Continuity is more than memory.

ContinuityRuntime checkpoints execution so long-running work can survive worker termination, application restarts, infrastructure changes and temporary failures without rebuilding the entire task from the beginning.

Capture safe execution boundaries with the state required to reconstruct the work later. Recovery begins from a known checkpoint rather than guessing what happened before the interruption.

When execution disappears unexpectedly, the runtime reconstructs the latest safe state, validates what already happened and resumes eligible work from the appropriate boundary.

Sessions become temporary execution windows rather than the boundary of the task. Agents can return later with the objective, current state, completed steps and unresolved work intact.

The runtime separates persistent work state from any individual inference engine. A different compatible model can inherit the validated state and continue the execution without treating the migration as a brand-new task.

Continuity packages the current state so another agent or worker can take responsibility with a clear understanding of what happened, what remains and where execution can safely continue.

Research agents, coding agents, operational agents and autonomous services increasingly need execution lifetimes much longer than a single inference call. ContinuityRuntime is designed around the lifetime of the objective rather than the lifetime of the process.

Long-running work often waits on humans, APIs, approvals, schedules or external events. Persist execution while nothing needs to run, then rehydrate the task when its next condition arrives.

Recovery needs awareness of actions already committed outside the runtime. Track execution boundaries and operation identifiers so resumed workflows can avoid blindly repeating payments, messages, writes, deployments or other external actions.

Maintain an inspectable chain showing which agent executed the work, which model was used, which checkpoint was restored, what changed and how the current execution descended from previous states.

Applications shouldn't have to reinvent durable state, recovery and continuation logic for every agent. Expose continuity through a runtime API for checkpointing, suspension, restoration, handoff and execution history.

A persistent objective can move through specialized agents without losing the shared working state. Planning, research, coding, verification and deployment become different phases of one continuous execution.

Approval requests and human interventions become durable events in the execution history. Someone can return hours or days later and see what needs a decision and why.

When execution enters an invalid state, operators can inspect prior checkpoints and restore an earlier known-good boundary while preserving the history of how the system arrived there.

Operate long-running agents through a control plane for active runs, sleeping tasks, checkpoints, failures, recoveries, migrations, handoffs and completion state.

Containers, workers, machines and inference providers are replaceable execution resources. The continuity record remains the durable representation of the work itself.

A persistent execution layer that preserves the state and lineage of long-running intelligent systems across time, interruption and changing infrastructure.