AI doesn't need more knowledge.
It needs coherence.
Willow preserves the active frame so AI systems keep the goal, constraints, and decisions intact across long workflows, tool calls, and model sessions.
A continuity and coherence layer between your app and the model — designed to keep threads oriented so models stay aligned with intent as conversations and agents run longer.
Thread drift is invisible until it isn't.
No error is thrown. No exception is caught. The model produces fluent, plausible output that quietly walks away from the original intent. By the time a human notices, the agent has already acted on it.
A user opens a thread. The model answers.
Three turns later, the agent has invented a new constraint.
By turn nine, the response no longer matches the brief.
The thread is intact. The intent has drifted.
Willow maintains continuity.
Models generate language.
Language models are excellent at producing the next token. They are not designed to hold intent across long workflows. Willow is.
Preserves the active frame
Goal, constraints, and prior decisions stay intact as the thread grows, tools run, and models switch.
Detects fragmentation risk
Continuous signal on when a thread is drifting, when to verify, and when to re-anchor before output is acted on.
Adapts provider behavior
Recommended actions and adaptive controls flow into whichever provider your app is calling — OpenAI, Anthropic, Gemini, or OSS.
Models generate language. Willow maintains the active frame.
The 5-phase loop.
Anchor → Stabilize.
Every Willow-instrumented thread cycles through five phases. Hover a phase to inspect.
Anchor
Bind the thread to declared intent. Capture operator context and origin vectors.
Estimate your context waste.
Model what Willow could save.
AI workflows lose money when context gets too large, stale, or fragmented. Model how much Willow could save by compressing continuity and reducing broken-session repair.
Your workload
The share of your AI usage involving long conversations, agents, workflows, project state, or cross-session context.
Many agentic workflows spend heavily on repeated input context.
Time spent re-prompting, rebuilding context, rerunning agents, or manually correcting drift.
- · 85.4% compact-artifact token reduction
- · 2.25× fresh-session continuity resume lift
- · 55% manual repair reduction estimate
Based on controlled private Willow tests (85.4% compact-artifact token reduction; 2.25× fresh-session continuity resume lift). Plan pricing shown is an example estimate while pricing is being finalized. Actual savings depend on workload, model pricing, usage patterns, and implementation.
Two surfaces.
One continuity engine underneath.
Willow Core
Continuity infrastructure for developers. API, MCP server, and Python SDK for agents, long-running workflows, and multi-model systems.
- REST + Streaming
- MCP tools
- Python SDK
Willow Observatory
The environment where people talk, investigate, organize, build, and return to the thread — with continuity held underneath every session.
- One continuous thread
- Investigate for more perspectives
- Organize and build in place
Investigate with more perspectives.
Bring in additional models when a question needs more depth. Willow preserves the goal, constraints, and decisions, surfaces disagreement, and returns one coherent synthesis — without making you leave the Observatory.
One question, several perspectives
Ask once. Willow gathers the perspectives that add depth, rather than repeating the same answer.
Contradictions surfaced, not buried
Where perspectives disagree, the disagreement is shown plainly instead of averaged away.
Willow synthesis keeps the thread intact
One coherent synthesis comes back into the same conversation, with the original goal and constraints still held.
Advanced model details stay optional
Per-model detail is there when you want it, and quietly out of the way when you don't.
Careful, not theatrical.
Only what we can source.
Willow is in Developer Preview. These are controlled private tests and focused provider validations — the honest floor, not a marketing ceiling.
| Signal | Result | Context |
|---|---|---|
| Live Core smoke — API, SDK, MCP, multi-model paths | passed | controlled launch smoke |
| CoherenceSignal fields returned in live responses | 4 fields | thread_integrity_score · fragmentation_risk · recommended_action · intent_vector |
| Provider-adaptive controls generated consistently | consistent | focused provider validation — provider compliance varied |
| Continuity artifact vs. source transcript | ~14.6% | synthetic long-context compression test |
| Gemini provider-adaptive followed-rate | 37.5% → 62.5% | focused validation · high-load/urgent reached 100% after policy refinement |
| Automated tests passing | 63 | launch smoke / test run |
Get instrumented.
Ship with coherence.
Limited preview cohort. Credentials provisioned within 48 hours.
01{02 "thread_integrity": 84.2,03 "fragmentation_risk": "watch",04 "recommended_action": "verify"05}