Building and Testing Reliable Agents
This talk was given as a workshop at the AI Engineering World's Fair on June, 24 2024. LLM-powered agents hold tremendous promise for autonomously performing tasks, but reliability is often a barrier for deployment and productionisation. Here, we'll show how to design and build reliable agents using LangGraph. We’ll cover ways to test agents using LangSmith, examining both agent's final response as well as agent tool use trajectory. We'll compare a custom LangGraph agent to a ReAct agent for RAG to showcase the reliability benefit associated with building custom agents using LangGraph. Slides: https://docs.google.com/presentation/... CoLab: https://drive.google.com/file/d/1KUCI... Notebook: https://github.com/langchain-ai/langg... LangGraph: https://blog.langchain.dev/langgraph-...

The Future of AI Agents: What Will Interrupt 2027 Look Like? | Interrupt 26

The Agent Development Lifecycle: Build, Test, Deploy, Monitor | Interrupt 26

The tool design tricks behind Benchling's AI agents

LangGraph Agents - Human-In-The-Loop Breakpoints

Introducing Managed Deep Agents | Interrupt 26

How to manage context the right way with LangSmith's Context Hub

Auto-Prompt Builder (with Hosted LangServe)

How We Built LangSmith Engine | Interrupt 26

Inside Cogent's three-agent architecture for autonomous defense | Geng Sng (Co-founder, Cogent)

Building Frontier CX Agents | Interrupt 26

Introducing LangSmith Engine

Run Untrusted Agent Code with LangSmith Sandboxes | Interrupt 26

Testing Driving GPT 5

LangChain Academy New Course: Introduction to LangSmith Deployment

