fez5587andClaude Fable 5
9b6e7a23f6
- engine/dispatcher.py: 10s tick, bounded in-flight (3), ready->kanban dispatch
with budget mapping (goal_max_turns/max_runtime_seconds), poll->AgentMessage
extraction->events, wall-clock cap with run termination, blocked triage state.
- engine/protocol.py: schema-enforced pool calls — raw report -> AgentMessage,
objective brief -> task decomposition (3.6s live).
- engine/main.py: POST /companies/{slug}/objectives (manager decompose ->
child work items, role-matched round-robin), GET work-items.
- schema: work_item.hermes_task_id/dispatched_at, employee.role.
- Hermes integration fixes discovered live:
* profiles must use the pool custom endpoint (provider config written into
per-profile config; 'ollama' provider = Ollama Cloud, wants API key)
* kanban toolset was globally disabled in deployed Hermes config — removed
from agent.disabled_toolsets (backup in scratchpad)
* no_skills profiles (73 bundled skills drowned 9B tool discipline)
* souls spell out kanban_show first, kanban_complete/kanban_block with
required args last — after this, tasks complete reliably.
- Live result: GasWatch objective decomposed into 3 tasks, all completed by
qwen3.5:9b employees (mvp_features.md, onepager.md, report.md), 4 cost
entries totaling ~24 min wall, full event trail.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
94 lines
3.5 KiB
Python
94 lines
3.5 KiB
Python
"""Structured extraction from free-form agent output.
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Kanban runs return free text; the protocol requires AgentMessage. Rather than
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trusting the worker to format correctly mid-run, the engine converts each raw
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report with one schema-enforced pool call (qwen3.5:9b scored 100% validity
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under `format` enforcement — benchmarks/RESULTS.md).
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"""
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import json
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from typing import Any
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import httpx
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from engine.config import settings
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from engine.schemas.events import AGENT_MESSAGE_FORMAT, AgentMessage
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MODEL = "qwen3.5:9b"
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EXTRACT_SYSTEM = (
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"You convert an AI employee's raw work report into exactly one structured "
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"protocol message. Choose the message_type that matches what actually "
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"happened:\n"
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"- task.completed: work finished, evidence given\n"
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"- task.progressed: partial progress, more to do\n"
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"- task.blocked: cannot continue for technical reasons\n"
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"- clarification.requested: needs a decision/answer from a human; include "
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"the options given\n"
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"- approval.requested: needs authority the employee lacks\n"
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"- incident.declared: something broke that affects others\n"
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"Copy concrete facts (files, commits, test counts) into summary/detail. "
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"Do not invent facts not present in the report."
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)
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DECOMPOSE_FORMAT: dict[str, Any] = {
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"type": "object",
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"properties": {
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"tasks": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"title": {"type": "string"},
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"description": {"type": "string"},
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"role": {"type": "string", "enum": ["engineer", "qa", "manager"]},
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"acceptance_criteria": {"type": "array", "items": {"type": "string"}},
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},
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"required": ["title", "description", "role", "acceptance_criteria"],
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},
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},
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"clarification_needed": {"type": "string"},
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},
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"required": ["tasks"],
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}
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async def _chat_structured(system: str, user: str, fmt: dict) -> dict:
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async with httpx.AsyncClient(timeout=180) as client:
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r = await client.post(
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f"{settings.ollama_pool_url}/api/chat",
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json={
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"model": MODEL,
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"messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": user},
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],
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"stream": False,
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"think": False,
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"format": fmt,
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},
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)
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r.raise_for_status()
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return json.loads(r.json()["message"]["content"])
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async def extract_agent_message(work_item_id: str, raw_report: str) -> AgentMessage:
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data = await _chat_structured(
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EXTRACT_SYSTEM,
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f"Work item ID: {work_item_id}\n\nRaw report:\n{raw_report[:6000]}",
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AGENT_MESSAGE_FORMAT,
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)
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data["work_item_id"] = work_item_id # never trust the model with the ID
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return AgentMessage.model_validate(data)
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async def decompose_objective(brief: str, company_name: str) -> dict:
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"""Manager-style decomposition into 2-5 small tasks. Returns validated dict."""
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system = (
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f"You are the engineering manager at {company_name}. Decompose the "
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"objective into 2-5 small, independently completable tasks, each with "
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"concrete acceptance criteria. Assign each to the right role. If the "
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"objective is materially ambiguous, fill clarification_needed with the "
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"question and give your best-guess decomposition anyway."
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)
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return await _chat_structured(system, brief, DECOMPOSE_FORMAT)
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