Fine-tuning forgets. RAG leaks context. Hypernetworks build the model your agent needs on demand.

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 output, and the promised… 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 output, and the promised efficiency drains into supervision. The agent did the work; you did the watching. It’s one reason so many agent pilots never turn into production systems.The pitch on the other side of that wall is the one every team wants to believe: an agent that runs a long job on its own, overnight if it has to, and leaves a person to validate only the last 10%. Whether that is achievable turns on a problem the orchestration conversation mostly skips. When AI firm Chroma tested 18 leading models, every one lost accuracy as its input grew, a property of how attention works, not a gap a stronger model closes. An agent fed more and more of your business as it runs…