Discussion on agentic LLM adoption in production, focusing on LangChain and LlamaIndex tooling, production readiness, tools, evaluation and observability, safety and guardrails.

Dialogue on deploying agentic LLMs in production, focusing on LangChain and LlamaIndex usage, tools, evaluation and observability, safety and guardrails.

Discussion on agentic LLM adoption, including LangChain and LlamaIndex in production, tools, evaluation and observability, safety and guardrails.

Agentic LLM adoption (LangChain/LlamaIndex in production, tools, evaluation and observability, safety and guardrails).

Dialogue 2: Agentic LLM adoption (LangChain/LlamaIndex in production, tools, evaluation and observability, safety and guardrails)

Amazon Q is powerful out of the box, but in the terminal it becomes a superpower. In this talk, I share how I turned Amazon Q CLI into a fully-fledged, verification‑first AI platform using the Model Context Protocol (MCP) all without adding $0 extra cost. I’ll walk through how I built 49 MCP servers to remove hallucinations, browser automation with Playwright, and live AWS verification that eliminates hallucinations in practice. You’ll see Financial Services Institution grade guardrails (production protection, cost controls, and full audit trails) that makes Q feel native to a builder’s workflow, secure and reliable. You’ll leave with a blueprint to extend Amazon Q/ any agentic coding beyond chat into a trusted, terminal‑first, agentic platform you own and can evolve infinitely.

Agentic LLM adoption in production, including LangChain and LlamaIndex in production contexts, tools, evaluation and observability, safety and guardrails.

Discussion on LangChain and LlamaIndex in production, tools, evaluation and observability, safety and guardrails for agentic LLM workflows.

Discussion on agentic LLM adoption in production, featuring LangChain and LlamaIndex; topics include tools, evaluation, observability, safety and guardrails.

Agentic LLM adoption — LangChain and LlamaIndex in production, tools, evaluation and observability, safety and guardrails.

Learn about fine tuning, prompt tuning, guardrails, and middleware to make LLMs more consistent and reliable.