The Control-vs-Magic Spectrum Building Agents

About this episode

Thiago Cardoso is the Director of Data & AI at iFood and the architect behind iFood Pago's AI agent platform. This fintech system serves millions of restaurants across Brazil through WhatsApp and the iFood app. In this episode, he breaks down what it actually takes to ship agentic AI in production at scale.

The Control-vs-Magic Spectrum Building Agents // MLOps Podcast #382 with Thiago Cardoso, Director of Data & AI at iFood

🤖 WHAT WE COVER:

🔹 Control vs. Magic — Thiago's spectrum model for thinking about AI agents, from deterministic pipelines to fully autonomous systems

🔹 iFood Pago Explained — How iFood's embedded fintech arm uses AI agents to provide credit, loans, and financial services to restaurants

🔹 WhatsApp as an AI Interface — Why WhatsApp is the primary channel for merchant interactions in Brazil and how agents are deployed there

🔹 Multi-Agent Architecture — Why single monolithic agents break down and how to split them into sub-graphs with specialized contexts and tool sets

🔹 Context Engineering — Why what you put in the agent's context window is more important than the model itself

🔹 Human-in-the-Loop Design — How to build trust with merchants while minimizing friction in agentic workflows

🔹 LangGraph in Production — How Thiago's team uses LangGraph to build stateful, multi-agent pipelines

🔹 Debugging with AI — Generating on-the-fly HTML/JavaScript visualization tools to investigate data pipeline problems

🔹 The Cost of Software Going to Zero — What happens to demand when software becomes nearly free to build

🔹 Personalization at Scale — Serving millions of restaurants with AI that knows their business context

🎯 This episode is for AI engineers, ML practitioners, and fintech builders who want to understand what production agentic AI looks like beyond the demos.

🔗 LINKS & RESOURCES:

Thiago Cardoso on LinkedIn: https://www.linkedin.com/in/thiagoncc/

iFood: https://www.ifood.com.br

iFood Pago: https://ifoodpago.com.br

ZenML iFood Case Study: https://www.zenml.io/llmops-database/building-a-hyper-personalized-food-ordering-agent

LangGraph: https://www.langchain.com/langgraph

⏱️ TIMESTAMPS

[00:00] Control vs Magic in AI

[00:18] Foodpago Fintech Ecosystem

[08:59] Scaling Personalization with AI

[15:04] Chat UI Evolution

[20:22] Context Layer in Systems

[26:39] Agent Growth Dynamics

[33:39] Job Evolution with Open Claude

[39:54] AI and Software Costs

[41:50] Wrap up

#AIAgents #Fintech #iFood