Context Serving

This lattice is the startup protocol. When an AI agent opens a session in an aDNA vault, it traverses exactly this graph — loading CLAUDE.md, reading STATE.md, checking AGENTS.md routing decisions, assembling context recipes, and narrowing from the full vault down to the exact files needed for the current objective.

The convergence model makes this narrowing explicit: the vault holds ~75K tokens of context, but any single session needs only a fraction. Each node in this graph reduces scope — from project rules, through campaign context, to mission objectives — until the agent holds precisely the context it needs and nothing more.

How it connects

This lattice implements The Convergence Model as an executable graph. The AGENTS.md router node demonstrates the AGENTS.md Routing pattern — each directory’s AGENTS.md helps the agent decide whether to load its contents. The context recipe node uses the Context Recipe pattern to assemble cross-topic bundles. See Context Optimization for the theory behind token-efficient serving.

Full lattice definition

lattice:
  name: context_serving
  version: "1.0.0"
  lattice_type: context_graph
  description: >
    Context serving graph implementing the aDNA convergence model. Each agent
    session traverses this graph to load only the subgraph reachable from the
    current objective — narrowing from full vault to exact files needed.
    Self-referential: this lattice IS the startup protocol described in
    CLAUDE.md Agent Protocol.
  execution:
    mode: reasoning
    runtime: local
    tier: L1
    model: "claude-opus-4-6"
  nodes:
    - id: claude_md
      type: dataset
      description: "Project CLAUDE.md — auto-loaded master context with safety rules, standing orders, domain knowledge"
      ref: "CLAUDE.md"
    - id: state_md
      type: dataset
      description: "STATE.md — operational snapshot with current phase, blockers, next steps"
      ref: "STATE.md"
    - id: agents_md_router
      type: reasoning
      description: "Read AGENTS.md in target directory to make load/skip decision"
      prompt: >
        Read the AGENTS.md file in the target directory. Based on the
        Load/Skip Decision section and the current session's objective,
        decide whether to load this directory's content into context.
        Consider token cost vs. relevance.
    - id: context_library
      type: dataset
      description: "Context library at what/context/ — 5 topics, 27 subtopics, ~75K tokens total"
      ref: "what/context/"
    - id: context_recipe
      type: reasoning
      description: "Check context_recipes.md for pre-assembled cross-topic bundles matching this session's domain"
      prompt: >
        Given the session's task domain, check context_recipes.md for a
        matching recipe. Load the recipe's specified subtopics at the
        appropriate budget tier (Minimal/Standard/Full). Prefer recipes
        over manual subtopic selection.
      ref: "what/context/context_recipes.md"
    - id: campaign_context
      type: dataset
      description: "Active campaign docs — campaign master doc + campaign CLAUDE.md"
      ref: "how/campaigns/campaign_rosetta/"
    - id: mission_context
      type: dataset
      description: "Active mission file — objectives, dependencies, progress"
    - id: session_context
      type: dataset
      description: "Assembled context for this session — the convergence output"
  edges:
    - from: claude_md
      to: state_md
      label: "project rules loaded"
    - from: state_md
      to: agents_md_router
      label: "operational state known"
    - from: agents_md_router
      to: context_library
      label: "directory loaded"
      condition: "load_decision == true"
    - from: agents_md_router
      to: campaign_context
      label: "skip to campaign"
      condition: "load_decision == false"
    - from: context_library
      to: context_recipe
      label: "raw subtopics"
    - from: context_recipe
      to: campaign_context
      label: "assembled domain context"
    - from: campaign_context
      to: mission_context
      label: "campaign scope"
    - from: mission_context
      to: session_context
      label: "mission objectives"
  fair:
    license: "MIT"
    creators:
      - "Lattice Labs"
    keywords:
      - context serving
      - convergence model
      - token selection
      - agents.md
      - context recipe
      - self-referential
    provenance: "Models the aDNA convergence model — the context loading strategy every agent session uses at startup. Derived from the Agent Protocol in CLAUDE.md."