Entire is a new developer platform built to solve the 'missing middle' of the SDLC in the agent era. It captures context, decisions, and evaluations from intent to production, reimagining the PR into 'Trails' and integrating programmable 'Runners' and 'Gates.'
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For enterprise data, simple file traversal fails. We need an agentic 'harness' that combines hybrid search, metadata filtering, and specialized parsing (LlamaParse) to ground answers from massive, multimodal corporate documents.
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AI companies are accidentally building banks. Scaling AI requires moving beyond simple usage billing to complex financial infrastructure that checks entitlements synchronously before consumption.
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AI struggles with production because it spans code, infrastructure, and telemetry. To fix this, a robust agentic harness requires six pillars: Model Orchestration, Causal Reasoning, Context Engineering, and more.
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Discover the top 3 audio model launches from Gemini Audio at Night, including personalized TTS, 100+ language live translation, and multimodal function calls for developers.
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Learn how to scale databases for the AI era. Ben Dicken covers essential principles like sharding, back pressure, and decoupling, and shows how AI agents can safely manage infrastructure using VSchema and Traffic Control.
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The future of AI infrastructure demands a shift from separated cloud layers to a co-designed stack. Speakers detailed the move from pull-based web search to agent-driven, push-based data consumption, emphasizing the need for GPU-native indexing and robust, holistic security.
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Building agent quality platforms is a complex systems challenge. Learn why simple spreadsheets fail and how to build the continuous improvement loop using advanced tracing and observability.
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Marc Klingen details the self-improving OSS agent stack, showing how AI can automate the loop between online tracing, offline datasets, and evaluations, moving teams from manual to automated agent refinement.
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Sydney Runkle details how to build a model router in the harness to reduce operational costs. By analyzing task traces and selecting models along the Pareto frontier, the router cut median cost per thread by 64%.
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EmbeddingGemma 2 is a lightweight, open model for natively multimodal embeddings. It maps text, images, video, and audio into a single unified embedding space, enabling powerful, fully offline search and RAG pipelines on edge devices.
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CoreWeave unveiled Forge, a unified platform for the AI Loop (Run, Observe, Curate, Improve, Evaluate). New hardware like the Vera Rubin NVL72 and Vera CPU accelerate the deployment of complex, agentic AI systems from concept to production.
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Web search can allow your agent to conduct external research and add that to information that you've already built in with your existing set of tools and skills. LangChain provides built-in search using parallel as part of the LangSmith platform.
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CTO Jacob Lauritzen details how Legora scales with AI agents, discussing custom orchestration tools, agentic bug-fix loops, and why measuring AI adoption by tokens is a flawed metric.
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The biggest AI models require distributed inference because they exceed single GPU memory capacity. Learn how Data, Pipeline, Tensor, and Expert parallelism scale LLMs across multiple GPUs.
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Codex is demonstrated as an advanced AI agent capable of automating complex, multi-stage development workflows. Learn how to use Goals, Subagents, and the App Server to build 'software factories' and manage entire CI/CD pipelines.
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Discover how merchants can accept payments initiated by AI agents using protocols like ACP and UCP. Learn about MCP apps, tokenization, and the trade-offs between embedded and external checkout flows.
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This workshop details how to master AI Observability by building a continuous agent improvement flywheel. Using Braintrust, engineers can derive signal from production traces via custom scoring and Topics, automating the process of identifying failures and proposing code changes.
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To ensure AI safety, agents must run in sandboxes that are modeled after the task's intent. Safety comes from limiting tools and context, not from the harness itself.
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CoreWeave Forge demonstrates the full AI loop: Observe, Curate, Improve, and Evaluate. Engineers can systematically improve AI agents using Agent Lens, Serverless RL, and model lineage tracking.
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