AI needs more than intelligence. It needs continuity.
Eight co-equal axes. No center, no hierarchy.
The continuity field, mapped along eight co-equal axes. Select any to see what it covers.
Why continuity matters
AI interactions are powerful but volatile — that’s why AI keeps forgetting. Every new session, tool, and model can drop the thread; context disappears, drifts, or becomes unsafe when it is not governed.
AI interactions are powerful but volatile; context disappears, drifts, or becomes unsafe when it is not governed.
The Continuity Layer is the durable responsibility for deciding what carries forward across models, tools, agents, and sessions.
Memory stores. Retrieval finds. Agents act. Runtime executes. Continuity governs what carries forward.
- 01
The problem isn’t memory. It’s everything around it.
Storing facts is largely solved. What still evaporates between sessions is state: decisions, the reasons behind them, working assumptions, unresolved threads, and where each piece of truth came from.
- 02
Continuity is the layer that carries understanding forward.
It’s what lets intelligence keep what it has learned, decided, inferred, promised, questioned, and left unfinished — across chats, tools, agents, and time.
- 03
Agents make this urgent.
Every new orchestrator spawns more carriers of partial memory and stale assumptions. More agents means more coordination problems — unless they converge on one shared account that is authoritative for that project's governed state, so people and agents build on the same record instead of coordinating with each other.
- 04
The problem predates AI.
People leave, decisions get buried, documents go stale. Decades of partial answers — wikis, decision records, audit trails, commit lineage — are evidence that the problem is real. AI didn’t create it; AI multiplied it.
- 05
A field is forming, and we haven’t found an independent, vendor-neutral map of it being kept up to date yet.
Memory systems, context engineering, stateful agents, provenance, evaluation — the pieces are emerging faster than the vocabulary. One underexplored question is whether long-running AI systems need project-level continuity in addition to agent-level infrastructure. This site exists to track that question in public.
A continuity layer is the part of an AI system that carries forward governed state across sessions, tools, and models: what is currently true, what changed, why it changed, and which evidence supports it. It sits above memory and retrieval, and below agent execution, turning stored history into usable, auditable continuity.
Choose the path that matches what you need
If you are arriving fresh, start with the level of explanation you need before moving into the map and evidence.
Layer, Engine, Runtime
The Continuity Layer is the durable frame that decides what carries forward. The Continuity Engine implements that frame, and the Continuity Runtime is where users, models, tools, and agents execute the work.
A governed path from stored history to current working truth.
- 01LayerDecidesSets the durable frame for what should carry forward.
- 02EngineGovernsSupersedes stale facts, keeps lineage, and reconstructs current state.
- 03RuntimeSurvivesCarries that governed state through real model, tool, and agent work.
Durable frame
The field frame and architectural responsibility for deciding what should persist across models, tools, agents, and sessions.
Implementation
The implementation that governs writes, supersedes stale facts, keeps lineage, and reconstructs the current account of the work.
Execution space
The live execution space where the user, model, tools, and agents interact while the Engine governs the work.
- User
- Continuity Runtime
- Model / Tool / Agent
- Continuity Runtime
- User
Agent execution — the tools and agents doing the work
Governs state: what is true now, what changed, why, and the evidence behind it
Memory & retrieval — where facts are stored and fetched
What a continuity layer is made of
The eight axes are not a second framework. They are the field dimensions the Layer reads and the Engine governs into a single working account across time.
Memory
What is stored.
Memory systems, Personal AI memory
Context
What is available right now, in this call.
Context engineering
State
What persists — the current shape of a project or task.
Stateful agents, Long-running workflows
Provenance
Where each piece of truth came from.
Provenance & audit, Organizational memory
Agents
Who acts, and under what authority.
Agent infrastructure
Workflows
How continuity survives a handoff — session to session, tool to tool, agent to agent.
Long-running workflows, MCP & tool continuity
Evaluation
How any of this is measured, and against what standard.
Eval benchmarks, Papers
Infrastructure
Where the layer actually lives — protocol, runtime, product, or process.
MCP & tool continuity, Commercial
The field, mapped
The Continuity Layer maintains a public map of the emerging systems that let AI preserve context, state, memory, provenance, and working understanding across time.
A preview, not the full record — tracked signals, map infrastructure, and the Lab path where Engine behavior has its first recorded evidence.
The continuity field, mapped along eight co-equal axes. Select any to see what it covers.
Kenotic Labs
- Continuity function
- The org-level Field Watch profile; see Entry 001 for the full neutral write-up, and the Evidence Library for the artifact-level caveats.
Continuity
- Continuity function
- Memory infrastructure — the supplier layer continuity systems build on, rather than a project-continuity system in its own right.
Continuity AI
- Continuity function
- Language collision worth tracking — helps calibrate how crowded the word "continuity" is becoming.
Astrolobe Continuity
- Continuity function
- Another independent attempt at project-level (not just chat-level) continuity — a convergent-reinvention data point, not a competitor claim.
ai-continuity-system
- Continuity function
- Memory/tooling infrastructure — worth tracking for how the open-source side of this field is converging on similar shapes.
“The Continuity Paradox”
- Continuity function
- Independent commentary that the continuity gap is visible and named outside this project.
Memside
- Continuity function
- Personal-memory angle on continuity, distinct from this project's project-level focus — a useful contrast case.
These surfaces show the field forming: categories, signals, and evidence infrastructure. The Lab holds the first recorded evidence of Engine behavior — Demo 001, a PASS bounded to read-only orientation and conflict rejection.
Open questions the map is holding
Questions this project tracks rather than claims to have answered. Drafted, and still moving.
- 01
What should persist across a project’s life — and what is noise a system should be free to forget?
- 02
Who owns a project’s memory when a person, an app, a model, and an agent all touch it?
- 03
How should an agent inherit context it didn’t create without inheriting its mistakes?
- 04
How should provenance be represented, so any decision can show where it came from?
- 05
What should stay portable across models, and what is tied to the system that made it?
- 06
When two agents hold conflicting versions of the truth, on what basis does one win?
Three ways in
New to AI continuity? Start with the seven-step path from chatbots to continuity.
Found a signal?
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Maintained by an independent builder testing continuity patterns in live systems.
This map is maintained by an independent builder who tests continuity patterns in ongoing work in this field. The maintainer also does commercial work in this field. The two are kept separate: the map sells nothing, takes no sponsorships, and favors no vendor — including the maintainer’s own.
The Continuity Layer is maintained by an independent builder testing continuity patterns in live systems. Entries reflect what can be verified from papers, products, and production use — claims are labeled as verified or speculative, and the map is corrected as the field moves.