From Demo to Production: Building Reliable AI Agents | Hands-On Workshop
Your agent works. Until it doesn't.
The demo looked great. The prototype passed every test you threw at it. Then it hit real users — and everything fell apart. Wrong answers. Hallucinated tool calls. Edge cases nobody anticipated. This isn't a skill gap. It's an infrastructure gap. And it's one almost every team shipping AI agents runs into.
This workshop exists to close it.
Over three hours, you'll work through a hands-on, structured workflow that takes a raw AI agent and hardens it for production — using fully open-source tools built for exactly this problem. You'll leave with working code, an installed workflow, and a repeatable process you can apply to whatever you're building next.
What You'll Build
No slides-heavy theory. You'll spend the majority of the night actually building, across three stages:
Simulate — Before real users ever touch your agent, generate hundreds of realistic, adversarial, and edge-case conversations at scale. Surface the failures that unit tests and manual testing consistently miss. If it's going to break, find out here.
Evaluate — Go far beyond "does it respond." Run your agent through structured evaluation suites that measure accuracy, hallucination rates, tool-calling correctness, task completion, and safety across diverse scenarios. Know exactly where your agent stands before it goes live.
Optimize — Take your eval results and turn them into targeted improvements. Tighten prompts, fix tool configs, harden guardrails. No more guesswork, no more shipping and praying — just a clear, data-driven loop from failure to fix.
By the end of the night, you'll have gone from a raw agent to a production-grade one — and you'll have the workflow to do it again from scratch on your next project.
Agenda
5:00 PM — Doors open — networking, snacks, get settled
5:30 PM — Welcome & intro: the reliability gap in production AI
5:50 PM — Stage 1 — Simulate: generating realistic test conversations at scale
6:30 PM — Stage 2 — Evaluate: structured evals beyond "does it respond"
7:15 PM — Stage 3 — Optimize: closing the loop from failure to fix
8:30 PM — Networking, snacks, Q&A, and wrap
9:00 PM — End
Who This Is For
AI engineers and backend developers with hands-on LLM experience who are actively shipping — or about to ship — agents into production. If you're building customer support bots, coding assistants, RAG pipelines, voice agents, or multi-agent workflows, this applies directly to your stack.
This is a practitioner-level session. We're keeping it to 30–40 engineers so the room stays tight and the conversation stays technical.
What to Bring
Laptop with Python installed
3+ years of software engineering experience, with hands-on exposure to LLM-based agents (any framework: LangChain, CrewAI, LlamaIndex, OpenAI SDK, or similar)
A willingness to break things and fix them
The Tools
Everything in this workshop runs on Future AGI's open-source libraries, which cover the full agent reliability lifecycle: simulation, evaluation, optimization, observability, and guardrails. You'll leave with the code and the tools already installed on your machine.
Details
📅 Date: May 11, 2025
⏰ Time: 2:00 – 6:00 PM
💸 Cost: Free
🪑 Spots: Limited to 30–40 engineers
Spots are limited and this will fill fast. Register now to confirm your seat.