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All digests tagged LangGraph
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Transitioning AI agents to production requires a governed framework. Learn how to implement centralized MCP Gateways, LangGraph workflows, and robust CI/CD pipelines for secure, scalable, and observable enterprise AI.
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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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Multi-agent coordination is a distributed systems problem. Low-level protocols like MCP and A2A are insufficient because they lack persistence, discovery, and real-time ordered transport. Band solves this by providing a global collaboration layer for autonomous agents.
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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 build reliable AI agents, focus on Control Graphs—structured workflows (SOPs). Implement these using code frameworks like LangGraph or by defining SOPs in text for LLMs to follow. Always include verifiers and use scripts for deterministic steps.
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DeepWiki, a codebase documentation product for agents like Devin, evolved from orchestration-led v1 to an agentic v2. Learn about context engineering principles, scaling codebases, and building robust knowledge graphs.
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OpenWiki is an open-source CLI that generates documentation specifically optimized for coding agents. It uses OKF and GitHub Actions to automatically maintain docs synced with your repo's Git history, improving agent efficiency.
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Harmonic quadrupled user retention by shifting from brittle query graphs to Deep Agents. Learn how a model-plus-tools loop and file system abstractions solve context overload.
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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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PAT, an AI analyst tool built by Bridgewater Associates, treats agentic coding as a compiler problem. It uses LangGraph for state management, enabling parallel code generation and deterministic execution to perform deep research in minutes.
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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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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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