5/5 Maestro automatically searches across this space (model ensembles, scaling strategies, execution policies), maps the Pareto frontier, and surfaces the full accuracy–cost–latency tradeoff surface. Read the full methodology here:
AGENTS
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Sequential vs Batched Execution Trade-offs in LLM Ensembles
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3/5 Execution policies help, too: you can see how sequential execution (vs. batched execution) saves spend but drives up latency for the same GPT-5 + MiniMax ensemble.
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AI21 Maestro Achieves SOTA on BrowseComp-Plus with 95.18% Accuracy
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1/5 We hit SOTA performance on BrowseComp-Plus with 95.18% accuracy using AI21 Maestro’s agent optimization. Here’s how we automated the search space and reached #1.
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Out-of-the-Box Frontend UI for AI Agents with Streaming
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[frontend] UI/UX is a surprisingly big part of building agents! It takes a while to hook up streaming, thread history, all the endpoints We do it for you! Get a frontend you can expose to users out of the box
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LangChain DeepAgents Deployment Now Available with Weekly Updates
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Checkout DeepAgents deploy here: https://
docs.langchain.com/oss/python/dee
pagents/deploy
… We're shipping updates every week (almost every day). What would you like to see next? -
DeepAgents Sandbox Integration with Modal, Daytona, and Runloop
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[sandbox] DeepAgents can connect to a sandbox to run! We integrate with @modal @daytonaio @RunloopDev Each provider has its own set of parameters, so be sure to check that! Docs: https://
docs.langchain.com/oss/python/dee
pagents/deploy#sandbox
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Multi-Tenant Auth for AI Agents in Production with Clerk and Supabase
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[auth] Part of deploying agents to production is deploying them in a multi-tenant way Auth is the way to do that! We support integrations with @clerk and @supabase
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LangSmith Agent Deployment with Multi-Provider Model Support
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[agent] This is where you specify the name of the agent – this is the deployment name in LangSmith This is also where you specify the model name. We support a lot of models! Not only ones from OpenAI, Anthropic, Google, but also @OpenRouter @FireworksAI_HQ @baseten @nvidia and
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DeepAgents Deploy: Configuration-Driven Cloud Agent Harness Setup
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DeepAgents deploy is a simple, configuration driven way to get an agent harness deployed to the cloud deepagents.toml is the file that configures it. It has four sections:
– agent
– sandbox
– auth
-frontend Here's what each one does -
Hassabis: AGI Is Close, Missing Continual Learning and Memory
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Demis Hassabis: We're on the right track to AGI; we probably have all the components. We're just missing a few things like continual learning and solving the memory problem. pic.twitter.com/zuIKOiKnB7
— Chubby♨️ (@kimmonismus) 30 avril 2026Demis Hassabis: We're on the right track to AGI; we probably have all the components. We're just missing a few things like continual learning and solving the memory problem.