these complex AI agents. FloTorch provides the necessary structure and capabilities to integrate, observe, and secure agents across different model providers. We’ll begin with an overview of the core architectural challenges in building sophisticated agents-from managing conversational memory and orchestrating tool use, to ensuring reliability and performance across multiple LLM calls. Then, we’ll show how FloTorch provides a unified workflow for integrating different LLMs, various memory stores, and custom tools, helping teams prototype, benchmark, and operationalize Agentic Applications without technical debt. We will also highlight its built-in Observability features, including Traces and OpenTelemetry integration (where available), which are critical for debugging and monitoring agent behavior in production. A live demo will walk through building a scalable agent with FloTorch, highlighting how developers can add memory, define tool-use logic, and integrate crucial observability into their pipelines.

What We Will Cover:

• Key challenges in architecting and deploying autonomous AI Agents at scale
• How FloTorch enables seamless integration of different LLMs, Memory, and Tools
• Designing robust, multi-step agentic workflows for business use cases
• Implementing Observability (Traces, OpenTelemetry) to monitor and debug agent decisions
• Strategies for ensuring safety, reliability, and governance in agentic applications
• Real-world examples of business value driven by FloTorch-powered agents

Hands-On Insights:

Through live demonstrations and interactive Q&A, participants will gain hands-on insights into using FloTorch to rapidly build, evaluate, and operationalize complex Agentic Applications—with integrated tools for memory, LLM switching, and production-ready observability.

As enterprises move from simple prompts to complex, autonomous workflows, the challenge shifts to effectively integrating multiple components—such as different LLMs, external tools, and memory—into reliable, production-grade Agentic Applications. In this session, we’ll introduce FloTorch, a framework designed to simplify the development, management, and deployment of these complex AI agents. FloTorch provides the necessary structure and capabilities to integrate, observe, and secure agents across different model providers. We’ll begin with an overview of the core architectural challenges in building sophisticated agents-from managing conversational memory and orchestrating tool use, to ensuring reliability and performance across multiple LLM calls. Then, we’ll show how FloTorch provides a unified workflow for integrating different LLMs, various memory stores, and custom tools, helping teams prototype, benchmark, and operationalize Agentic Applications without technical debt. We will also highlight its built-in Observability features, including Traces and OpenTelemetry integration (where available), which are critical for debugging and monitoring agent behavior in production. A live demo will walk through building a scalable agent with FloTorch, highlighting how developers can add memory, define tool-use logic, and integrate crucial observability into their pipelines.

What We Will Cover:

• Key challenges in architecting and deploying autonomous AI Agents at scale
• How FloTorch enables seamless integration of different LLMs, Memory, and Tools
• Designing robust, multi-step agentic workflows for business use cases
• Implementing Observability (Traces, OpenTelemetry) to monitor and debug agent decisions
• Strategies for ensuring safety, reliability, and governance in agentic applications
• Real-world examples of business value driven by FloTorch-powered agents

Hands-On Insights:

Through live demonstrations and interactive Q&A, participants will gain hands-on insights into using FloTorch to rapidly build, evaluate, and operationalize complex Agentic Applications—with integrated tools for memory, LLM switching, and production-ready observability.

As enterprises move from simple prompts to complex, autonomous workflows, the challenge shifts to effectively integrating multiple components—such as different LLMs, external tools, and memory—into reliable, production-grade Agentic Applications. In this session, we’ll introduce FloTorch, a framework designed to simplify the development, management, and deployment of these complex AI agents. FloTorch provides the necessary structure and capabilities to integrate, observe, and secure agents across different model providers. We’ll begin with an overview of the core architectural challenges in building sophisticated agents-from managing conversational memory and orchestrating tool use, to ensuring reliability and performance across multiple LLM calls. Then, we’ll show how FloTorch provides a unified workflow for integrating different LLMs, various memory stores, and custom tools, helping teams prototype, benchmark, and operationalize Agentic Applications without technical debt. We will also highlight its built-in Observability features, including Traces and OpenTelemetry integration (where available), which are critical for debugging and monitoring agent behavior in production. A live demo will walk through building a scalable agent with FloTorch, highlighting how developers can add memory, define tool-use logic, and integrate crucial observability into their pipelines.

What We Will Cover:

• Key challenges in architecting and deploying autonomous AI Agents at scale
• How FloTorch enables seamless integration of different LLMs, Memory, and Tools
• Designing robust, multi-step agentic workflows for business use cases
• Implementing Observability (Traces, OpenTelemetry) to monitor and debug agent decisions
• Strategies for ensuring safety, reliability, and governance in agentic applications
• Real-world examples of business value driven by FloTorch-powered agents

Hands-On Insights:

Through live demonstrations and interactive Q&A, participants will gain hands-on insights into using FloTorch to rapidly build, evaluate, and operationalize complex Agentic Applications—with integrated tools for memory, LLM switching, and production-ready observability.

Join Amperity’s Marcus Owens, Lead Solution Consultant, to learn more about the rapid innovations in data architecture brought by the new wave of AI agents. This session will start with a quick overview of what makes a good AI Agent – and then focus on how Agentic strategies can accelerate two key needs in customer data: 

Make Customer Data Usable – How AI Agents accelerate customer data engineering with Amperity’s Stitch and Chuck Data – saving data engineering teams hundreds of hours of effort. 

Make Use of Customer Data – How AmpAI allows Marketers to build outcome-driven customer journeys, going from intent to results faster than ever before.

Agentic AI has emerged as a way to allow the LLMs themselves the ability to complete high-level tasks with limited to no manual intervention required by humans. This talk will examine these agentic systems, discussing what comprises an agent, how to build and orchestrate multi-agent systems, and what kinds of problems are best suited to agentic systems. This talk will also explore a new paradigm that’s emerging in industry today: vibe coding.

Agentic AI Protocols: Coordinating the Future of Autonomous Systems

As autonomous agents become more capable, coordinating their interactions with tools and with each other has become a critical challenge. In this session, we’ll introduce the emerging landscape of Agentic AI protocols — with a focus on MCP (Model Context Protocol), A2A (Agent-to-Agent Protocol), and ACP (Agent Communication Protocol). These AI protocols are laying the foundation for interoperability, scalability, and real-world adoption of agentic AI. We’ll begin with an overview of the multi-agent coordination challenge — why multiple agents require shared standards for communication, negotiation, and orchestration. Then we’ll explore how MCP, developed by Anthropic, provides a “USB-C for AI” that enables seamless integration with thousands of external tools. Building on this, we’ll look at A2A for direct agent-to-agent coordination and ACP for creating a common messaging language between heterogeneous agents. You’ll also see a live demo of Zapier’s MCP integration, showing how an AI agent can trigger multi-step workflows across 7,000+ apps — extended with a multi-agent scenario to illustrate how protocols complement each other in practice. Finally, we’ll compare MCP, A2A, and ACP, and discuss strategies for designing scalable, standards-based agent systems.

What We Will Cover:

• Understand the multi-agent coordination challenge and why protocols are needed
• Learn how MCP, A2A, and ACP address different aspects of agent interoperability
• See a live demo of Zapier’s MCP integration with an extended multi-agent workflow
• Explore design strategies for orchestrating multiple agents, including retries, delegation, and error handling
• Compare MCP vs. A2A vs. ACP — when to use each and how they work together
• Discover practical applications and future directions for Agentic AI protocols

Hands on Exercise:

Through live demos and audience Q&A, participants will gain hands-on insights into MCP-powered automation and explore how emerging protocols like A2A and ACP extend agent capabilities for real-world, multi-agent systems.

Agentic AI Protocols: Coordinating the Future of Autonomous Systems

As autonomous agents become more capable, coordinating their interactions with tools and with each other has become a critical challenge. In this session, we’ll introduce the emerging landscape of Agentic AI protocols — with a focus on MCP (Model Context Protocol), A2A (Agent-to-Agent Protocol), and ACP (Agent Communication Protocol). These AI protocols are laying the foundation for interoperability, scalability, and real-world adoption of agentic AI. We’ll begin with an overview of the multi-agent coordination challenge — why multiple agents require shared standards for communication, negotiation, and orchestration. Then we’ll explore how MCP, developed by Anthropic, provides a “USB-C for AI” that enables seamless integration with thousands of external tools. Building on this, we’ll look at A2A for direct agent-to-agent coordination and ACP for creating a common messaging language between heterogeneous agents. You’ll also see a live demo of Zapier’s MCP integration, showing how an AI agent can trigger multi-step workflows across 7,000+ apps — extended with a multi-agent scenario to illustrate how protocols complement each other in practice. Finally, we’ll compare MCP, A2A, and ACP, and discuss strategies for designing scalable, standards-based agent systems.

What We Will Cover:

• Understand the multi-agent coordination challenge and why protocols are needed
• Learn how MCP, A2A, and ACP address different aspects of agent interoperability
• See a live demo of Zapier’s MCP integration with an extended multi-agent workflow
• Explore design strategies for orchestrating multiple agents, including retries, delegation, and error handling
• Compare MCP vs. A2A vs. ACP — when to use each and how they work together
• Discover practical applications and future directions for Agentic AI protocols

Hands on Exercise:

Through live demos and audience Q&A, participants will gain hands-on insights into MCP-powered automation and explore how emerging protocols like A2A and ACP extend agent capabilities for real-world, multi-agent systems.

Agentic AI Protocols: Coordinating the Future of Autonomous Systems

As autonomous agents become more capable, coordinating their interactions with tools and with each other has become a critical challenge. In this session, we’ll introduce the emerging landscape of Agentic AI protocols — with a focus on MCP (Model Context Protocol), A2A (Agent-to-Agent Protocol), and ACP (Agent Communication Protocol). These AI protocols are laying the foundation for interoperability, scalability, and real-world adoption of agentic AI. We’ll begin with an overview of the multi-agent coordination challenge — why multiple agents require shared standards for communication, negotiation, and orchestration. Then we’ll explore how MCP, developed by Anthropic, provides a “USB-C for AI” that enables seamless integration with thousands of external tools. Building on this, we’ll look at A2A for direct agent-to-agent coordination and ACP for creating a common messaging language between heterogeneous agents. You’ll also see a live demo of Zapier’s MCP integration, showing how an AI agent can trigger multi-step workflows across 7,000+ apps — extended with a multi-agent scenario to illustrate how protocols complement each other in practice. Finally, we’ll compare MCP, A2A, and ACP, and discuss strategies for designing scalable, standards-based agent systems.

What We Will Cover:

• Understand the multi-agent coordination challenge and why protocols are needed
• Learn how MCP, A2A, and ACP address different aspects of agent interoperability
• See a live demo of Zapier’s MCP integration with an extended multi-agent workflow
• Explore design strategies for orchestrating multiple agents, including retries, delegation, and error handling
• Compare MCP vs. A2A vs. ACP — when to use each and how they work together
• Discover practical applications and future directions for Agentic AI protocols

Hands on Exercise:

Through live demos and audience Q&A, participants will gain hands-on insights into MCP-powered automation and explore how emerging protocols like A2A and ACP extend agent capabilities for real-world, multi-agent systems.

An overview of agentic AI and how to build agentic apps using the LlamaIndex framework. Covers core design patterns such as event-driven workflows, routing, parallelization, orchestrator–worker setups, and evaluator–optimizer loops, and discusses multi-agent systems. The session will explore MCP servers and tools for providing live context to agents, and uses the open-source LlamaIndex Python framework with models from OpenAI and Anthropic.

An overview of agentic AI concepts, focusing on how to build agentic apps with LlamaIndex. We'll cover core design patterns such as event-driven workflows, routing, parallelization, orchestrator–worker setups, and evaluator–optimizer loops, and show how to bring them to life in the LlamaIndex framework. The session also explores how these pieces fit into multi-agent systems, with a focus on MCP servers and tools that provide live context to agents. By the end, you'll learn to build agents using LlamaIndex, compose multi-agent systems, design reusable tools for agents, and give agents real-time knowledge. The talk uses the LlamaIndex Python framework and models from OpenAI and Anthropic.

Summary In this episode of the Data Engineering Podcast Arun Joseph talks about developing and implementing agent platforms to empower businesses with agentic capabilities. From leading AI engineering at Deutsche Telekom to his current entrepreneurial venture focused on multi-agent systems, Arun shares insights on building agentic systems at an organizational scale, highlighting the importance of robust models, data connectivity, and orchestration loops. Listen in as he discusses the challenges of managing data context and cost in large-scale agent systems, the need for a unified context management platform to prevent data silos, and the potential for open-source projects like LMOS to provide a foundational substrate for agentic use cases that can transform enterprise architectures by enabling more efficient data management and decision-making processes.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. This episode is brought to you by Coresignal, your go-to source for high-quality public web data to power best-in-class AI products. Instead of spending time collecting, cleaning, and enriching data in-house, use ready-made multi-source B2B data that can be smoothly integrated into your systems via APIs or as datasets. With over 3 billion data records from 15+ online sources, Coresignal delivers high-quality data on companies, employees, and jobs. It is powering decision-making for more than 700 companies across AI, investment, HR tech, sales tech, and market intelligence industries. A founding member of the Ethical Web Data Collection Initiative, Coresignal stands out not only for its data quality but also for its commitment to responsible data collection practices. Recognized as the top data provider by Datarade for two consecutive years, Coresignal is the go-to partner for those who need fresh, accurate, and ethically sourced B2B data at scale. Discover how Coresignal's data can enhance your AI platforms. Visit dataengineeringpodcast.com/coresignal to start your free 14-day trial. Your host is Tobias Macey and today I'm interviewing Arun Joseph about building an agent platform to empower the business to adopt agentic capabilitiesInterview IntroductionHow did you get involved in the area of data management?Can you start by giving an overview of how Deutsche Telekom has been approaching applications of generative AI?What are the key challenges that have slowed adoption/implementation?Enabling non-engineering teams to define and manage AI agents in production is a challenging goal. From a data engineering perspective, what does the abstraction layer for these teams look like? How do you manage the underlying data pipelines, versioning of agents, and monitoring of these user-defined agents?What was your process for developing the architecture and interfaces for what ultimately became the LMOS?How do the principles of operatings systems help with managing the abstractions and composability of the framework?Can you describe the overall architecture of the LMOS?What does a typical workflow look like for someone who wants to build a new agent use case?How do you handle data discovery and embedding generation to avoid unnecessary duplication of processing?With your focus on openness and local control, how do you see your work complementing projects like OumiWhat are the most interesting, innovative, or unexpected ways that you have seen LMOS used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on LMOS?When is LMOS the wrong choice?What do you have planned for the future of LMOS and MASAIC?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links LMOSDeutsche TelekomMASAICOpenAI Agents SDKRAG == Retrieval Augmented GenerationLangChainMarvin MinskyVector DatabaseMCP == Model Context ProtocolA2A (Agent to Agent) ProtocolQdrantLlamaIndexDVC == Data Version ControlKubernetesKotlinIstioXerox PARC)OODA (Observe, Orient, Decide, Act) LoopThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

In this talk, we will show that agents can be represented as MCP servers, allowing them to be run from any MCP client (such as Claude, Cursor and other applications).

This is made possible with mcp-agent, a simple, composable framework to build agents using Model Context Protocol.

Overview

Currently "agentic" behavior exists only on the MCP client side – clients like Claude or Cursor use MCP servers, which are often simple tool APIs, to solve tasks.

However, if Agents are MCP servers themselves, then any MCP client can invoke, coordinate and orchestrate agents the same way it does with any other MCP server.

This paradigm shift enables: 1. Agent Composition: Build complex multi-agent systems over the same base protocol (MCP). 2. Platform Independence: Use your agents from any MCP-compatible client 3. Scalability: Run agent workflows on dedicated infrastructure, not just within client environments 4. Customization: Develop your own agent workflows and reuse them across any MCP client.

Background

mcp-agent was inspired by 2 foundational updates that Anthropic introduced for AI application developers:

1. Model Context Protocol - a standardized interface to let any software be accessible to AI assistants via MCP servers.

2. Building Effective Agents - a seminal writeup on simple, composable patterns for building production-ready AI agents.

mcp-agent puts these two foundational pieces into an AI application framework:

1. It handles the pesky business of managing the lifecycle of MCP server connections.

2. It implements every pattern described in Building Effective Agents, and does so in a composable way, allowing you to chain these patterns together.

Now as MCP continues to grow adoption, we are exploring advanced agent architectures that allow for sophisticated workflows in simple ways.

This Meetup is presented by our friends from AI Camp Berlin. For more information and to help us keep track, please register via the event page of AICamp with this Link

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​From Zero to Agentic: Mastering Workflow Automation with n8n

FOR JOINING LIVE: Just go to https://youtube.com/@AgentGeeks on 11th June 2025 at 6:30 PM CEST.

Duration: 3 hours Location: Berlin, Germany Target Audience: Developers, automation enthusiasts, startup founders, and operations professionals interested in leveraging n8n for advanced workflow automation.

Masterclass Overview This masterclass is designed to take participants from the fundamentals of n8n to the advanced realm of agentic workflow automation. Participants will learn how to build intelligent, adaptive workflows that can make decisions, adapt to new situations, and autonomously achieve goals using n8n.

Agenda 18:00 Check-in & networking 18:30 What is new in n8n? By Max Tkacz 18:40 Beyond the Demo: Building AI Agents for Production. By Joanna Stoffregen. 19:00 n8n Masterclass by Aemal Sayer 22:00 Q&A, Networking & Closing

About the Masterclass In the Masterclass Aemal will first give a detailed lecture about n8n basics. Then he will give a step by step tutorial about how to build an AI Agent that connects with your meeting transcriber, in this case being Bluedot and build a workflow that composes an email right after your call is completed.

Whether you're technical or not, this event will give you the mental models and best practices to build better AI systems.

Prerequisites

• Basic understanding of workflow automation concepts.
• Familiarity with APIs and data formats (e.g., JSON).

Resources Provided

• Access to the masterclass materials and example workflows.
• List of recommended readings and tutorials for further learning.
• Tutorial recorded by Aemal Sayer

About the Facilitators Max Tkacz is a seasoned product builder with over a decade of experience in UX, now leading Developer Relations at n8n. As the mind behind The Studio, he shares human-written insights and showcases the potential of low-code AI automation with a fast-growing global community.

Joanna Stoffregen is the founder of Labsbit.ai, a Gen-AI product development studio based in Berlin. With deep experience building AI agents, automation tools, and voice AI systems, she brings sharp insight and high energy to every tech gathering. She's also an active community builder with AICamp Berlin, where she curates AI meetups that connect innovators across the city. At this event, Joanna will guide the conversation, keep the energy high, and ensure everyone walks away inspired and informed.

Aemal Sayer is a Berlin-based AI engineer and tech entrepreneur specializing in building AI agents for SMEs and scaleups. With a focus on automating complex workflows without the need for APIs (aka. computer use), Aemal has delivered transformative AI solutions for clients like Klarna, Siemens, and Allianz. His expertise lies in end-to-end AI agent development, LLM integration, and ensuring compliance with EU AI regulations.

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Please register via the event page of AICamp with this Link

📹 NOTICE OF VIDEOGRAPHY & LIVESTREAMING / HINWEIS ZUR VIDEOAUFZEICHNUNG & LIVESTREAM

Please be advised that this event is being recorded and livestreamed. Video and photos may be used for commercial purposes, including but not limited to: online marketing, YouTube, promotional material, and social media. By entering the event space, you consent to the use of your image and voice in such media. If you prefer not to appear in recordings, you are welcome to watch the livestream instead via our YouTube channel: 👉 https://www.youtube.com/@AgentGeeks

On 2025-04-17, we will have 3 amazing speakers to share with you how AI can help you solve problems you may have. And thank you very much to Google Berlin for hosting us this time. Reserve your seats now and see you very soon!

Agenda

5:30 PM: Doors open, Registration, Dinner and Drinks

6:30 PM: Build with Gemini – Learn about Gemini's multimodal and agentic capabilities" + Q&A by Patrick Löber

Gemini 2.0 and 2.5 are the latest foundational models released by Google DeepMind, offering multimodal understanding, realtime interactions, text-to-image generations, thinking, and tool support for agentic use cases. In this talk you'll get an overview of Gemini's capabilities, learn about prototyping with Google AI Studio, and how to build with Gemini models.

7:00 PM: "Applied NLP in the Age of Generative" + Q&A by Ines Montani

Large Language Models (LLMs) and in-context learning have introduced a new paradigm for developing natural language understanding systems: prompts are all you need! Prototyping has never been easier, but not all prototypes give a smooth path to production. In this talk, titled "Reality is Not an End-to-End Prediction Problem: Applied NLP in the Age of Generative AI," I'll share the most important lessons we've learned from solving real-world information extraction problems in industry and show you a new approach and mindset for designing robust and modular NLP pipelines in the age of Generative AI.

7:50 PM: Break

8:00 PM: AI Engineering for Everyone + Q&A by Tejas Kumar

This talk dives deep into the landscape of AI in 2025 with a focus on agents and extending the capabilities of language models through Model Context Protocol (MCP). After this talk, the audience will have a holistic understanding of AI for 2025 and will be able to build real-world solutions with them.

8:50 PM: Drinks and Networking

Speakers

Patrick Löber - Google DeepMind (Developer Relationships Engineer)

Ines Montani - Explosion (CEO & Founder)

Ines is a software developer working on Artificial Intelligence and Natural Language Processing technologies, and the co-founder and CEO of Explosion (https://explosion.ai/) https://bsky.app/profile/inesmon…

Tejas Kumar - DataStax (Developer Relations Engineer)

Tejas Kumar is an international keynote speaker, best selling author, and host of the developer-loved ConTejas Code podcast with an engineering background spanning 23 years, from design to frontend to backend to devops. Today, Tejas shares talks at large with developer communities worldwide, equipping them to do their best work.

Hosted By

Alex Mir, GDG Organizer

GDG Berlin co-lead and Software Engineer

Emy Jamalian, QA engineer

I am a girl, a middle eastern, a European residence, an extra extroverted, a believer in making things work, a fighter for fairness, a motivator.

Jerome Mouton, Organizer

Louis Tsai, GDG Organizer

manjula dube, Organizer

I am Software Engineer & teacher. I'm a world renowned tech speaker.I am from India currently living in Berlin with my husband Sahil Mhapsekar.

I work at The Vanguard Group Europe. I am Founder of Geekabyte that aims to deliver in person tech workshops on Web Development & organises international conferences, React India & JS Conf India. I'm also a Google Developer Expert. I have been obsessed with coding ever since I graduated out of college.

I am founding member of Mumbai Women Coders that aims to encourage more women in tech & provide an avenue into the technology world. I love contributing to open source in my free time. I love Javascript, React & my family ❤️ In coming years I see my self teaching people to code.

Shrinish Donde,

Passionate about telecom and sports. Likes to be in organising and networking.

Mohamed Islem Ayari, Junior cloud Developper

Complete your event RSVP here: https://gdg.community.dev/events/details/google-gdg-berlin-presents-using-ai-to-help-solve-problems-both-big-and-small/.

Join us on January 29 for a chance to connect, learn, and get inspired by the latest and greatest in AI. Whether you're a pro at AI or just getting started, this event is all about building our community and shaping the future of AI development. Get ready for an evening packed with awesome sessions, discussions, and networking opportunities!

Let's dive into the exciting world of AI together!

Agenda

• 6:00 PM ET: Welcome and introduction
• 6:05 PM ET to 8:00 PM ET: Program
• 8:00 PM ET to 9:00 PM ET: Networking

Program Sessions

| Session Title | Session Description | Speaker | | :------------- |:-------------|:-------------| | Empowering Enterprises with LLM Agents: Assistants for Data-Driven Insights | Large Language Model (LLM) agents are emerging as powerful tools that can revolutionize how organizations interact with their data. This session will delve into the concept of LLM agents and their potential to transform internal operations by creating intelligent virtual assistants on top of your datawarehouse. We will explore how these agents can be leveraged to generate SQL query statements dynamically, enabling seamless access to data stored in various data warehouses such as Databricks, Microsoft Fabric. Attendees will gain insights into the architecture and implementation of these virtual assistants, and discover how they can enhance productivity, streamline decision-making processes, and unlock valuable insights from their data. | Sammy Deprez | | Simplifying AI in your .NET Application | Discover how to effortlessly integrate AI into your .NET applications with the Microsoft.Extensions.AI library. Learn to add chat features, embedding generation, and tool calling seamlessly. Plus, explore innovative techniques for real-time prompt management, allowing you to test and refine AI prompts live without stopping your application. Join us for a fast-paced session packed with practical insights and cutting-edge strategies to optimize your development workflow. | Maria Naggaga Nakanwagi | | AutoGen 0.4: A Programming Framework for Agentic AI reimagined | AutoGen is an open-source framework for building AI agent systems. It simplifies the creation of event-driven, distributed, scalable, and resilient agentic applications using multi-agent architectures. In this talk, Jack will provide an overview of the AutoGen framework and dig into 0.4, a reimagining of the framework. | Jack Gerrits | | AI in Action: Lessons and Success Stories | Join us for an engaging panel discussion featuring practitioners of artificial intelligence. Our panelists will share their firsthand experiences, challenges, and successes in implementing solutions across various industries. Learn about innovative use cases and discover best practices from experts who are at the forefront of AI technology.| Lisa Qu, Vivian Lei, Samir Mahmoud, Justin Trugman |

🌟 Session Overview 🌟

Session Name: Anatomy of Inter-Agentic APIs Speaker: Swanand Rao Session Description: With the viral adoption of AI driven by commercially available LLMs, new venues of business have opened up. These new business models are driven by the likes of co-pilots and virtual agents that bring tremendous productivity gains across business workflows. Concepts like Generative AI and Conversational AI have already gained mainstream popularity. A new branch of AI that is gaining momentum is Agentic AI. This talk is geared towards identifying mechanisms that make inter-agent communication possible and the ontology of such interactions, solving for the needed concepts of roles, tasks, memory, tools, context, and prompts for a scalable and reliable inter-agent business workflow.

🚀 About Big Data and RPA 2024 🚀

Unlock the future of innovation and automation at Big Data & RPA Conference Europe 2024! 🌟 This unique event brings together the brightest minds in big data, machine learning, AI, and robotic process automation to explore cutting-edge solutions and trends shaping the tech landscape. Perfect for data engineers, analysts, RPA developers, and business leaders, the conference offers dual insights into the power of data-driven strategies and intelligent automation. 🚀 Gain practical knowledge on topics like hyperautomation, AI integration, advanced analytics, and workflow optimization while networking with global experts. Don’t miss this exclusive opportunity to expand your expertise and revolutionize your processes—all from the comfort of your home! 📊🤖✨

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