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LangSmith
7 posts about LangSmith by 3 contributors.
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Everything about LangSmith
- Article · AI Agents
When Prompt Engineering Stops Helping: Model and Retrieval Changes in Enterprise Tax Management AI
Prompt engineering is a powerful tool, but it is not a panacea. In enterprise tax management, the cost of error is too high to rely on prompts alone. By building a multi-agent system with LangGraph, we can isolate failures: if retrieval is poor, we fix the vector store; if reasoning is weak, we upgrade the model; if compliance is risky, we add guardrails. This architectural approach ensures that our AI copilot is not just smart, but also safe, accurate, and auditable. The next step is to integrate real-time regulatory feeds and rigorous evaluation pipelines to evolve this POC into a production-grade platform.
- Article · AI Agents
The Silent Killer of AI: Detecting and Preventing Target Leakage in Enterprise RAG
Target leakage is the difference between a model that works in a notebook and one that works in the world. By implementing a Multi-Agent Leakage Prevention Pipeline in LangGraph, you ensure that your Enterprise RAG system is not just smart, but also honest. For FinBank, this means their loan default predictions are based on what they actually know about the applicant today, not on what happened to them tomorrow. This rigor is what separates experimental AI from enterprise-grade reliability.
- Article · AI Agents
Implementing Human-in-the-Loop (HITL) in Enterprise LangGraph: The Ultimate Safety Net
Human-in-the-Loop is not a bottleneck; it is the ultimate safety valve for enterprise AI. By leveraging LangGraph’s native interrupt() and Checkpointer mechanisms, you can build systems that combine the speed and analytical power of LLM-driven RAG with the judgment, accountability, and regulatory compliance of human experts.
- Article · Langchain
Mastering Context Management in AI Agents: Why LangChain is Essential for Enterprise Developers
Learn how LangChain enables enterprise AI agents to manage context, memory, stateful workflows, RAG, LangGraph orchestration, and observability for production systems.
- Article · AI Agents
Preventing Infinite Loops in Enterprise Multi-Agent LangGraph Systems
Preventing infinite loops in multi-agent systems isn't about hoping for the best; it's about designing for the worst. By combining hard step limits, state-aware routing, and semantic loop detection (checking if we're doing the same thing twice), you can build an Enterprise RAG system that is not only intelligent but also incredibly resilient.
- Article · Langchain
Tracking Metrics for Individual Nodes in LangGraph
Treating a LangGraph agent as a black box hides the real sources of failure. By instrumenting each node with latency, error, domain-quality, and cost metrics and shipping them to Prometheus + LangSmith you turn an opaque orchestration into a measurable system you can actually improve.
- Article · Langchain
LangChain Stock Research Agent v3 | DeepAgent Multi-Agent Finance AI
Explore the “Stock Research Agent v3” from LangChain—a multi-agent, deep-research AI system built with LangGraph and LangSmith for advanced financial research. Understand architecture, capabilities, use-cases, limitations, and how you can apply it.
