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Enterprise AI agents are stalling — not because of model performance, but because of permissioning. Every agentic workflow eventually hits the same wall: what is this agent allowed to touch, on whose behalf, and how does the system know?Workday's answer is to make its existing system of record the governance layer for agents. Gerrit Kazmaier, the company's president for product and technology, told VentureBeat in an interview that customers often struggle when they cobble together solutions for their agents. “Sana makes sure the integrity of the approvals and security model is always adhered to,” Kazmaier said. “Frankly, that’s where we see customers struggling when they try to build do-it–yourself AI by just accessing raw data, so the richness of the security model gets lost, and the results become overly broad.”Workday, which

Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a framework from researchers at multiple universities, encodes new knowledge into a dedicated smaller memory model that operates separately from the main LLM.The modular architecture works with both open- and closed-source models and sidesteps the complexity of RAG pipelines and full model retraining.Experiments show that MeMo handles complex queries reliably even when retrieval pipelines are noisy. It avoids the catastrophic forgetting associated with direct fine-tuning and provides a cost-effective pathway for continuous knowledge updates.The challenge of updating LLM memoryLarge language models are frozen after training and their internal knowledge remains static until they undergo subsequent, computat

At 620 million monthly users, calling a frontier model for every image recommendation isn't a strategy — it's a bill. Pinterest CTO Matt Madrigal solved it by gutting Qwen3-VL's vision layer and rebuilding it with proprietary embeddings, cutting costs 90% and boosting accuracy 30%.Madrigal’s team has been heavily investing in customizing open-source models “foundationally in-house.” “If you've got really unique data that you can then fine-tune an open source model with, data quality will, frankly, outweigh or overcome model size,” Madrigal explained in a recent VB Beyond the Pilot podcast. How Pinterest customized Qwen for visual discoveryPinterest, which has around 620 million monthly active users, has long applied open source models for visual search and discovery, going back to Google’s BERT and OpenAI’s CLIP. The company fine-tuned its own Pin C

As enterprise AI agents move into production, organizations are confronting a growing reliability problem. Many teams are discovering that LLM performance alone does not determine whether agents succeed in production. Long-running AI workflows must survive crashes, preserve state, recover from failures, manage inference costs, and coordinate across APIs, tools, and enterprise systems.After a first wave focused on rapid deployment, organizations now need to revisit those first-generation implementations, and redesign early agent architectures around workflow orchestration, observability, governance, and recovery, said Preeti Somal, Senior VP Engineering at Temporal Technologies, during the latest AI Impact Series event in New York. “We do have a lot of customers that come to us where they’re building version 2.0 of the same agent,” Somal said. “They had to move really fast, but they didn’t take care of the plumbing. Things crash and burn, and then they’re ba

Test-time scaling (TTS) has emerged as a proven method to improve the performance of large language models in real-world applications by giving them extra compute cycles at inference time. However, TTS strategies have historically been handcrafted, relying heavily on human intuition to dictate the rules of the model’s reasoning. To address this bottleneck, researchers from Meta, Google, and several universities have introduced AutoTTS, a framework that automatically discovers optimal TTS strategies. This automated approach allows enterprise organizations to dynamically optimize compute allocation without manually tuning heuristics. By implementing the optimal strategies discovered by AutoTTS, organizations can directly reduce the token usage and operational costs of deploying advanced reasoning models in production environments. In experimental trials, AutoTTS managed inference budgets efficiently, successfully reducin

Mistral AI used its inaugural conference on Wednesday to announce a sweeping expansion into industrial manufacturing, a new inference data center south of Paris, and a rebranding of its consumer-facing assistant — moves that collectively signal the three-year-old French startup's ambition to become the enterprise AI provider of record for companies that refuse to hand their most sensitive data to American hyperscalers.At the AI NOW Summit, held at a venue in central Paris, co-founder and CEO Arthur Mensch took the stage alongside CTO Timothée Lacroix and Chief Scientist Guillaume Lample to lay out a strategy that stretches from bare-metal GPU clusters to physics simulations for aircraft wings. The company disclosed that it now employs 1,000 people and is targeting €1 billion ($1.17B USD) in revenue for 2026 — a figure that, if achieved, would be an extraordinary growth trajectory for a compan

Anthropic today released Claude Opus 4.8, an upgrade to its flagship model that ships at the same price as its predecessor, alongside a dramatically cheaper "fast mode" tier and a new feature that lets the model spawn hundreds of parallel subagents for codebase-scale work.The model is available immediately across Anthropic's surfaces — claude.ai, Claude Code, the API, and Cowork — at unchanged pricing: $5 per million input tokens and $25 per million output tokens. Developers can call it as claude-opus-4-8.The headline efficiency story is fast mode. Anthropic has slashed the price of running Opus 4.8 in fast mode — where the model produces tokens at roughly 2.5x normal speed — to $10 per million input tokens and $50 per million output tokens, down from $30/$150 for Opus 4.7That's a 3X reduction from the fast-mode pricing of previous models, and brings high-throug