As AI moves from chatbots to autonomous agents, the hardest challenge isn't getting an LLM to reason. It's building the infrastructure that allows agents to reliably understand live data, use external tools, and take actions in production.

In this talk, I'll walk through how I approach building production-grade AI systems for real-time Web3 and financial applications, combining LLMs, RAG, agentic workflows, vector search, and scalable inference infrastructure.

We'll cover:

How to build RAG systems that continuously retrieve and reason over real-time blockchain and financial data

Designing agentic workflows where LLMs can use tools, query external systems, and interact with Web3 applications

Handling the latency, throughput, and cost challenges of running LLM inference in real-time systems using vLLM, batching, quantization, and GPU optimization

Architecting the infrastructure layer across Kubernetes and cloud AI platforms for scalable agent deployment

Preventing hallucinations and stale information when AI systems operate on rapidly changing transactional and financial data

Building observability, evaluation, and reliability mechanisms for autonomous AI workflows

Real-world architecture patterns for applications such as fraud detection, market intelligence, compliance, risk analysis, and intelligent financial automation

This isn't a theoretical discussion about AI agents. The focus is on the engineering required to make these systems reliable, scalable, and production-ready.

Attendees will leave with concrete architecture patterns for combining real-time data, RAG, LLM inference, and autonomous agents to build the next generation of AI-native Web3 applications.