Presented by SolidigmAs inference workloads evolve from discrete question-and-answer exchanges into persistent, multi-step agentic systems, GPU availability is no longer the most critical AI bottleneck. Instead, the bottleneck has migrated from compute to context, says Jeff Harthorn, AI applied research lead at Solidigm."Why context management has become a primary bottleneck, more than GPU availability or compute efficiency, is the question of 2026," says Harthorn. "GPUs have gotten dramatically cheaper per FLOP. Model architectures and inference serving engines have all gotten much more efficient. But the thing that's grown faster than both of those is context. The persistent state that has to live between sessions has grown even faster than context itself."It [...]
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 [...]
AI agents forget. Every time a coding assistant loses track of a debugging thread, or a data analysis agent re-ingests the same context it already processed, the team pays in latency, token costs, and [...]
RAG isn't always fast enough or intelligent enough for modern agentic AI workflows. As teams move from short-lived chatbots to long-running, tool-heavy agents embedded in production systems, thos [...]
Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context sourc [...]
An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pull [...]
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 [...]
Enterprise AI applications that handle large documents or long-horizon tasks face a severe memory bottleneck. As the context grows longer, so does the KV cache, the area where the model’s working me [...]
Redis built its name as the caching layer that kept web applications from collapsing under load. The problem it is targeting now has the same structure but is harder to solve: production AI agents fai [...]