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World Congress 2026 Europe - Virtual Stage • Jul 2, 2026 • Session details

AI Agents & Agentic AI

Xavier Sala Presas

Reactive AI is obsolete. Learn to build, evaluate, and deploy proactive multi-agent workflows using Python, Google ADK, and the Model Context Protocol.

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#1 about 4 min

Comparing AI agents with generative models and chatbots

Highlighting limitations in reactive AI systems demonstrates the need for autonomous agentic capabilities.

#2 about 2 min

Defining autonomous AI agents and their practical applications

Combining reasoning with external tools enables software to execute multi-step user intents autonomously.

#3 about 4 min

Configuring base agent properties using google ADK

Defining models, tools, and separate instruction files shapes precise operational behavior for large language models.

#4 about 2 min

Creating specialized multi-agent systems for complex workflows

Delegating specific tasks to modular sub-agents prevents single massive prompts from becoming overly complicated.

#5 about 2 min

Organizing project folder structures for deployable agents

Structuring files for prompts, tests, and environment variables ensures clean deployment to serverless or containerized environments.

#6 about 5 min

Implementing graph-based and multi-tool agent architectures

Designing explicit execution paths with sequential, loop, and parallel graphs removes reliance on unpredictable language model routing.

#7 about 3 min

Standardizing data exchanges using input and output schemas

Enforcing strict JSON structures and lazy-loading skills optimizes context windows and ensures reliable data handling.

#8 about 1 min

Core components of robust AI agent architectures

Combining external tools, specialized models, and short-term memory forms the foundation for scalable orchestration.

#9 about 4 min

Utilizing agentic protocols for seamless system integration

Adopting standardized frameworks like the model context protocol reduces custom integration code when connecting diverse tools.

#10 about 1 min

Evaluating language models through qualitative execution trajectories

Analyzing intermediate tool calls and routing steps addresses the unreliability of deterministic testing in generative systems.

#11 about 2 min

Deploying AI agents to serverless compute engines

Executing command-line tools deploys autonomous workflows directly into scalable cloud infrastructure and kubernetes clusters.

#12 about 3 min

Managing runtime costs and ethical guardrails in production

Establishing monitoring bounds and explaining autonomous decisions mitigates the inherent risks of unstructured data processing.

Matching moments

1:30 min

Locating AI agents within the broader machine learning landscape

Jörg Neumann Jörg Neumann · WWC 2025

40 sec

Introduction to building real-world AI agent solutions

Dennis Zielke Dennis Zielke +1 · WWC 2025

2:06 min

Rethinking team structures around AI agent capabilities

Mike Mike · WWC 2025

1:32 min

Architectural patterns for developing robust generative AI applications

Julián Duque Julián Duque · WWC 2025

1:11 min

Shifting security models from passive chatbots to active agents

Péter Farkas Péter Farkas · Europe 2026 Virtual

40 sec

Empowering automated workflows with agentic AI models

Mike Mike · WWC 2025

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