CIAgent
88a1dab810
---ci--- phase: 7 milestone: v0.2 status: complete requirements: covered: [REQ-2-001, REQ-2-002, REQ-2-003, REQ-2-004, REQ-2-005, REQ-2-006, REQ-2-007, REQ-2-008, REQ-2-009, REQ-2-010, REQ-2-011, REQ-2-012] partial: [] ---/ci--- Milestone v0.2 (ai-tutor-architecture) merged to main. Escalation record (audit remediation, durable): P1 executor delegation failed twice (empty subagent results, zero files created); auto-resolved at full autonomy to inline execution with identical plan fidelity (commit 3271373, reflog-only after phase branch squash-delete).
38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
"""LabAgent — in-flow feedback over simulated sandbox telemetry (REQ-2-007).
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Scenario-driven: consumes a LabTelemetryScenario from the corpus, renders
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the event timeline into the conversation, streams concrete feedback.
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No session chat — each request is one scenario read.
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"""
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from collections.abc import AsyncIterator
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from ..config import Settings
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from ..corpus.learner_context import LearnerContext, get_learner_context
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from ..corpus.telemetry import LabTelemetryScenario, summarize_scenario
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from ..llm.base import LLMProvider
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from ..prompts.lab import SYSTEM_PROMPT, render_context
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from .base import BaseAgent
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class LabAgent(BaseAgent):
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name = "lab"
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def __init__(self, provider: LLMProvider, settings: Settings) -> None:
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super().__init__(provider, settings)
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def system_prompt(self, learner_context: LearnerContext | None = None) -> str:
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ctx = learner_context or get_learner_context()
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return SYSTEM_PROMPT.format_map(render_context(ctx))
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async def stream_feedback(
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self,
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scenario: LabTelemetryScenario,
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learner_context: LearnerContext | None = None,
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) -> AsyncIterator[str]:
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timeline = summarize_scenario(scenario)
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async for token in self.stream_reply(
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history=None, user_input=timeline, learner_context=learner_context
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):
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yield token
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