MIRAGE
Model-Independent Agent Continuity
A continuity architecture that lets an active AI-agent task transition safely between a cloud LLM and a local SLM when internet connectivity becomes slow, unreliable, or unavailable.
This is an active research and prior-art area. The description below is intentionally high-level — it explains the problem and the shape of the solution without publishing implementation details.
At a Glance
The problem
Long-running AI agents can be interrupted by lost connectivity, slow cloud responses, provider outages, or model unavailability. Ordinary chat-history transfer isn’t enough — an agent has to preserve its active objective, decisions already made, tools already used, and pending operations.
Without reliable continuity, an agent can repeat an action, contradict an earlier decision, or continue from an inconsistent state — including duplicating external side effects like a sent message or a payment.
The approach
MIRAGE transfers a model-independent execution state rather than merely copying conversation history. A first model prepares a documented, compressed transition state; a second model reconstructs the active task, verifies consistency, and continues only after passing a reliability check.
When the original model or cloud provider becomes available again, results are reconciled without repeating completed actions.
What it addresses
What’s not public
Given active patent and prior-art work, this page deliberately omits internal mechanisms, checkpoint schemas, reconciliation algorithms, reliability metrics, and implementation details. No source repository is linked while filings are in progress.
Frequently Asked Questions
What problem does MIRAGE address?
Long-running AI agents can be interrupted by lost connectivity, slow cloud responses, or model unavailability. Ordinary chat-history transfer isn't enough to preserve an agent's active objective and pending operations safely.
How does MIRAGE keep an agent's work consistent?
It transfers a model-independent execution state rather than copying conversation history — a second model reconstructs the active task and verifies consistency before continuing, preventing duplicated side effects.
Why isn't more implementation detail public?
MIRAGE is an active research and prior-art area with filings in progress, so checkpoint schemas, reconciliation algorithms, and reliability metrics are intentionally not published.