Section Insights
Introduction to Managed Deep Agents
What are managed deep agents and how do they work?
Managed deep agents simplify the process of building and deploying production agents by allowing users to define business logic, rules, tools, and skills, while the system handles orchestration and infrastructure.
- Managed deep agents streamline agent development.
- Users define the agent's logic and requirements.
- The system manages orchestration and infrastructure.
Architecture of Managed Deep Agents
What components are involved in managed deep agents?
The architecture includes user-defined business logic, an agent harness, and a managed runtime that provides necessary infrastructure and services.
- The agent harness is model-agnostic and open-source.
- Managed runtime includes agent server and scheduling.
- Users focus on logic while the system manages execution.
Defining Managed Deep Agents
How are managed deep agents structured?
Managed deep agents are organized in a folder containing various components such as instructions, skills, memory, and middleware, which will be explored in detail in the series.
- Agents are structured in a simple folder format.
- Components include instructions, skills, and middleware.
- The series will cover each component in detail.
Getting Started with LangSmith
What do you need to start using managed deep agents?
To begin, users need a LangSmith API key, which allows access to the platform for agent engineering, including a free tier for most functionalities.
- A LangSmith API key is essential for starting.
- The platform offers a generous free tier.
- Users can create and run agents locally.
Creating a LangSmith Account
How do you obtain a LangSmith API key?
Users can create an account on LangSmith, log in, and generate an API key through the settings menu, which is necessary for the next steps in learning about managed deep agents.
- Creating an account is the first step.
- API keys are generated in the settings menu.
- The next video will cover the quick start process.
Transcript
0:00 In this YouTube series, we're going to dive deep into LangChain managed deep agents. Managed deep agents is the simplest way to build and deploy production agents. All you have to do is define your agent's business logic, the rules it should follow, the tools it should use, the skills it should have. Managed deep agents then takes care of bundling the harness, the orchestration bit that does all the running of the agent, with the actual infrastructure that's used to power it.
0:31 So, specifically if we look at this diagram here, I think it shows it nicely. You provide your business logic, managed deep agents provides the agent harness, so deep agents is an open-source model-agnostic agent harness, and then a managed runtime with the agent server, sandboxes, schedules, and much more. Managed deep agents are really simple to define. They are just a folder. In that folder, there's a bunch of different parts of that agent, from instructions to skills to memory to middleware.
1:02 In this YouTube series, we're going to cover each of these parts in detail, explaining what they are and how you can use them to create a really powerful, production-ready managed deep agent. One of the things you're going to need to get started with managed deep agents is a LangSmith API key. So, LangSmith is our platform for agent engineering. It comes with a generous free tier, which will let you do pretty much everything in this YouTube series, except for the final step of deploying your agent. But, you'll be able to create it, run it locally, and even run it in the LangSmith Agent Studio.
1:37 So, you can create an account here. Once you have an account and log in, you can then grab your LangSmith API key by going to settings and then creating an API key here. The next step in learning about managed deep agents is to do the quick start, and that's what we're going to do in the next video. Let's go to that.
Summary
- Managed deep agents streamline the creation and deployment of production agents.
- Users define business logic, rules, tools, and skills for their agents.
- The system bundles the agent harness with the necessary infrastructure.
- Managed deep agents consist of a folder containing various components like instructions, skills, and memory.
- A LangSmith API key is required to get started, allowing users to create and run agents locally.
- The series will cover each component of managed deep agents in detail.
- Viewers can utilize a free tier of LangSmith for most of the series' activities, except final deployment.
Questions Answered
What are managed deep agents and how do they work?
Managed deep agents simplify the process of building and deploying production agents by allowing users to define business logic, rules, tools, and skills, while the system handles orchestration and infrastructure.
What components are involved in managed deep agents?
The architecture includes user-defined business logic, an agent harness, and a managed runtime that provides necessary infrastructure and services.
How are managed deep agents structured?
Managed deep agents are organized in a folder containing various components such as instructions, skills, memory, and middleware, which will be explored in detail in the series.
What do you need to start using managed deep agents?
To begin, users need a LangSmith API key, which allows access to the platform for agent engineering, including a free tier for most functionalities.
How do you obtain a LangSmith API key?
Users can create an account on LangSmith, log in, and generate an API key through the settings menu, which is necessary for the next steps in learning about managed deep agents.