Context engineering is the practice of designing and managing the complete information environment that AI agents use to reason and act. Unlike prompt engineering, which optimizes a single input prompt, context engineering builds a complete system of knowledge, memory, tools, and governance that...
Large language models are confident, eloquent, and often get questions about your business wrong. The reason is that they weren’t trained on your data. They know the world in general, but they don’t know your return policy, your product specifications, or the current terms of your contracts....
Most enterprise companies don’t have a knowledge problem. They just have a problem accessing that knowledge. This is a mistake most companies make: you know the answers exist, but they’re scattered all over the place. When a new employee joins your team, they don’t know where to look. A support...
Nearly 80% of corporate data is unstructured. It can be found anywhere: in documents, emails, support tickets, call transcripts, and so on. This data accumulates over time – one year, two years, five years, and so on. But now, enterprise AI promises to turn this massive amount of data into...
When an AI agent invents a return policy that doesn’t exist or refers to a contract clause that never existed, these are AI hallucinations. In a consumer chatbot, this is simply annoying. In an enterprise agent that responds to customers or makes operational decisions, however, it poses a massive...