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All digests tagged Deep Agents
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Context rot degrades agent reasoning as history piles up. Deep Agents mitigates this using offloading (saving large tool results to virtual file systems) and summarization (condensing context when token usage hits 85%).
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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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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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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 LangChain using a custom tutor skill integrated into coding agents. The LCA Tutors repo allows structured learning through quizzes and labs for foundational concepts.
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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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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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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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