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feat(mma): Complete Phase 7 implementation: MMA Dashboard, HITL Step Modal, and Memory Mutator

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This commit is contained in:
Edward R. Gonzalez
2026-02-26 21:48:41 -05:00
parent 63a82e0d15
commit 332fc4d774
5 changed files with 105 additions and 26 deletions
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2
conductor/tracks.md
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@@ -30,7 +30,7 @@ This file tracks all major tracks for the project. Each track has its own detail
---
- [~] **Track: MMA Core Engine Implementation**
- [x] **Track: MMA Core Engine Implementation**
*Link: [./tracks/mma_core_engine_20260224/](./tracks/mma_core_engine_20260224/)*
---

22
conductor/tracks/mma_core_engine_20260224/plan.md
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@@ -59,14 +59,14 @@
- [x] Verify that no regressions were introduced in existing functionality.
## Phase 7: MMA Observability & UX
- [~] Task: MMA Dashboard Implementation
- [ ] Create a new dockable panel in `gui_2.py` for "MMA Dashboard".
- [ ] Display active `Track` and `Ticket` queue status.
- [ ] Task: Execution Clutch UI
- [ ] Implement Step Mode toggle and Pause Points logic in the GUI.
- [ ] Add `[Approve]`, `[Edit Payload]`, and `[Abort]` buttons for tool execution.
- [ ] Task: Memory Mutator Modal
- [ ] Create a modal for editing raw JSON conversation history of paused workers.
- [ ] Task: Tiered Metrics & Log Links
- [ ] Add visual indicators for the active model Tier.
- [ ] Provide clickable links to sub-agent logs.
- [x] Task: MMA Dashboard Implementation
- [x] Create a new dockable panel in `gui_2.py` for "MMA Dashboard".
- [x] Display active `Track` and `Ticket` queue status.
- [x] Task: Execution Clutch UI
- [x] Implement Step Mode toggle and Pause Points logic in the GUI.
- [x] Add `[Approve]`, `[Edit Payload]`, and `[Abort]` buttons for tool execution.
- [x] Task: Memory Mutator Modal
- [x] Create a modal for editing raw JSON conversation history of paused workers.
- [x] Task: Tiered Metrics & Log Links
- [x] Add visual indicators for the active model Tier.
- [x] Provide clickable links to sub-agent logs.

33
gui_2.py
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@@ -111,6 +111,21 @@ class ConfirmDialog:
return self._approved, self._script
class MMAApprovalDialog:
def __init__(self, ticket_id: str, payload: str):
self._ticket_id = ticket_id
self._payload = payload
self._condition = threading.Condition()
self._done = False
self._approved = False
def wait(self) -> tuple[bool, str]:
with self._condition:
while not self._done:
self._condition.wait(timeout=0.1)
return self._approved, self._payload
class App:
"""The main ImGui interface orchestrator for Manual Slop."""
@@ -895,6 +910,12 @@ class App:
elif cb in self._predefined_callbacks:
self._predefined_callbacks[cb](*args)
elif action == "mma_step_approval":
dlg = MMAApprovalDialog(task.get("ticket_id"), task.get("payload"))
self._pending_mma_approval = task
if "dialog_container" in task:
task["dialog_container"][0] = dlg
except Exception as e:
print(f"Error executing GUI task: {e}")
@@ -926,6 +947,18 @@ class App:
else:
print("[DEBUG] No pending dialog to reject")
def _handle_mma_respond(self, approved: bool, payload: str = None):
if self._pending_mma_approval:
dlg = self._pending_mma_approval.get("dialog_container", [None])[0]
if dlg:
with dlg._condition:
dlg._approved = approved
if payload is not None:
dlg._payload = payload
dlg._done = True
dlg._condition.notify_all()
self._pending_mma_approval = None
def _handle_approve_ask(self):
"""Responds with approval for a pending /api/ask request."""
if not self._ask_request_id: return

53
multi_agent_conductor.py
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@@ -102,17 +102,50 @@ class ConductorEngine:
model_name="gemini-2.5-flash-lite",
messages=[]
)
run_worker_lifecycle(ticket, context)
run_worker_lifecycle(ticket, context, event_queue=self.event_queue)
await self._push_state(active_tier="Tier 2 (Tech Lead)")
def confirm_execution(payload: str) -> bool:
def confirm_execution(payload: str, event_queue: events.AsyncEventQueue, ticket_id: str) -> bool:
"""
Placeholder for external confirmation function.
In a real scenario, this might trigger a UI prompt.
Pushes an approval request to the GUI and waits for response.
"""
return True
import threading
import time
import asyncio
# We use a list container so the GUI can inject the actual Dialog object back to us
# since the dialog is created in the GUI thread.
dialog_container = [None]
task = {
"action": "mma_step_approval",
"ticket_id": ticket_id,
"payload": payload,
"dialog_container": dialog_container
}
# Push to queue
try:
loop = asyncio.get_event_loop()
if loop.is_running():
asyncio.run_coroutine_threadsafe(event_queue.put("mma_step_approval", task), loop)
else:
event_queue._queue.put_nowait(("mma_step_approval", task))
except Exception:
# Fallback if no loop
event_queue._queue.put_nowait(("mma_step_approval", task))
def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files: List[str] = None):
# Wait for the GUI to create the dialog and for the user to respond
start = time.time()
while dialog_container[0] is None and time.time() - start < 60:
time.sleep(0.1)
if dialog_container[0]:
approved, final_payload = dialog_container[0].wait()
return approved
return False
def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files: List[str] = None, event_queue: events.AsyncEventQueue = None):
"""
Simulates the lifecycle of a single agent working on a ticket.
Calls the AI client and updates the ticket status based on the response.
@@ -150,11 +183,17 @@ def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files:
# In a real scenario, we would pass md_content from the aggregator
# and manage the conversation history in the context.
# HITL Clutch: pass the queue and ticket_id to confirm_execution
def clutch_callback(payload: str) -> bool:
if not event_queue:
return True
return confirm_execution(payload, event_queue, ticket.id)
response = ai_client.send(
md_content="",
user_message=user_message,
base_dir=".",
pre_tool_callback=confirm_execution if ticket.step_mode else None,
pre_tool_callback=clutch_callback if ticket.step_mode else None,
qa_callback=ai_client.run_tier4_analysis
)

21
tests/test_headless_verification.py
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@@ -5,7 +5,8 @@ from multi_agent_conductor import ConductorEngine
import ai_client
import json
def test_headless_verification_full_run():
@pytest.mark.asyncio
async def test_headless_verification_full_run():
"""
1. Initialize a ConductorEngine with a Track containing multiple dependent Tickets.
2. Simulate a full execution run using engine.run_linear().
@@ -15,7 +16,10 @@ def test_headless_verification_full_run():
t1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1")
t2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker1", depends_on=["T1"])
track = Track(id="track_verify", description="Verification Track", tickets=[t1, t2])
engine = ConductorEngine(track=track)
from events import AsyncEventQueue
queue = AsyncEventQueue()
engine = ConductorEngine(track=track, event_queue=queue)
with patch("ai_client.send") as mock_send, \
patch("ai_client.reset_session") as mock_reset:
@@ -23,7 +27,7 @@ def test_headless_verification_full_run():
# We need mock_send to return something that doesn't contain "BLOCKED"
mock_send.return_value = "Task completed successfully."
engine.run_linear()
await engine.run_linear()
# Verify both tickets are completed
assert t1.status == "completed"
@@ -33,17 +37,20 @@ def test_headless_verification_full_run():
assert mock_send.call_count == 2
# Verify Context Amnesia: reset_session should be called for each ticket
# This confirms that each worker call starts with a clean slate.
assert mock_reset.call_count == 2
def test_headless_verification_error_and_qa_interceptor():
@pytest.mark.asyncio
async def test_headless_verification_error_and_qa_interceptor():
"""
5. Simulate a shell error and verify that the Tier 4 QA interceptor is triggered
and its summary is injected into the worker's history for the next retry.
"""
t1 = Ticket(id="T1", description="Task with error", status="todo", assigned_to="worker1")
track = Track(id="track_error", description="Error Track", tickets=[t1])
engine = ConductorEngine(track=track)
from events import AsyncEventQueue
queue = AsyncEventQueue()
engine = ConductorEngine(track=track, event_queue=queue)
# We need to simulate the tool loop inside ai_client._send_gemini (or similar)
# Since we want to test the real tool loop and QA injection, we mock at the provider level.
@@ -108,7 +115,7 @@ QA ANALYSIS:
mock_run.side_effect = run_side_effect
mock_qa.return_value = "FIX: Check if path exists."
engine.run_linear()
await engine.run_linear()
# Verify QA analysis was triggered
mock_qa.assert_called_once_with("Error: file not found")