The architecture is typically hybrid: edge handles latency-sensitive control, cloud platforms handle analytics and AI development at scale.
SYSTEMS
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LangSmith LLM Gateway for Redacting Sensitive Data in LLM Requests
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Before LangSmith LLM Gateway: An agent processes a request that includes a SSN. It now sits in LLM provider logs, in trace data, + possibly in downstream systems that consumed the response. With LangSmith LLM Gateway: Data is redacted from requests before it hits a model or
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Deep Agents v0.6 Introduces ContextHubBackend for Agent File Management
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New in Deep Agents v0.6: ContextHubBackend A versioned home for the files that power agent behavior, backed by LangSmith Context Hub, enabling context improvements from one run to the next.
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Anthropic Releases Claude Opus 4.8: Faster, Cheaper, and Smarter
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ANTHROPIC ACABA DE LANZAR CLAUDE OPUS 4.8
— Nico (@nicos_ai) 28 mai 2026
Y puede que sea su mejor modelo hasta la fecha:
→ 2.5x más rápido y 3x más barato con modo /fast
→ Trabaja SOLO como un ingeniero senior, sin que lo supervises
→ Lanza cientos de subagentes en paralelo para completar tareas complejas… https://t.co/QSIQ1rvfpo pic.twitter.com/2cNoRMTRDgANTHROPIC JUST RELEASED CLAUDE OPUS 4.8 And it might just be their best model to date: → 2.5x faster and 3x cheaper with /fast mode
→ Works ONLY like a senior engineer, no supervision needed
→ Launches hundreds of subagents in parallel to handle complex tasks on its own And -
New LangChain Academy Course on Scaling Deep Agent Deployment
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New LangChain Academy Course: Intro to LangSmith Deployment
— LangChain (@LangChain) 28 mai 2026
In this course, you’ll learn how to scale a single-user desktop Deep Agent all the way to a multi-tenant deployment running on managed, elastic infrastructure. pic.twitter.com/pFXZRhTZDHNew LangChain Academy Course: Intro to LangSmith Deployment In this course, you’ll learn how to scale a single-user desktop Deep Agent all the way to a multi-tenant deployment running on managed, elastic infrastructure.
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RL Inference Stack Development in C for GB300 Hardware
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Next will be writing the inference stack in C for simultaneous high-speed RL across a large block of GB300s. (We do use a little C++ tbh, but not much)
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AI Agents Need to Learn from Executions, Not Just Complexity
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Exacto. Ese es el problema que nadie estaba atacando. Todos haciendo agentes más complejos pero ninguno que realmente aprenda de sus propias ejecuciones
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AI Agentic Systems Hallucination Compliance Framework
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Important post for entrepreneurs from @a16z yesterday and a look at a new system that ensures AI agentic systems don't hallucinate their way into compliance hell.
— Robert Scoble (@Scobleizer) 28 mai 2026
"The value comes less from the underlying model’s raw capability (though that’s still important!) than from the… https://t.co/tmh6mxv3fc pic.twitter.com/9pgzceZNZMImportant post for entrepreneurs from @a16z yesterday and a look at a new system that ensures AI agentic systems don't hallucinate their way into compliance hell. "The value comes less from the underlying model’s raw capability (though that’s still important!) than from the
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Standardized Agent Harnesses Drive Managed Services Growth
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As agent harnesses become more standardized, we’re going to see a lot more “managed agent services”
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AI Agents Need to Learn from Executions for Meaningful Functionality
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I've been waiting for something like this for a while. Agents that don't learn from their executions make no sense.