Tao

tao — Autonomous Execution Engine

The Tao is the synthesis of four subsystems:

• /nexus — Master dispatch router: decides what skill should handle a task
• /tao-loop — Iterate, generate, deploy with persistent checkpointing
• /tao-judge — Iterative evaluator / calibrator / grader-gate
• /session-handoff — Continuity / escrow for state persistence across sessions

When to invoke

• The user says "run this autonomously" or "handle this overnight"
• A task spans >1 skill or >1 domain and requires closed-loop execution
• Prior work exists that needs resumption via /resume-from-handoff
• The user grants 100% authority for exhaustive autonomous action

Invocation Contract

Input: {prayer.field, prayer.layer, prayer.operator, prayer.objective, prayer.subject, prayer.context} Derivation from user request:

• field: work_area_group (e.g., "RestoreAssist", "Unite-Hub", "Marketing", "Portfolio")
• layer: execution_depth (routine | complex | board-critical)
• operator: action_type (build | audit | research | deploy | maintain)
• objective: primary_deliverable (e.g., "ship the auth refactor", "audit all env vars")
• subject: target_entity (project, repo, feature, or "across all projects")
• context: freeform constraints, prior handoff ID, budget, timeline

Execution Algorithm

Define: tao(prayer)

Phase 1 — Initiation (TAO-Start)

1. Hydrate state — check for prior handoff via /resume-from-handoff
2. Classify — /nexus routes the prayer to the primary skill(s)
3. Load — skill_view(primary_skill) to calibrate output contracts
4. Budget — estimate tokens, set TERMINAL TRIGGER, COMPACTION THRESHOLD
5. Emit prayer hash — prayer_hash = sha256(prayer) for audit trace

Phase 2 — Execution Loop (TAO-Loop)

While prayer.layer not satisfied:

1. ACT — Execute the next step using the routed skill(s)
2. CHECKPOINT — Persist state: files written, git state, tokens consumed, evidence URLs
3. JUDGE — /tao-judge evaluates the ACT output
4. TERMINAL TRIGGER CHECK
   • If judge_score >= threshold AND all must-haves pass → Phase 3
   • If max_loops reached → Phase 3 (with partial flag)
   • If anomaly detected AND anomaly_score >= 0.4 → Phase 4 (ESCROW)
   • If token budget critical (<10%) → COMPACT + continue

Phase 3 — Conclusion (TAO-End)

1. EMIT — Final deliverable in the format requested (or the skill's native format)
2. AUDIT — /audit-emit produces the structured JSON audit trace
3. HANDOFF — /session-handoff captures:
   • What was done
   • What shipped
   • What remains
   • Exact pickup point for next session
   • Verification commands
4. IMPROVE — Every execution produces improvement instructions:
   • What worked well
   • What was slow / expensive
   • What the skills got wrong
   • Recommended patches to relevant skill SKILL.md files
5. QUEUE — Improvement instructions stored in tao_improvement_queue for periodic review via /meta-curator

Phase 4 — Escrow (TAO-Stop)

Triggered by: anomaly_score >= 0.4, external stop, manual intervention request, or unresolvable contradiction.

1. HALT all execution immediately
2. PAUSE state: preserve checkpoints, context canvas, evidence URLs
3. ESCALATE to /boardroom with full trace
4. HANDOFF the pause state for human review
5. LOCK the prayer hash; resume requires explicit re-invocation

Self-Improvement Loop

The Tao is not just an executor — it is a learner.

After every TAO-End:

1. Analyse the execution trace for patterns
2. Extract skill-specific improvement notes (e.g., "/judge missed this edge case")
3. Batch improvement notes per skill
4. When a skill accumulates >3 improvement notes → auto-emit a patch proposal to /meta-curator
5. When a patch is accepted → update the skill SKILL.md and increment the skill version
6. When >5 patches accepted in a session → emit a Tao system upgrade note

Observability Contract

Every Tao session emits:

{
  "tao_session_id": "",
  "prayer_hash": "sha256",
  "phases": [
    {"phase": "TAO-Start", "timestamp": "", "duration_seconds": 0.0},
    {"phase": "TAO-Loop", "timestamp": "", "loops": 0, "avg_judge_score": 0.0, "duration_seconds": 0.0},
    {"phase": "TAO-End", "timestamp": "", "duration_seconds": 0.0},
    {"phase": "TAO-Improvement", "timestamp": "", "improvements_queued": 0}
  ],
  "primary_skill": "",
  "skills_invoked": [],
  "files_changed": [],
  "git_commits": [],
  "audit_trace_hash": "",
  "handoff_id": "",
  "improvement_queue": [],
  "terminal_reason": "success | max_loops | anomaly | manual_stop",
  "tokens_total": 0,
  "cost_estimate_aud": 0.0
}

Cross-Model Fallbacks

If the primary model fails during Tao execution:

• Routine steps → fallback to Sonnet/Haiku
• Judge reviews → fallback to deeper model (Opus/Claude-4)
• MOA disagreements → fallback to Boardroom
• Escrow → always uses the strongest available model

Autonomy Rules

1. The Tao NEVER asks "what should I do next?" — it decides and reports
2. The Tao ALWAYS handoffs before stopping — never lose state
3. The Tao ALWAYS emits an audit trace — every execution is accountable
4. The Tao ALWAYS queues improvements — every execution makes the system better
5. The Tao ALWAYS respects the CEO Board and Pi governance gate on external outputs
6. The Tao ALWAYS runs destructive operations through /judge first
7. The Tao NEVER auto-executes financial transactions, contract signings, or production deployments without explicit pass-through approval
8. The Tao treats dirty-tree / mega-diff as a trigger for lane-splitting via Kanban/cron, not a reason to stop

References

• /nexus — Master dispatch router
• /tao-loop — Iterate-Generate-Deploy loop
• /tao-judge — Iterative evaluator
• /session-handoff — State escrow
• /resume-from-handoff — State retrieval
• /boardroom — Escalation panel
• /audit-emit — Structured audit formatter
• /meta-curator — Skill lifecycle management