Building an AI agent that works in a demo is easy. Making it "reliable, secure, observable, and cost-effective in production" is where the real engineering begins.

In this practical, architecture-focused session, we’ll explore "7 challenges that emerge when AI agents move from prototype to production":

1. Identity & Authorization — Who is the agent acting as?
2. Secrets & Credentials — How should sensitive access be managed?
3. State & Memory — What happens when workflows fail halfway?
4. Tool Reliability — Handling retries, timeouts, and duplicate actions.
5. Observability — Tracing decisions across models, tools, and APIs.
6. Cost & Latency — Controlling model calls and response times.
7. Security & Guardrails — Limiting unsafe actions and blast radius.

We’ll connect these challenges to practical engineering patterns and a production-ready architecture.