As agent-based systems evolve, one design question keeps coming up: 🤔 who decides the work is done? In VentureBeat, our very own Sean Brownell shares why separating the builder from the evaluator isn’t new — but remains one of the most important patterns for building reliable, observable AI systems. The takeaway: 💡 what works for deterministic tasks doesn’t always translate to subjective or design-driven work — where human judgment still plays a critical role. A thoughtful perspective on where agent orchestration is heading: https://lnkd.in/gmdCNdiB #enterpriseAI #aiengineering #agentarchitecture
Agent Orchestration: Where Human Judgment Meets AI
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There’s a growing conversation around how AI agents decide when work is complete — and not all approaches are equal. This VentureBeat article features Sprinklr’s Sean Brownell and breaks down why separating execution from evaluation is such a critical design choice — and where it actually holds up in practice. An important lens for how enterprise AI is evolving beyond the demo stage. 👉 http://ms.spr.ly/6049vpDQc
Anthropic's Claude Code adds a built-in evaluator to catch agents that quit too soon venturebeat.com To view or add a comment, sign in
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There’s a growing conversation around how AI agents decide when work is complete — and not all approaches are equal. This VentureBeat article features Sprinklr’s Sean Brownell and breaks down why separating execution from evaluation is such a critical design choice — and where it actually holds up in practice. An important lens for how enterprise AI is evolving beyond the demo stage. 👉 http://ms.spr.ly/6047vTVAN
Anthropic's Claude Code adds a built-in evaluator to catch agents that quit too soon venturebeat.com To view or add a comment, sign in
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There’s a growing conversation around how AI agents decide when work is finished — and not all approaches are equal. This VentureBeat article features Sprinklr’s Sean Brownell and breaks down why separating execution from evaluation is such a critical design choice — and where it actually holds up in practice. An important lens for how enterprise AI is evolving beyond the demo stage. 👉 http://ms.spr.ly/6043vpG0N
Anthropic's Claude Code adds a built-in evaluator to catch agents that quit too soon venturebeat.com To view or add a comment, sign in
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Every article we publish from today carries an inline flow diagram that compresses the story's argument into four to six color-coded steps, with a one-click Markdown export for AI agents. And the Agent Readiness Score is now a public API across REST, MCP, n8n, Zapier, Make, and a Claude.ai connector.
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DeepSeek just dropped V4. Open-sourced. 1.6T parameters. 1M context window as the default. Benchmarks rivaling the best closed models in the world. Six months ago, that was a moat. Today it is a Hugging Face download. This is not a surprise if you have been paying attention. It is a pattern. Capabilities that cost hundreds of millions to develop reach open availability within months of their closed-source counterparts. The compression is relentless and it is accelerating. Intelligence, the narrow computational kind, is becoming infrastructure. What TCP/IP did to communication, commoditized AI is doing to cognition. The question worth asking is not which model wins. It is what remains scarce once the model stops being scarce. The answer: judgment. The ability to decide what to trust, where the system breaks, what to build on top of it, and how to adapt when the capability layer shifts again next quarter, because it will. https://lnkd.in/gxnmnn6m #AI #DeepSeek #LLM #Agentic
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Chinese DeepSeek AI released V4 Preview as an open-source model family with a claimed 1M-token context window. Long-context models are becoming dramatically cheaper and more widely distributed. OpenAI, Anthropic, and Google now face even more pricing pressure at the lower and mid tiers as they compete with a parallel AI stack emerging outside U.S. control. DeepSeek AI is not just a model competitor. It is a pressure mechanism on the entire Western AI margin structure. If good-enough long-context reasoning becomes cheap, application defensibility must come from workflow, data, and distribution. So anyone building the new rule of thumb is to use frontier models where judgement matters and use cheaper/open models for intake, extraction, classification, summarisation, and retrieval. https://lnkd.in/dDCyZ-Tb
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Claude Code Hooks: Deterministic Control Over AI Workflows While claude.md instructions are treated as suggestions, Hooks provide deterministic guarantees. Learn how to use pre- and post-tool hooks to enforce formatting, block dangerous commands, and standardize your team's workflow.
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@VentureBeat The #AI scaffolding layer is collapsing — and LlamaIndex's CEO says that's exactly what should happen. What survives when the framework era ends. https://lnkd.in/ex_MRf9v
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Neuron AI now supports Parallel Branches execution! If you have a file, and you want to extract structured data from it while simultaneously generating a description. These two tasks don’t depend on each other. There’s no reason to wait for one before starting the other. Run them in parallel will took half of time. https://lnkd.in/d-uKHu-G
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seen this a lot, where human judgment ends up catching stuff AI just cant spot, especially in creative or design-heavy gigs.