# MMA: 4-Tier Multi-Model Agent Orchestration

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---

## Overview

The MMA (Multi-Model Agent) system is a hierarchical task decomposition and execution engine. A high-level "epic" is broken into tracks, tracks are decomposed into tickets with dependency relationships, and tickets are executed by stateless workers with human-in-the-loop approval at every destructive boundary.

```
Tier 1: Orchestrator   — product alignment, epic → tracks
Tier 2: Tech Lead      — track → tickets (DAG), architectural oversight
Tier 3: Worker         — stateless TDD implementation per ticket
Tier 4: QA             — stateless error analysis, no fixes
```

---

## Data Structures (`models.py`)

### Ticket

The atomic unit of work. All MMA execution revolves around transitioning tickets through their state machine.

```python
@dataclass
class Ticket:
    id: str                                    # e.g., "T-001"
    description: str                           # Human-readable task description
    status: str                                # "todo" | "in_progress" | "completed" | "blocked"
    assigned_to: str                           # Tier assignment: "tier3-worker", "tier4-qa"
    target_file: Optional[str] = None          # File this ticket modifies
    context_requirements: List[str] = field()  # Files needed for context injection
    depends_on: List[str] = field()            # Ticket IDs that must complete first
    blocked_reason: Optional[str] = None       # Why this ticket is blocked
    step_mode: bool = False                    # If True, requires manual approval before execution

    def mark_blocked(self, reason: str) -> None   # Sets status="blocked", stores reason
    def mark_complete(self) -> None                # Sets status="completed"
    def to_dict(self) -> Dict[str, Any]
    @classmethod
    def from_dict(cls, data) -> "Ticket"
```

**Status state machine:**

```
todo ──> in_progress ──> completed
 |            |
 v            v
blocked     blocked
```

### Track

A collection of tickets with a shared goal.

```python
@dataclass
class Track:
    id: str                                    # Track identifier
    description: str                           # Track-level brief
    tickets: List[Ticket] = field()            # Ordered list of tickets

    def get_executable_tickets(self) -> List[Ticket]
        # Returns all 'todo' tickets whose depends_on are all 'completed'
```

### WorkerContext

```python
@dataclass
class WorkerContext:
    ticket_id: str          # Which ticket this worker is processing
    model_name: str         # LLM model to use (e.g., "gemini-2.5-flash-lite")
    messages: List[dict]    # Conversation history for this worker
```

---

## DAG Engine (`dag_engine.py`)

Two classes: `TrackDAG` (graph) and `ExecutionEngine` (state machine).

### TrackDAG

```python
class TrackDAG:
    def __init__(self, tickets: List[Ticket]):
        self.tickets = tickets
        self.ticket_map = {t.id: t for t in tickets}  # O(1) lookup by ID
```

**`get_ready_tasks()`**: Returns tickets where `status == 'todo'` AND all `depends_on` have `status == 'completed'`. Missing dependencies are treated as NOT completed (fail-safe).

**`has_cycle()`**: Classic DFS cycle detection using visited set + recursion stack:

```python
def has_cycle(self) -> bool:
    visited = set()
    rec_stack = set()
    def is_cyclic(ticket_id):
        if ticket_id in rec_stack: return True    # Back edge = cycle
        if ticket_id in visited: return False      # Already explored
        visited.add(ticket_id)
        rec_stack.add(ticket_id)
        for neighbor in ticket.depends_on:
            if is_cyclic(neighbor): return True
        rec_stack.remove(ticket_id)
        return False
    for ticket in self.tickets:
        if ticket.id not in visited:
            if is_cyclic(ticket.id): return True
    return False
```

**`topological_sort()`**: Calls `has_cycle()` first — raises `ValueError` if cycle found. Standard DFS post-order topological sort. Returns list of ticket ID strings in dependency order.

### ExecutionEngine

```python
class ExecutionEngine:
    def __init__(self, dag: TrackDAG, auto_queue: bool = False):
        self.dag = dag
        self.auto_queue = auto_queue
```

**`tick()`** — the heartbeat. On each call:
1. Queries `dag.get_ready_tasks()` for eligible tickets.
2. If `auto_queue` is enabled: non-`step_mode` tasks are automatically promoted to `in_progress`.
3. `step_mode` tasks remain in `todo` until `approve_task()` is called.
4. Returns the list of ready tasks.

**`approve_task(task_id)`**: Manually transitions `todo` → `in_progress` if all dependencies are met.

**`update_task_status(task_id, status)`**: Force-sets status (used by workers to mark `completed` or `blocked`).

---

## ConductorEngine (`multi_agent_conductor.py`)

The Tier 2 orchestrator. Owns the execution loop that drives tickets through the DAG.

```python
class ConductorEngine:
    def __init__(self, track: Track, event_queue=None, auto_queue=False):
        self.track = track
        self.event_queue = event_queue
        self.tier_usage = {
            "Tier 1": {"input": 0, "output": 0},
            "Tier 2": {"input": 0, "output": 0},
            "Tier 3": {"input": 0, "output": 0},
            "Tier 4": {"input": 0, "output": 0},
        }
        self.dag = TrackDAG(self.track.tickets)
        self.engine = ExecutionEngine(self.dag, auto_queue=auto_queue)
```

### State Broadcast (`_push_state`)

On every state change, the engine pushes the full orchestration state to the GUI via `AsyncEventQueue`:

```python
async def _push_state(self, status="running", active_tier=None):
    payload = {
        "status": status,           # "running" | "done" | "blocked"
        "active_tier": active_tier,  # e.g., "Tier 2 (Tech Lead)", "Tier 3 (Worker): T-001"
        "tier_usage": self.tier_usage,
        "track": {"id": self.track.id, "title": self.track.description},
        "tickets": [asdict(t) for t in self.track.tickets]
    }
    await self.event_queue.put("mma_state_update", payload)
```

This payload is consumed by the GUI's `_process_pending_gui_tasks` handler for `"mma_state_update"`, which updates `mma_status`, `active_tier`, `mma_tier_usage`, `active_tickets`, and `active_track`.

### Ticket Ingestion (`parse_json_tickets`)

Parses a JSON array of ticket dicts (from Tier 2 LLM output) into `Ticket` objects, appends to `self.track.tickets`, then rebuilds the `TrackDAG` and `ExecutionEngine`.

### Main Execution Loop (`run`)

```python
async def run(self):
    while True:
        ready_tasks = self.engine.tick()

        if not ready_tasks:
            if all tickets completed:
                await self._push_state("done")
                break
            if any in_progress:
                await asyncio.sleep(1)    # Waiting for async workers
                continue
            else:
                await self._push_state("blocked")
                break

        for ticket in ready_tasks:
            if in_progress or (auto_queue and not step_mode):
                ticket.status = "in_progress"
                await self._push_state("running", f"Tier 3 (Worker): {ticket.id}")

                # Create worker context
                context = WorkerContext(
                    ticket_id=ticket.id,
                    model_name="gemini-2.5-flash-lite",
                    messages=[]
                )

                # Execute in thread pool (blocking AI call)
                await loop.run_in_executor(
                    None, run_worker_lifecycle, ticket, context, ...
                )

                await self._push_state("running", "Tier 2 (Tech Lead)")

            elif todo and (step_mode or not auto_queue):
                await self._push_state("running", f"Awaiting Approval: {ticket.id}")
                await asyncio.sleep(1)    # Pause for HITL approval
```

---

## Tier 2: Tech Lead (`conductor_tech_lead.py`)

The Tier 2 AI call converts a high-level Track brief into discrete Tier 3 tickets.

### `generate_tickets(track_brief, module_skeletons) -> list[dict]`

```python
def generate_tickets(track_brief: str, module_skeletons: str) -> list[dict]:
    system_prompt = mma_prompts.PROMPTS.get("tier2_sprint_planning")
    user_message = (
        f"### TRACK BRIEF:\n{track_brief}\n\n"
        f"### MODULE SKELETONS:\n{module_skeletons}\n\n"
        "Please generate the implementation tickets for this track."
    )
    # Temporarily override system prompt
    old_system_prompt = ai_client._custom_system_prompt
    ai_client.set_custom_system_prompt(system_prompt)
    try:
        response = ai_client.send(md_content="", user_message=user_message)
        # Multi-layer JSON extraction:
        #   1. Try ```json ... ``` blocks
        #   2. Try ``` ... ``` blocks
        #   3. Regex search for [ { ... } ] pattern
        tickets = json.loads(json_match)
        return tickets
    finally:
        ai_client.set_custom_system_prompt(old_system_prompt)
```

The JSON extraction is defensive — handles markdown code fences, bare JSON, and regex fallback for embedded arrays.

### `topological_sort(tickets: list[dict]) -> list[dict]`

Convenience wrapper: converts raw dicts to `Ticket` objects, builds a `TrackDAG`, calls `dag.topological_sort()`, returns the original dicts reordered by sorted IDs.

---

## Tier 3: Worker Lifecycle (`run_worker_lifecycle`)

This free function executes a single ticket. Key behaviors:

### Context Amnesia

```python
ai_client.reset_session()  # Each ticket starts with a clean slate
```

No conversational bleed between tickets. Every worker is stateless.

### Context Injection

For `context_requirements` files:
- First file: `parser.get_curated_view(content)` — full skeleton with `@core_logic` and `[HOT]` bodies preserved.
- Subsequent files: `parser.get_skeleton(content)` — cheaper, signatures + docstrings only.

### Prompt Construction

```python
user_message = (
    f"You are assigned to Ticket {ticket.id}.\n"
    f"Task Description: {ticket.description}\n"
    f"\nContext Files:\n{context_injection}\n"
    "Please complete this task. If you are blocked and cannot proceed, "
    "start your response with 'BLOCKED' and explain why."
)
```

### HITL Clutch Integration

If `event_queue` is provided, `confirm_spawn()` is called before executing, allowing the user to:
- Read the prompt and context.
- Edit both the prompt and context markdown.
- Approve, reject, or abort the entire track.

The `confirm_spawn` function uses the `dialog_container` pattern:

1. Create `dialog_container = [None]` (mutable container for thread communication).
2. Push `"mma_spawn_approval"` task to event queue with the container.
3. Poll `dialog_container[0]` every 100ms for up to 60 seconds.
4. When the GUI fills in the dialog, call `.wait()` to get the result.
5. Returns `(approved, modified_prompt, modified_context)`.

---

## Tier 4: QA Error Analysis

Stateless error analysis. Invoked via the `qa_callback` parameter in `shell_runner.run_powershell()` when a command fails.

```python
def run_tier4_analysis(error_message: str) -> str:
    """Stateless Tier 4 QA analysis of an error message."""
    # Uses a dedicated system prompt for error triage
    # Returns analysis text (root cause, suggested fix)
    # Does NOT modify any code — analysis only
```

Integrated directly into the shell execution pipeline: if `qa_callback` is provided and the command has non-zero exit or stderr output, the callback result is appended to the tool output as `QA ANALYSIS:\n<result>`.

---

## Cross-System Data Flow

The full MMA lifecycle from epic to completion:

1. **Tier 1 (Orchestrator)**: User enters an epic description in the GUI. Creates a `Track` with a brief.
2. **Tier 2 (Tech Lead)**: `conductor_tech_lead.generate_tickets()` calls `ai_client.send()` with the `tier2_sprint_planning` prompt, producing a JSON ticket list.
3. **Ingestion**: `ConductorEngine.parse_json_tickets()` ingests the JSON, builds `Ticket` objects, constructs `TrackDAG` + `ExecutionEngine`.
4. **Execution loop**: `ConductorEngine.run()` enters the async loop, calling `engine.tick()` each iteration.
5. **Worker dispatch**: For each ready ticket, `run_worker_lifecycle()` is called in a thread executor. It uses `ai_client.send()` with MCP tools (dispatched through `mcp_client.dispatch()`).
6. **Security enforcement**: MCP tools enforce the allowlist via `_resolve_and_check()` on every filesystem operation.
7. **State broadcast**: `_push_state()` → `AsyncEventQueue` → GUI renders DAG + ticket status.
8. **External visibility**: `ApiHookClient.get_mma_status()` queries the Hook API for the full orchestration state.
9. **HITL gates**: `confirm_spawn()` pushes to event queue → GUI renders dialog → user approves/edits → `dialog_container[0].wait()` returns the decision.

---

## Token Firewalling

Each tier operates within its own token budget:

- **Tier 3 workers** use lightweight models (default: `gemini-2.5-flash-lite`) and receive only the files listed in `context_requirements`.
- **Context Amnesia** ensures no accumulated history bleeds between tickets.
- **Tier 2** tracks cumulative `tier_usage` per tier: `{"input": N, "output": N}` for token cost monitoring.
- **First file vs subsequent files**: The first `context_requirements` file gets a curated view (preserving hot paths); subsequent files get only skeletons.

---

## Track State Persistence

Track state can be persisted to disk via `project_manager.py`:

```
conductor/tracks/<track_id>/
    spec.md          # Track specification (human-authored)
    plan.md          # Implementation plan with checkbox tasks
    metadata.json    # Track metadata (id, type, status, timestamps)
    state.toml       # Structured TrackState with task list
```

`project_manager.get_all_tracks(base_dir)` scans the tracks directory with a three-tier metadata fallback:
1. `state.toml` (structured `TrackState`) — counts tasks with `status == "completed"`.
2. `metadata.json` (legacy) — gets id/title/status only.
3. `plan.md` (regex) — counts `- [x]` vs `- [ ]` checkboxes for progress.