Today’s LLMs excel at reasoning, but can still struggle with context. This is particularly true in real-time ordering systems like Instacart. Instacart CTO Anirban Kundu calls it the "brownie recipe problem." It's not as simple as telling an LLM ‘I want to make brownies.’ To be truly assistive when planning the meal, the model must go beyond that simple directive to understand what’s available in the user’s market based on their preferences — say, organic eggs versus regular eggs — and factor that into what’s deliverable in their geography so food doesn’t spoil. This among other critical factors. For Instacart, the challenge is juggling latency with the right mix of context to provide experiences in, ideally, less than one second’s time. “If reasonin [...]
A new framework from Stanford University and SambaNova addresses a critical challenge in building robust AI agents: context engineering. Called Agentic Context Engineering (ACE), the framework automat [...]
For all their superhuman power, today’s AI models suffer from a surprisingly human flaw: They forget. Give an AI assistant a sprawling conversation, a multi-step reasoning task or a project spanning [...]
Enterprise AI agents today face a fundamental timing problem: They can't easily act on critical business events because they aren't always aware of them in real-time.The challenge is infrast [...]
One employee at Vercel adopted an AI tool. One employee at that AI vendor got hit with an infostealer. That combination created a walk-in path to Vercel’s production environments through an OAuth gr [...]
When startup fundraising platform VentureCrowd began deploying AI coding agents, they saw the same gains as other enterprises: they cut the front-end development cycle by 90% in some projects.However, [...]
Enterprise AI agents have a new production failure mode, and it is not the model. As enterprises move from single-layer RAG to hybrid retrieval architectures, the same underlying data produces differe [...]
Enterprise teams keep watching the same thing happen. An AI agent demos beautifully, goes to production, and stalls: it runs for a short stretch, then needs a human to top up its context and check its [...]