Enterprise hits and misses - agents need meaning, not just data. Anthropic grapples with model pricing, and retailers get an economic gut check.

Lead story -Meaning, meet action! Why context engineering needs context

We said "no good AI without data quality" - we were wrong (kind of). We thought "you need to serve up the right context for thx LLM" - wrong again, kind of.

The most effective type of enterprise AI is about context, but the right context architecture is no small feat. And: even the right context doesn't necessarily lead to the right outcome, despite what some over-excited vendors may imply. That's where the so-called "harness" comes in, as we attempt to verify/validate LLM output before the recommended API call is made.

For busy decision makers with ice cream headaches, it comes down to this: for enterprise-grade results, probabilistic LLM agents need architectural constraints and data context, not just quality. Make sure you have tech teams (or trusted partners) who can do this. So what...

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