Aether: GCP-native framework to terminate LLM agent drift

Aether tackles one of the most persistent headaches in LLM orchestration: the fragility of the prompt. The project introduces the concept of Weighted Intent Tokens (WIT), essentially attempting to compile fuzzy human instructions into strict vector representations. The goal is to move away from the 'voodoo' of prompt engineering and toward a deterministic control plane for agent behavior, effectively treating intent as a data type rather than a suggestion. From a technical standpoint, the project is in its absolute infancy. The provided core consists primarily of a Python-based compiler logic meant to stabilize agent output. By substituting ambiguous strings with weighted vectors, Aether seeks to prevent 'Context Rot'—the phenomenon where long-context agents lose the plot or drift from their original system instructions. It is a bold architectural bet: that we can bypass the inherent randomness of natural language interfaces through a mathematical proxy. However, the current transparency is low. While the vision is compelling, the available open-source core is sparse, and the heavy lifting appears to be locked behind the full framework available via Gumroad. For a tool promising 'reliability' and 'strictness,' the lack of comprehensive documentation, benchmarks, or a robust test suite in the public repo is a red flag for production-grade adoption. This is for the experimental agent builder who is tired of their bots hallucinating their personas mid-session. If you are building complex, multi-step autonomous workflows on GCP and have hit the ceiling of what 'be more concise' can achieve in a system prompt, Aether's approach is worth a look—provided you're comfortable acting as a beta tester for an early-stage primitive.