feat(teacher): implement Tier 2 LLM self-evaluation

tests/test_teacher_eval_tier2.py

@@ -0,0 +1,172 @@
+import asyncio
+from types import SimpleNamespace
+import pytest
+
+import src.teacher_escalation as teacher_escalation
+
+
+@pytest.mark.asyncio
+async def test_evaluate_turn_llm_ok(monkeypatch):
+    seen = {}
+
+    def fake_resolve_endpoint(prefix, fallback_url=None, owner=None):
+        seen["prefix"] = prefix
+        seen["owner"] = owner
+        return "http://endpoint.local/v1", "utility-model", {}
+
+    async def fake_llm_call_async(url, model, messages, **kwargs):
+        seen["called"] = True
+        return "ok"
+
+    monkeypatch.setattr("src.endpoint_resolver.resolve_endpoint", fake_resolve_endpoint)
+    monkeypatch.setattr("src.llm_core.llm_call_async", fake_llm_call_async)
+
+    status, reason = await teacher_escalation.evaluate_turn_llm(
+        user_request="test request",
+        tool_results=[],
+        agent_reply="test reply",
+        student_endpoint_url="http://student.local/v1",
+        owner="alice",
+    )
+
+    assert status == "ok"
+    assert reason is None
+    assert seen["prefix"] == "utility"
+    assert seen["owner"] == "alice"
+    assert seen["called"] is True
+
+
+@pytest.mark.asyncio
+async def test_evaluate_turn_llm_failure(monkeypatch):
+    def fake_resolve_endpoint(prefix, fallback_url=None, owner=None):
+        return "http://endpoint.local/v1", "utility-model", {}
+
+    async def fake_llm_call_async(url, model, messages, **kwargs):
+        return "this agent execution is a failure"
+
+    monkeypatch.setattr("src.endpoint_resolver.resolve_endpoint", fake_resolve_endpoint)
+    monkeypatch.setattr("src.llm_core.llm_call_async", fake_llm_call_async)
+
+    status, reason = await teacher_escalation.evaluate_turn_llm(
+        user_request="test request",
+        tool_results=[],
+        agent_reply="test reply",
+        student_endpoint_url="http://student.local/v1",
+        owner="alice",
+    )
+
+    assert status == "failure"
+    assert "LLM evaluation flagged failure" in reason
+
+
+@pytest.mark.asyncio
+async def test_evaluate_turn_llm_exception_handling(monkeypatch):
+    def fake_resolve_endpoint(prefix, fallback_url=None, owner=None):
+        return "http://endpoint.local/v1", "utility-model", {}
+
+    async def fake_llm_call_async(url, model, messages, **kwargs):
+        raise RuntimeError("model timeout")
+
+    monkeypatch.setattr("src.endpoint_resolver.resolve_endpoint", fake_resolve_endpoint)
+    monkeypatch.setattr("src.llm_core.llm_call_async", fake_llm_call_async)
+
+    # Should degrade gracefully to "ok"
+    status, reason = await teacher_escalation.evaluate_turn_llm(
+        user_request="test request",
+        tool_results=[],
+        agent_reply="test reply",
+        student_endpoint_url="http://student.local/v1",
+        owner="alice",
+    )
+
+    assert status == "ok"
+    assert reason is None
+
+
+@pytest.mark.asyncio
+async def test_maybe_escalate_triggers_tier2_background_task(monkeypatch):
+    # Enable teacher settings
+    monkeypatch.setattr("src.settings.get_setting", lambda key, default=None: {"teacher_enabled": True, "teacher_model": "teacher-model"}.get(key, default))
+
+    # Regex check says OK
+    monkeypatch.setattr("src.teacher_escalation.evaluate_turn_regex", lambda *args: ("ok", None))
+
+    llm_eval_called = []
+    async def fake_evaluate_turn_llm(*args, **kwargs):
+        llm_eval_called.append(True)
+        return "failure", "LLM flagged failure"
+
+    monkeypatch.setattr("src.teacher_escalation.evaluate_turn_llm", fake_evaluate_turn_llm)
+
+    escalate_called = []
+    async def fake_escalate_and_learn(user_request, tool_results, agent_reply, failure_reason, owner):
+        escalate_called.append(failure_reason)
+        return "skill-slug"
+
+    monkeypatch.setattr("src.teacher_escalation.escalate_and_learn", fake_escalate_and_learn)
+
+    # Call maybe_escalate
+    task = teacher_escalation.maybe_escalate(
+        student_endpoint_url="http://student.local/v1",
+        mode="agent",
+        user_request="test request",
+        tool_results=[],
+        agent_reply="test reply",
+        owner="alice",
+    )
+
+    assert task is not None
+    assert task.get_name() == "teacher_escalation_tier2"
+
+    # Await the background task execution
+    await task
+
+    assert llm_eval_called == [True]
+    assert escalate_called == ["LLM flagged failure"]
+
+
+@pytest.mark.asyncio
+async def test_run_teacher_inline_triggers_tier2_escalation(monkeypatch):
+    # Settings and gates
+    monkeypatch.setattr("src.settings.get_setting", lambda key, default=None: {"teacher_enabled": True, "teacher_model": "teacher-model"}.get(key, default))
+    monkeypatch.setattr("src.ai_interaction._resolve_model", lambda spec, owner=None: ("http://teacher.local/v1", "teacher-model", {}))
+
+    # Regex evaluation says "ok"
+    monkeypatch.setattr("src.teacher_escalation.evaluate_turn_regex", lambda *args: ("ok", None))
+
+    # LLM evaluation flags "failure"
+    async def fake_evaluate_turn_llm(*args, **kwargs):
+        return "failure", "LLM flagged failure"
+    monkeypatch.setattr("src.teacher_escalation.evaluate_turn_llm", fake_evaluate_turn_llm)
+
+    # Mock stream_agent_loop recursively called by run_teacher_inline
+    async def fake_stream_agent_loop(*args, **kwargs):
+        yield "data: {\"type\": \"tool_output\", \"tool\": \"bash\"}\n\n"
+        yield "data: {\"type\": \"text\", \"delta\": \"Teacher reply\"}\n\n"
+        yield "data: [DONE]\n\n"
+    monkeypatch.setattr("src.agent_loop.stream_agent_loop", fake_stream_agent_loop)
+
+    # Mock _call_teacher returning a skill definition
+    async def fake_call_teacher(spec, prompt, owner=None):
+        return '```json\n{"action": "add", "name": "test-skill"}\n```'
+    monkeypatch.setattr("src.teacher_escalation._call_teacher", fake_call_teacher)
+
+    # Mock do_manage_skills
+    async def fake_do_manage_skills(skill_json, owner=None):
+        return {"success": True}
+    monkeypatch.setattr("src.tool_implementations.do_manage_skills", fake_do_manage_skills)
+
+    events = []
+    async for evt in teacher_escalation.run_teacher_inline(
+        student_endpoint_url="http://student.local/v1",
+        student_messages=[{"role": "user", "content": "test request"}],
+        student_tool_events=[],
+        student_reply="student reply",
+        owner="alice",
+    ):
+        events.append(evt)
+
+    # Make sure teacher takeover was announced and executed
+    assert any("teacher_takeover" in evt for evt in events)
+    assert any("tool_output" in evt for evt in events)
+    assert any("skill_saved" in evt for evt in events)