An adaptive agent runtime that uses frontier models to plan and smaller, specialized models to do the work.
Uraion Fabric is in development and has not been released.
The frontier model handles planning, difficult reasoning, delegation, and escalation.
Smaller models handle focused tasks such as coding, research, tool use, and verification.
Memory stores useful solutions and failures. The system can reuse what it learns without retraining for each user.
This is a high-level design. The implementation may change as the system develops.
Frontier models handle the planning and reasoning that need them.
Smaller models handle focused tasks.
Keep useful procedures, environment details, and past failures across runs.
Add user-specific lessons to memory without waiting for fine-tuning.
Start with the smallest capable model and escalate when needed.
Run independent tasks at the same time while preserving dependencies.
Run sensitive work and smaller models on controlled infrastructure.
Check results with tests, tool output, and structured review.
Work across frontier providers and open-model families.
Use validated experience to improve memory now and train better worker models later.
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