AI Engineer topic

Observability and reliability

131 AI Engineer conference talks about Observability and reliability.

AI Engineer World's Fair 202517:06

12-Factor Agents: Patterns of reliable LLM applications

Dex Horthy

Architecture · APIs, MCP, and protocols · RAG, context, and search

AI Engineer World's Fair 202520:55

3 ingredients for building reliable enterprise agents

Harrison Chase

Evals · Coding and developer tools · Agent engineering

AI Engineer World's Fair 202525:39

7 Habits of Highly Effective Generative AI Evaluations

Justin Muller

Reasoning and models · Evals · Observability and reliability

AI Engineer Summit 202520:50

Agents are built at the fringe: getting from 90 to 100

Kevin Hou

Coding and developer tools · RAG, context, and search · Observability and reliability

AI Engineer World's Fair 202630:14

Agents Building Agents

Alfonso Graziano

Architecture · Agent engineering · Evals

AI Engineer World's Fair 202633:39

AI Agents for Performance: Ship Faster, Pay Less

Rajat Shah

Infrastructure and deployment · Agent engineering · RAG, context, and search

AI Engineer Summit 202529:10

AI Agents, Meet Test Driven Development

Anita Kirkovska

RAG, context, and search · Coding and developer tools · Observability and reliability

AI Engineer Summit 20231:43:16

AI Engineering 201: Inference

Charles Frye

Infrastructure and deployment · Observability and reliability · Reasoning and models

AI Engineer World's Fair 202624:56

Always-on agents run production without the on-call tax

Justin Smith

Infrastructure and deployment · Observability and reliability · Coding and developer tools

AI Engineer World's Fair 202514:25

Break It 'Til You Make It: Building the Self-Improving Stack for AI Agents

Aparna Dhinakaran

Evals · Reasoning and models · RAG, context, and search

AI Engineer World's Fair 202527:02

Breaking the Chain: Agent Continuations for Resumable AI Workflows

Greg Benson

Agent engineering · Reasoning and models · Coding and developer tools

AI Engineer Code 202552:08

Build a Prompt Learning Loop

SallyAnn DeLucia · Fuad Ali

Observability and reliability · RAG, context, and search · Evals

AI Engineer World's Fair 202624:23

Build for the Memo, Not the Demo — Notes from 200 Investment Committees

Shawn Chan

Finance · Leadership · Observability and reliability

AI Engineer Europe 20261:08:15

Build Your First Demand-Driven Context Base: Let AI Agents Tell You What They Need

Raj Navakoti

RAG, context, and search · Coding and developer tools · Observability and reliability

AI Engineer Europe 202618:22

Building a Chess Coach

Anant Dole · Asbjørn Steinskog

Agent engineering · Other / unclassified · Creative and generative media

AI Engineer World's Fair 202515:50

Building Applications with AI Agents

Michael Albada

Observability and reliability · Architecture · Evals

AI Engineer World's Fair 202621:39

Building Closed-Loop Evals for a Multimodal Agent at Uber Scale

Soumya Gupta · Jai Chopra

Observability and reliability · Evals · Safety and governance

AI Engineer World's Fair 20267:14

Building Deterministic Infrastructure for Non-Deterministic AI Agents

Nishant Gupta

Observability and reliability · Infrastructure and deployment · RAG, context, and search

AI Engineer Code 20251:09:49

Building durable Agents with Workflow DevKit & AI SDK

Peter Wielander

Observability and reliability · Infrastructure and deployment · Enterprise

AI Engineer Code 20251:18:30

Building Durable, Production-Ready Agents with OpenAI SDK and Temporal

Cornelia Davis

Agent engineering · Infrastructure and deployment · APIs, MCP, and protocols

AI Engineer World's Fair 20241:07:23

Building & Scaling an AI Agent Swarm of low latency real time voice bots!

Damien Murphy

Agent engineering · Speech and audio · Developer workflows and testing

AI Engineer World's Fair 202645:48

Bypassing the Multimodal Tax: Framework-Free Hybrid RAG, Raw SQL RRF, and Live UI Telemetry

Abed Matini

RAG, context, and search · Observability and reliability · Developer workflows and testing

AI Engineer World's Fair 202615:12

Chat and citations won't save your vertical AI

Atul Ramachandran

Agent engineering · Observability and reliability · Safety and governance

AI Engineer Code 202516:41

Code World Model: Building World Models for Computation

Jacob Kahn

Architecture · Coding and developer tools · Observability and reliability

AI Engineer World's Fair 202418:29

Compute & System Design for Next Generation Frontier Models

Dylan Patel

RAG, context, and search · Observability and reliability · Infrastructure and deployment

AI Engineer World's Fair 202514:40

Conquering Agent Chaos

Rick Blalock

Observability and reliability · Coding and developer tools · APIs, MCP, and protocols

AI Engineer Code 202523:52

Context Platform Engineering to Reduce Token Anxiety — Val Bercovici and Callan Fox, WEKA

Val Bercovici · Callan Fox

RAG, context, and search · Infrastructure and deployment · Observability and reliability

AI Engineer World's Fair 202619:46

Designing Agents (The Floor Is the Frontier)

Ben Hylak

Evals · Infrastructure and deployment · Agent engineering

AI Engineer Code 20251:13:13

DSPy: The End of Prompt Engineering

Kevin Madura

RAG, context, and search · Observability and reliability · Coding and developer tools

AI Engineer World's Fair 202516:41

Effective AI Agents Need Data Flywheels, Not The Next Biggest LLM – Sylendran Arunagiri, NVIDIA

Sylendran Arunagiri

Architecture · Agent engineering · Observability and reliability

AI Engineer Code 202520:19

Efficient Reinforcement Learning

Rhythm Garg · Linden Li

Data and model adaptation · Infrastructure and deployment · Observability and reliability

AI Engineer World's Fair 202616:33

Emulated: The data for fully autonomous software engineers and companies

Joseph Wang · Sid Patllollu

Data and model adaptation · Developer workflows and testing · Observability and reliability

AI Engineer World's Fair 202616:30

Ending AI Slop

Thais Castello Branco

Observability and reliability · Safety and governance · Enterprise

AI Engineer Summit 202520:00

Building and evaluating AI Agents That Matter

Sayash Kapoor

Other / unclassified · Evals · Infrastructure and deployment

AI Engineer Europe 202650:25

Everything You Need To Know About Agent Observability

Danny Gollapalli · Ben Hylak · Zubin Koticha

Observability and reliability · Coding and developer tools · Other / unclassified

AI Engineer Europe 202610:11

Feedback Loops are All You Need

Mehedi Hassan

Architecture · Speech and audio · Observability and reliability

AI Engineer World's Fair 202620:24

From Agent Traces to Agent Simulations — Rustem Feyzkhanov, Snorkel AI

Rustem Feyzkhanov

Evals · RAG, context, and search · Observability and reliability

AI Engineer Europe 202626:29

From Chaos to Choreography: Multi-Agent Orchestration Patterns That Actually Work — Sandipan Bhaumik

Sandipan Bhaumik

Agent engineering · Observability and reliability · Infrastructure and deployment

AI Engineer Europe 202625:26

From MCP to Scale: Pipelines That Build Themselves

Rafael Levi

Agent engineering · APIs, MCP, and protocols · Coding and developer tools

AI Engineer World's Fair 202617:46

From RL to IRL — Gaurav Mishra, Amazon AGI Lab

Gaurav Mishra

Coding and developer tools · Safety and governance · Observability and reliability

AI Engineer World's Fair 202620:36

From Signal to PR: Anatomy of a Self-Improving Agent

Jason Lopatecki

Observability and reliability · Coding and developer tools · Safety and governance

AI Engineer World's Fair 202519:12

Fuzzing in the GenAI Era

Leonard Tang

Evals · Speech and audio · Reasoning and models

AI Engineer Code 202513:39

Hacking Subagents Into Codex CLI — Brian John, BetterUp

Brian John

Coding and developer tools · Observability and reliability · Agent engineering

AI Engineer Europe 202620:43

How agent o11y differs from traditional o11y

Phil Hetzel

Agent engineering · RAG, context, and search · Observability and reliability

AI Engineer World's Fair 202513:32

How agents broke app-level infrastructure

Evan Boyle

Agent engineering · Infrastructure and deployment · Developer workflows and testing

AI Engineer Europe 202625:13

How Google DeepMind Runs Agents at Scale — KP Sawhney & Ian Ballantyne, Google DeepMind

KP Sawhney · Ian Ballantyne

Observability and reliability · APIs, MCP, and protocols · Developer workflows and testing

AI Engineer World's Fair 202519:23

How to look at your data; what to look for, how to measure

Jeff Huber · Jason Liu

Other / unclassified · RAG, context, and search · Agent engineering

AI Engineer Europe 202617:05

How Transformers Finally Ate Vision

Isaac Robinson

Vision and video · Infrastructure and deployment · Architecture