Agents in Production [Podcast Limited Series] Episode Nine – Training LLMs, Picking the Right Models, and GPU Headaches Paul van der Boor and Zulkuf Genc from Prosus join Demetrios to talk about what it really takes to get AI agents running in production. From building solid eval sets to juggling GPU logistics and figuring out which models are worth using (and when), they share hard-won lessons from the front lines. If you're working with LLMs at scale—or thinking about it—this one’s for you.

What happens when you empower AI agents to design, configure, and deploy other agents? At Hypermode, we put this question to the test by developing Concierge—an agent that acts as both architect and orchestrator, assembling custom agent workflows on demand. In this session, I’ll share the technical journey behind building Concierge, our “agent that builds agents,” and how it’s reshaping the way teams approach automation and task completion. Key topics will include: The architecture and design patterns enabling agent creation How Concierge leverages natural language and user intent to assemble tailored agent teams Real-world challenges: managing reliability, evaluation, and guardrails when agents are in charge Lessons learned from deploying agent-built agents in production environments The future of agentic systems: towards self-improving, self-deploying AI teams

Agents are only as useful as the data they can access. EnrichMCP turns your existing data models, like SQLAlchemy schemas, into an agent-ready MCP server. It exposes type-checked, callable methods that agents can discover, reason about, and invoke directly. In this session, we’ll connect EnrichMCP to a live database, run real agent queries, and walk through how it builds a semantic interface over your data. We’ll cover relationship navigation (like user to orders to products), how input and output are validated with Pydantic, and how to extend the server with custom logic or non-SQL sources. Finally, we’ll discuss performance, security, and how to bring this pattern into production.