From Models to Autonomous Minds: The Next Era of Verifiable AI

Build. Verify. Interact

Live on June 25, 2026 | 10:15 - 15:45 CEST

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🇬🇧 English

The Future of AI Is Autonomous, Verifiable, and Interactive

Artificial intelligence is moving beyond static prediction models into a new generation of systems that can reason, act, adapt, and interact in real time. As organizations increasingly rely on AI for mission-critical decisions, trust, transparency, and reliability become essential. The next era of AI demands systems that are not only powerful, but also verifiable, explainable, and secure by design.

From Black-Box Models to Intelligent Systems You Can Trust

This event explores how AI evolves into cryptographically verifiable, agentic, and naturally interactive software systems. Learn how blockchain-backed trust guarantees, classical machine learning principles, modern orchestration frameworks, and real-time conversational interfaces combine to create AI applications that are scalable, autonomous, and ready for production. Discover practical strategies to build systems that move beyond experimentation and into unstoppable real-world execution.

Your expert sessions

10:15 - 11:00 | Building Identity into LLM Workflows with Verifiable Credentials | Ben Dechrai

Learn how to secure AI systems with modern identity concepts and Verifiable Credentials. This session explores real-world prompt injection attacks, the vulnerabilities that enable them, and practical techniques for protecting LLM-based applications from manipulation and misuse.

You’ll discover emerging identity approaches, from W3C Verifiable Credentials to on-chain verification, and learn how they can help establish trust, accountability, and transparency in AI-driven workflows.

Through practical demos and case studies, Ben Dechrai will also discuss auditability, policy-as-code, and federated governance models as building blocks for future LLM-aware identity ecosystems. You’ll leave with actionable patterns for securing AI applications today while preparing for the next generation of decentralized identity architectures.

11:30 - 12:15 | From Models to Minds: Bridging Traditional ML with Agentic AI | Sinda Khenine

The past decade focused on training models, the next one is about building intelligent systems that can think and act autonomously.

This talk connects the traditional machine learning pipeline, data preparation, modeling, and deployment with modern agentic AI architectures. Concepts such as feature engineering, clustering, and model evaluation are reimagined in the context of LLMs, autonomous reasoning, and retrieval-augmented generation (RAG).

Using practical examples, the session bridges classic ML principles like bias-variance tradeoff, explainability, and feedback loops with modern AI tooling such as function calling, LangChain orchestration, and RAG. Attendees will gain a clear understanding of how to evolve from static ML models to adaptive AI systems while reusing existing ML expertise to build scalable, reliable AI workflows.

15:00 - 15:45 | “Hello, AI?!” — Real-time Interactions with Language Models, Local and Offline-Capable | Christian Liebel

Large Language Models (LLMs) are reshaping software design: instead of traditional GUIs and clicks, natural language, both text and voice is becoming the primary interface.

In this session, attendees will learn how to integrate voice-enabled AI models directly into applications and control them in real time through speech. By combining local AI models, these solutions can also run entirely on-device, enabling greater privacy and independence from the cloud.

Through practical demos, Christian Liebel from Thinktecture will demonstrate how to interact with LLMs using voice, connect custom functionalities, and create smart conversational interfaces for modern applications.

Expert Knowledge for

AI Engineers who want to build trustworthy, production-ready autonomous systems
ML Engineers looking to evolve from traditional pipelines to agentic AI architectures
Software Architects exploring verifiable AI infrastructure and secure deployment models
Developers who want to create natural, voice-enabled AI interfaces