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Managed Deep Agents simplifies deploying mission-critical AI agents. It bundles the Deep Agents harness with managed LangSmith infrastructure, allowing developers to define, test, and deploy agents using the mda CLI.
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Sydney Runkle details how to build a model router in the harness to reduce operational costs. By analyzing task traces and selecting models along the Pareto frontier, the router cut median cost per thread by 64%.
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Web search can allow your agent to conduct external research and add that to information that you've already built in with your existing set of tools and skills. LangChain provides built-in search using parallel as part of the LangSmith platform.
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LangChain outlines the shift to 'owning your intelligence' by building domain-specific agents. The platform provides Managed Deep Agents, Jev decision models, and LangSmith Engine v2 for robust governance and production readiness.
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Learn how to build custom HTTP channels for Managed Deep Agents to handle inbound requests from external services like HubSpot or iMessage. We cover defining verifiers, parsers, and managing user context.
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SmithTune provides a single, end-to-end workflow to convert agent traces into custom fine-tuned models. The process includes dataset creation, training (using providers like Fireworks or Baseten), and evaluation.
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Learn how to use Schedules for Managed Deep Agents to automate recurring tasks. Configure cron jobs to generate weekly reports from Salesforce and deliver them automatically to Slack via LangSmith.
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Jev is a System 1 model that makes fast, structured decisions without generating text. Learn how to integrate it into agent harnesses for safety classification and model routing, significantly improving efficiency and reliability.
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Learn how to build Patch, an agent that converts Slack messages into working GitHub PRs. This walkthrough details using Managed Deep Agents to integrate Slack, GitHub, and a sandbox with minimal code.
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Jev, a new System 1 model from TypeSafe AI, is designed for fast, structured decision-making. It is up to 200x faster and 400x cheaper than LLMs for classification tasks, making it ideal for model routing and agent evaluation.
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Learn how middleware extends Managed Deep Agents, enabling custom behaviors like PII redaction and tool call logging. See how to build custom middleware using `wrap_tool_call`.
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Lyft rebuilt its customer support agent stack using a meta-agent architecture with LangGraph and LangSmith. This self-serve platform reduced agent build time from six months to two weeks, boosting resolution rate by 16%.
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Learn how to deploy a Managed Deep Agent to Slack using LangSmith. This guide covers initialization, authorization, monitoring live traces, and customizing message triggers for production readiness.
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To prevent performance regressions in Managed Deep Agents, use Harbor and `mda evals init` to scaffold robust eval suites. Evals are structured into environments, jobs, and checks, and results are monitored in LangSmith.
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Learn how to evaluate AI agents at scale using an LLM-as-a-judge in LangSmith. Automate quality checks for user frustration and manage costs by adjusting the sampling rate.
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Learn how to analyze thousands of production traces and build structured datasets of agent failures in minutes. This workflow uses the LangSmith CLI and a coding agent to classify perceived errors into reusable failure types.
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Clay details its agentic stack (Claygent/Sculptor), emphasizing that scaling requires robust evaluation suites, a data lake foundation, and leveraging large context models to build self-iterating loops.
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LangSmith Preview Builds bring standard software development workflows to AI agents. By deploying a temporary environment for every pull request, teams can test agent changes safely before they reach production.
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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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LangSmith Tuned Evaluators automatically attach quality feedback to production traces and threads. They analyze agent interactions to find behavior that needs attention, starting with Perceived Error.
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