One substrate. Longer work. Any provider.
The work should
not reset.
Context Continuity is a near-ready, provider-agnostic substrate for AI agents working across long, multi-step tasks.
Long-horizon work needs a place to persist.
Agents that work across many steps need more than a single context window. The intended outcome is simple: one consistent foundation that helps the work stay coherent as its context changes.
A persistent thread through changing layers.
The intended outcome
Keep the task in view while the context turns over.
Context Continuity is being built for teams that are assembling agent products or platforms across providers and well-known harnesses. It is a portable enabling layer, not a replacement for the agent you already run.
Portable by intent
One enabling layer for the work beneath the model.
The promise is portability: give technical teams a consistent substrate across supported providers and well-known harnesses, instead of recreating the enabling foundation for each ecosystem.
One run, carried across stages.
Proof should be scoped to the workload.
Exact proof points, supported integrations, and observed reliability results are still being validated. That boundary matters. The first conversation should start with a real workload, its interruptions, and the harnesses that matter to your team.
What to evaluate together
No broader claim is implied.
Bring the workload that cannot afford a reset.
Tell us what your agent needs to carry, where the context changes, and which providers or harnesses you need to keep in play.
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