Managed Deep Agents - Tools
Learn how to add custom tools to your managed deep agent using LangChain. Tools allow agents to interact with external APIs and databases by defining them as decorated Python functions.
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LangChain
All digests tagged LangChain
Managed Deep Agents - Skills
Skills package task-specific procedures and context into reusable directories. You can define them in markdown files, and they are picked up automatically by the agent.
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Managed Deep Agents - Quickstart
This guide walks through scaffolding and deploying Managed Deep Agents using the MDA CLI. Key steps include defining instructions in `instructions.mmd`, local testing with `MDA dev`, and syncing context to the Context Hub before final deployment.
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Keep model costs in check with LangSmith LLM Gateway
LangSmith LLM Gateway provides a single endpoint to enforce consistent cost controls (spend limits and rate limits) across multiple LLM providers, solving decentralized token spend issues.
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Building Governed Agents: A Framework for Cost, Control and Compliance
Centralized LLM Gateways are critical for governing autonomous agents, managing cost, ensuring compliance (GDPR, HIPAA), and providing reliability through fallbacks and rate limits across the entire Agent Development Lifecycle.
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Improving Agents is a Data Mining Problem — Vivek Trivedy, LangChain
Improving agents is a data mining problem. The key to continuous improvement is collecting comprehensive agent traces, which serve as the substrate for observability and continual learning.
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Build a social media agent with Managed Deep Agents
Learn how to build an autonomous social media agent using LangChain's Managed Deep Agents. We cover tool creation, scheduling, memory management, and full deployment to Slack.
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Create an agent that can browse the web with Managed Deep Agents and Browserbase's Stagehand
We're going to use LangSmith managed deep agents and Stagehand V4 tools. This allows us to build an agent that can interact with web pages by scrolling, clicking, and navigating across pages.
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How I helped developers talk about feelings and needs - Gitte Klitgaard - NDC Copenhagen 2026
The talk emphasizes that building strong teams requires more than just coding; it demands psychological safety, clear communication tools, and setting a 'frame' for discussion.
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Building Deep Agents and Deploying in Production
Deep Agents are defined as a sophisticated 'harness' built around foundational LLMs, providing necessary infrastructure—memory, tools, file systems, and middleware—to make agents reliable for production use.
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Inside the Agent Engine: A LangChain and Traversal Fireside Chat
Building AI SRE agents requires overcoming petabyte-scale data challenges. Success hinges on creating a robust agent harness, building a production world model, and strategically balancing offline vs. online search.
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The Agent Development Lifecycle 101 by Harrison Chase
Mastering the Agent Development Lifecycle requires moving beyond demos to systematic iteration across Build, Test, Deploy, Monitor, and Govern. Learn how advanced tooling uses tracing and online evals to ensure agents behave reliably at scale.
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/goal: Building big features with dcode
dcode introduces the /goal command for long-running tasks. It establishes visible acceptance criteria and uses a 'goal loop' to guide agents over hours, allowing mid-run steering and inspection via LangSmith tracing.
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60% Faster Time-to-Interview: Transforming Hiring with AI Agents with LangChain
Learn how LinkedIn built a hiring agent using LangGraph to cut time-to-interview by 60%. We cover the shift from static workflows to advanced plan-execute-replan loops and essential harness engineering techniques.
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