211 lines
8.3 KiB
Markdown
211 lines
8.3 KiB
Markdown
# AtoCore Master Plan Status
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## Current Position
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AtoCore is currently between **Phase 7** and **Phase 8**.
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The platform is no longer just a proof of concept. The local engine exists, the
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core correctness pass is complete, Dalidou hosts the canonical runtime and
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machine database, and OpenClaw on the T420 can consume AtoCore safely in
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read-only additive mode.
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## Phase Status
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### Completed
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- Phase 0 - Foundation
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- Phase 0.5 - Proof of Concept
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- Phase 1 - Ingestion
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### Baseline Complete
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- Phase 2 - Memory Core
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- Phase 3 - Retrieval
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- Phase 5 - Project State
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- Phase 7 - Context Builder
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### Baseline Complete
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- Phase 4 - Identity / Preferences. As of 2026-04-12: 3 identity
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memories (role, projects, infrastructure) and 3 preference memories
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(no API keys, multi-model collab, action-over-discussion) seeded
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on live Dalidou. Identity/preference band surfaces in context packs
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at 5% budget ratio. Future identity/preference extraction happens
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organically via the nightly LLM extraction pipeline.
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- Phase 8 - OpenClaw Integration. As of 2026-04-12 the T420 OpenClaw
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helper (`t420-openclaw/atocore.py`) is verified end-to-end against
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live Dalidou: health check, auto-context with project detection,
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Trusted Project State surfacing, project-memory band, fail-open on
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unreachable host. Tested from both the development machine and the
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T420 via SSH. The helper covers 15 of the 33 API endpoints — the
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excluded endpoints (memory management, interactions, backup) are
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correctly scoped to the operator client (`scripts/atocore_client.py`)
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per the read-only additive integration model.
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### Baseline Complete
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- Phase 9 - Reflection (all three foundation commits landed:
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A capture, B reinforcement, C candidate extraction + review queue).
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As of 2026-04-11 the capture → reinforce half runs automatically on
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every Stop-hook capture (length-aware token-overlap matcher handles
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paragraph-length memories), and project-scoped memories now reach
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the context pack via a dedicated `--- Project Memories ---` band
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between identity/preference and retrieved chunks. The extract half
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is still a manual / batch flow by design (`scripts/atocore_client.py
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batch-extract` + `triage`). First live batch-extract run over 42
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captured interactions produced 1 candidate (rule extractor is
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conservative and keys on structural cues like `## Decision:`
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headings that rarely appear in conversational LLM responses) —
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extractor tuning is a known follow-up.
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### Not Yet Complete In The Intended Sense
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- Phase 6 - AtoDrive
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- Phase 10 - Write-back
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- Phase 11 - Multi-model
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- Phase 12 - Evaluation
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- Phase 13 - Hardening
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### Engineering Layer Planning Sprint
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**Status: complete.** All 8 architecture docs are drafted. The
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engineering layer is now ready for V1 implementation against the
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active project set.
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- [engineering-query-catalog.md](architecture/engineering-query-catalog.md) —
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the 20 v1-required queries the engineering layer must answer
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- [memory-vs-entities.md](architecture/memory-vs-entities.md) —
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canonical home split between memory and entity tables
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- [promotion-rules.md](architecture/promotion-rules.md) —
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Layer 0 → Layer 2 pipeline, triggers, review queue mechanics
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- [conflict-model.md](architecture/conflict-model.md) —
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detection, representation, and resolution of contradictory facts
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- [tool-handoff-boundaries.md](architecture/tool-handoff-boundaries.md) —
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KB-CAD / KB-FEM one-way mirror stance, ingest endpoints, drift handling
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- [representation-authority.md](architecture/representation-authority.md) —
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canonical home matrix across PKM / KB / repos / AtoCore for 22 fact kinds
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- [human-mirror-rules.md](architecture/human-mirror-rules.md) —
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templates, regeneration triggers, edit flow, "do not edit" enforcement
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- [engineering-v1-acceptance.md](architecture/engineering-v1-acceptance.md) —
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measurable done definition with 23 acceptance criteria
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- [engineering-knowledge-hybrid-architecture.md](architecture/engineering-knowledge-hybrid-architecture.md) —
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the 5-layer model (from the previous planning wave)
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- [engineering-ontology-v1.md](architecture/engineering-ontology-v1.md) —
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the initial V1 object and relationship inventory (previous wave)
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- [project-identity-canonicalization.md](architecture/project-identity-canonicalization.md) —
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the helper-at-every-service-boundary contract that keeps the
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trust hierarchy dependable across alias and canonical-id callers;
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required reading before adding new project-keyed entity surfaces
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in the V1 implementation sprint
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The next concrete next step is the V1 implementation sprint, which
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should follow engineering-v1-acceptance.md as its checklist, and
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must apply the project-identity-canonicalization contract at every
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new service-layer entry point.
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### LLM Client Integration
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A separate but related architectural concern: how AtoCore is reachable
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from many different LLM client contexts (OpenClaw, Claude Code, future
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Codex skills, future MCP server). The layering rule is documented in:
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- [llm-client-integration.md](architecture/llm-client-integration.md) —
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three-layer shape: HTTP API → shared operator client
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(`scripts/atocore_client.py`) → per-agent thin frontends; the
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shared client is the canonical backbone every new client should
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shell out to instead of reimplementing HTTP calls
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This sits implicitly between Phase 8 (OpenClaw) and Phase 11
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(multi-model). Memory-review and engineering-entity commands are
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deferred from the shared client until their workflows are exercised.
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## What Is Real Today
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- canonical AtoCore runtime on Dalidou
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- canonical machine DB and vector store on Dalidou
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- project registry with:
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- template
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- proposal preview
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- register
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- update
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- refresh
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- read-only additive OpenClaw helper on the T420
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- seeded project corpus for:
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- `p04-gigabit`
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- `p05-interferometer`
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- `p06-polisher`
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- conservative Trusted Project State for those active projects
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- first operational backup foundation for SQLite + project registry
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- implementation-facing architecture notes for future engineering knowledge work
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- first organic routing layer in OpenClaw via:
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- `detect-project`
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- `auto-context`
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## Now
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These are the current practical priorities.
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1. Finish practical OpenClaw integration
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- make the helper lifecycle feel natural in daily use
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- use the new organic routing layer for project-knowledge questions
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- confirm fail-open behavior remains acceptable
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- keep AtoCore clearly additive
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2. Tighten retrieval quality
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- reduce cross-project competition
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- improve ranking on short or ambiguous prompts
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- add only a few anchor docs where retrieval is still weak
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3. Continue controlled ingestion
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- deepen active projects selectively
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- avoid noisy bulk corpus growth
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4. Strengthen operational boringness
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- backup and restore procedure
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- Chroma rebuild / backup policy
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- retention and restore validation
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## Next
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These are the next major layers after the current practical pass.
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1. Clarify AtoDrive as a real operational truth layer
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2. Mature identity / preferences handling
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3. Improve observability for:
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- retrieval quality
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- context-pack inspection
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- comparison of behavior with and without AtoCore
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## Later
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These are the deliberate future expansions already supported by the architecture
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direction, but not yet ready for immediate implementation.
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1. Minimal engineering knowledge layer
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- driven by `docs/architecture/engineering-knowledge-hybrid-architecture.md`
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- guided by `docs/architecture/engineering-ontology-v1.md`
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2. Minimal typed objects and relationships
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3. Evidence-linking and provenance-rich structured records
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4. Human mirror generation from structured state
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## Not Yet
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These remain intentionally deferred.
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- automatic write-back from OpenClaw into AtoCore
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- automatic memory promotion
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- ~~reflection loop integration~~ — baseline now in (capture→reinforce
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auto, extract batch/manual). Extractor tuning and scheduled batch
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extraction still open.
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- replacing OpenClaw's own memory system
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- live machine-DB sync between machines
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- full ontology / graph expansion before the current baseline is stable
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## Working Rule
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The next sensible implementation threshold for the engineering ontology work is:
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- after the current ingestion, retrieval, registry, OpenClaw helper, organic
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routing, and backup baseline feels boring and dependable
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Until then, the architecture docs should shape decisions, not force premature
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schema work.
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