{
  "schema": "longtermcapabilities-ai-architecture-decision/v1",
  "version": "1.64.0",
  "releaseId": "lts-1.64.0-upgrade-safe-rewrites-and-privacy-erasure-integrity",
  "generated": "2026-08-03T20:40:00Z",
  "path": "/machine-intelligence/architecture-decision-guide/",
  "architectures": [
    {
      "id": "deterministic",
      "name": "Deterministic workflow",
      "use_when": "The sequence, validations, and exception rules can be expressed clearly and the workflow benefits from predictable cost, latency, and recovery.",
      "avoid_when": "The task genuinely requires adaptive evidence gathering or planning across changing conditions.",
      "minimum_evidence": [
        "State machine or rules",
        "Representative tests",
        "Exception handling",
        "Rollback and reconciliation"
      ]
    },
    {
      "id": "rag",
      "name": "Retrieval-augmented generation",
      "use_when": "The main uncertainty is finding and synthesizing permitted evidence while the retrieval path can remain mostly fixed.",
      "avoid_when": "The system must choose among materially different plans or external actions.",
      "minimum_evidence": [
        "Source and tenant boundaries",
        "Retrieval quality",
        "Groundedness and citation checks",
        "Freshness and retention"
      ]
    },
    {
      "id": "single-agent",
      "name": "One bounded agent",
      "use_when": "The task needs adaptive sequencing or tool use but still has one coherent goal, state, authority envelope, and accountable owner.",
      "avoid_when": "Roles are genuinely independent, require parallel work, or must preserve information boundaries.",
      "minimum_evidence": [
        "Control loop",
        "Tool contracts",
        "Trajectory evaluation",
        "Budgets, stop conditions, and human authority"
      ]
    },
    {
      "id": "multi-agent",
      "name": "Multi-agent system",
      "use_when": "Specialization, parallelism, independent challenge, information separation, or dynamic allocation improves a measured outcome over the single-agent baseline.",
      "avoid_when": "The extra roles are decorative, correlated, or difficult to evaluate and recover.",
      "minimum_evidence": [
        "Topology and role distinctions",
        "Message contracts",
        "Shared-state rules",
        "Team-level benchmark and failure containment"
      ]
    },
    {
      "id": "swarm",
      "name": "Swarm-style coordination",
      "use_when": "Decentralized local rules, large-scale search or allocation, resilience to individual failure, and emergent adaptation are intrinsic to the problem.",
      "avoid_when": "A named accountable decision path or centralized policy approval is required for each consequential action.",
      "minimum_evidence": [
        "Local rules and feedback",
        "Emergence bounds",
        "Global invariants",
        "Containment, simulation, and independent stop control"
      ]
    }
  ],
  "boundary": "Starting hypotheses only; no automated design approval or return-on-investment claim."
}