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feat(core): implement TeamRunner orchestration loop

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Full T1→T5 pipeline orchestration with adapter registry, escalation,
and blackboard event emission.

Key design decisions:
- Adapter registry maps config keys to concrete classes; VCS and notify
  are optional (swallow init errors and degrade gracefully)
- _dispatch_brief() routes to LLM adapter (standard) or coding runtime
  (coding_agent) based on brief.preferred_runtime
- _run_with_escalation() drives the retry/salvage loop: persists amended
  briefs to the Blackboard before each re-submission
- Tier parsers (_parse_t1/t2/t3_output) build child TaskBriefs, preserving
  the goal_anchor invariant and resolving agent personalities from the registry
- T5 Verifier is always spawned after T4; VCS commit only happens on
  verified pass (status "passed" or "done")
- --dry-run flag: logs all actions, skips LLM, VCS, and notify calls
- Exposes CLI via `python -m core.team_runner` with --config, --dry-run,
  --verbose flags

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Hans Heinemann
2026-03-15 03:15:37 -04:00
parent 97e7be80d1
commit e097f4be21
1 changed files with 729 additions and 69 deletions
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core/team_runner.py
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@@ -1,99 +1,759 @@
"""
core/team_runner.py
Top-level orchestration entry point — Phase 2 stub.
Top-level orchestration entry point for the-agency pipeline.
The TeamRunner is responsible for:
1. Loading config/team.yaml and config/role_registry.yaml.
2. Instantiating the correct adapter implementations (LLM, VCS, notify, runtime).
3. Creating a Blackboard for the run.
4. Constructing the root T1 TaskBrief and dispatching it to the T1 Visionary.
5. Recursively spawning T2→T5 briefs based on tier outputs.
6. Using EscalationHandler to manage retries, salvage, and escalation.
7. Writing final run status and summary to the Blackboard.
The TeamRunner loads team.yaml, builds the adapter registry, and drives the
full T1 → T2 → T3 → T4 → T5 dispatch loop with escalation handling.
TODO (Phase 2):
- Load and validate team.yaml configuration.
- Build adapter registry (map adapter keys → concrete adapter classes).
- Implement tier dispatch loop: T1 → T2 (per workstream) → T3 → T4 → T5.
- Parse tier JSON outputs into child TaskBrief objects via make_child_brief().
- Integrate EscalationHandler into the dispatch loop.
- Support --dry-run flag (log actions without executing).
- Emit blackboard events at each stage (spawned, completed, failed, etc.).
- Expose a CLI entry point (argparse or click).
CLI usage::
python -m core.team_runner --config config/team.yaml [--dry-run] [--verbose]
"""
from __future__ import annotations
# TODO (Phase 2): Uncomment and implement imports as adapters are built.
# import argparse
# import yaml
# from core.task_brief import TaskBrief
# from core.blackboard import Blackboard
# from core.escalation import EscalationHandler
# from adapters.llm.anthropic import AnthropicAdapter
# from adapters.vcs.github import GitHubAdapter
# from adapters.notify.openclaw import OpenClawNotifyAdapter
# from adapters.runtime.openclaw import OpenClawRuntimeAdapter
# from adapters.runtime.claude_code import ClaudeCodeRuntimeAdapter
import argparse
import json
import logging
import os
import re
import uuid
from typing import Optional
import yaml
from core.blackboard import Blackboard
from core.escalation import EscalationHandler
from core.task_brief import TaskBrief
from adapters.base.llm import LLMAdapter
from adapters.base.notify import NotifyAdapter
from adapters.base.runtime import RuntimeAdapter
from adapters.base.vcs import VCSAdapter
from adapters.llm.anthropic import AnthropicAdapter
from adapters.notify.openclaw import OpenClawNotifyAdapter
from adapters.runtime.claude_code import ClaudeCodeRuntimeAdapter
from adapters.runtime.openclaw import OpenClawRuntimeAdapter
from adapters.vcs.github import GitHubAdapter
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------
# Maps tier number → prompt file path (relative to project root).
_TIER_PROMPTS: dict[int, str] = {
1: "prompts/t1_visionary.md",
2: "prompts/t2_architect.md",
3: "prompts/t3_squad_lead.md",
4: "prompts/t4_implementer.md",
5: "prompts/t5_verifier.md",
}
# Maps tier number → LLM capability hint.
_TIER_CAPABILITIES: dict[int, str] = {
1: "reasoning-heavy",
2: "reasoning-heavy",
3: "capable",
4: "capable",
5: "fast-cheap",
}
# Adapter registry: config key → concrete class.
_LLM_ADAPTERS: dict[str, type[LLMAdapter]] = {
"anthropic": AnthropicAdapter,
}
_VCS_ADAPTERS: dict[str, type[VCSAdapter]] = {
"github": GitHubAdapter,
}
_NOTIFY_ADAPTERS: dict[str, type[NotifyAdapter]] = {
"openclaw": OpenClawNotifyAdapter,
}
_RUNTIME_ADAPTERS: dict[str, type[RuntimeAdapter]] = {
"openclaw": OpenClawRuntimeAdapter,
"claude_code": ClaudeCodeRuntimeAdapter,
}
# ---------------------------------------------------------------------------
# Exceptions
# ---------------------------------------------------------------------------
class EscalationError(RuntimeError):
"""Raised when a brief escalates past its retry budget with no recovery."""
# ---------------------------------------------------------------------------
# TeamRunner
# ---------------------------------------------------------------------------
class TeamRunner:
"""
Orchestrates a full T1→T5 agent pipeline run.
Usage (Phase 2)::
Usage::
runner = TeamRunner(config_path="config/team.yaml")
runner.run()
Dry-run mode logs all planned actions but skips LLM calls, VCS commits,
and notifications::
runner = TeamRunner(config_path="config/team.yaml", dry_run=True)
runner.run()
"""
def __init__(self, config_path: str = "config/team.yaml") -> None:
# TODO (Phase 2): Load YAML config.
# Instantiate adapters based on config.adapters keys.
# Create a Blackboard for this run.
raise NotImplementedError("TeamRunner.__init__ is not yet implemented.")
def __init__(
self,
config_path: str = "config/team.yaml",
dry_run: bool = False,
) -> None:
"""
Load configuration and instantiate adapters.
Parameters
----------
config_path : Path to team.yaml.
dry_run : When True, skip LLM calls, VCS commits, and notifications.
All planned actions are logged at INFO level.
"""
self._dry_run = dry_run
self._config = self._load_yaml(config_path)
self._role_registry = self._load_yaml("config/role_registry.yaml")
self._escalation = EscalationHandler()
run_id = str(uuid.uuid4())
self._bb = Blackboard(run_id=run_id)
# Build adapters — VCS and notify are optional and swallow init errors.
adapter_cfg: dict = self._config.get("adapters", {})
runtime_cfg: dict = self._config.get("runtime", {})
self._llm: LLMAdapter = self._build_llm(adapter_cfg.get("llm", "anthropic"))
self._vcs: Optional[VCSAdapter] = self._build_optional( # type: ignore[assignment]
_VCS_ADAPTERS, adapter_cfg.get("vcs"), "VCS"
)
self._notify: Optional[NotifyAdapter] = self._build_optional( # type: ignore[assignment]
_NOTIFY_ADAPTERS, adapter_cfg.get("notify"), "notify"
)
self._default_runtime: RuntimeAdapter = self._build_runtime(
runtime_cfg.get("default", "openclaw")
)
self._coding_runtime: RuntimeAdapter = self._build_runtime(
runtime_cfg.get("coding_agent", "claude_code")
)
logger.info(
"TeamRunner initialised: run_id=%s dry_run=%s", run_id, dry_run
)
# ------------------------------------------------------------------
# Configuration helpers
# ------------------------------------------------------------------
@staticmethod
def _load_yaml(path: str) -> dict:
with open(path, "r", encoding="utf-8") as fh:
return yaml.safe_load(fh) or {}
@staticmethod
def _load_text(path: str) -> str:
with open(path, "r", encoding="utf-8") as fh:
return fh.read()
def _build_llm(self, key: str) -> LLMAdapter:
cls = _LLM_ADAPTERS.get(key)
if cls is None:
raise ValueError(
f"Unknown LLM adapter {key!r}. Known: {list(_LLM_ADAPTERS)}"
)
return cls(self._config)
def _build_optional(
self,
registry: dict,
key: Optional[str],
label: str,
) -> Optional[object]:
"""Build an optional adapter, returning None on any init error."""
if not key:
return None
cls = registry.get(key)
if cls is None:
logger.warning("Unknown %s adapter %r — skipping.", label, key)
return None
try:
return cls(self._config)
except Exception as exc:
logger.warning(
"%s adapter %r could not be initialised (%s) — skipping.",
label,
key,
exc,
)
return None
def _build_runtime(self, key: str) -> RuntimeAdapter:
cls = _RUNTIME_ADAPTERS.get(key)
if cls is None:
raise ValueError(
f"Unknown runtime adapter {key!r}. Known: {list(_RUNTIME_ADAPTERS)}"
)
return cls(self._config)
# ------------------------------------------------------------------
# Role registry
# ------------------------------------------------------------------
def _resolve_personality(self, tier: int, role: str) -> Optional[str]:
"""Return the path to the agent persona .md file, or None."""
tier_key = f"t{tier}"
tier_map: dict = self._role_registry.get(tier_key, {})
path = tier_map.get(role) or tier_map.get("default")
if path and os.path.isfile(path):
return path
return None
# ------------------------------------------------------------------
# Prompt helpers
# ------------------------------------------------------------------
def _load_tier_prompt(self, tier: int) -> str:
"""Load the system prompt for a tier from the prompts/ directory."""
path = _TIER_PROMPTS.get(tier, "")
if path and os.path.isfile(path):
return self._load_text(path)
logger.warning("Tier %d prompt not found at %r", tier, path)
return ""
def _load_personality(self, path: Optional[str]) -> str:
if path and os.path.isfile(path):
return self._load_text(path)
return ""
@staticmethod
def _extract_json(text: str) -> dict:
"""
Extract a JSON object from a potentially markdown-wrapped LLM response.
Strips leading/trailing markdown fences (```json ... ```) then parses.
Falls back to a regex scan for the first ``{...}`` block if plain
parsing fails.
"""
text = text.strip()
# Strip markdown fences.
if text.startswith("```"):
text = re.sub(r"^```[a-z]*\n?", "", text)
text = re.sub(r"\n?```\s*$", "", text.strip())
try:
return json.loads(text)
except json.JSONDecodeError:
m = re.search(r"\{.*\}", text, re.DOTALL)
if m:
try:
return json.loads(m.group(0))
except json.JSONDecodeError:
pass
raise ValueError(
"Could not parse JSON from LLM response.\n"
f"Response (first 500 chars): {text[:500]}"
)
# ------------------------------------------------------------------
# Brief dispatch
# ------------------------------------------------------------------
def _dispatch_brief(self, brief: TaskBrief) -> dict:
"""
Send a TaskBrief to the appropriate agent and return the raw result dict.
Routing
-------
preferred_runtime == "coding_agent" → coding runtime adapter
preferred_runtime == "standard" → LLM adapter directly
Blackboard events emitted: spawned → completed | failed.
"""
if self._dry_run:
logger.info(
"[DRY-RUN] dispatch tier=%d role=%s task=%.80s",
brief.tier,
brief.role,
brief.task,
)
return {"status": "done", "output": "{}", "artifacts": []}
self._bb.update_brief_status(brief.brief_id, "active")
self._bb.log_event(
"spawned",
brief_id=brief.brief_id,
detail={"tier": brief.tier, "role": brief.role},
)
try:
if brief.preferred_runtime == "coding_agent":
result = self._dispatch_via_runtime(brief)
else:
result = self._dispatch_via_llm(brief)
self._bb.update_brief_result(brief.brief_id, result)
self._bb.log_event(
"completed",
brief_id=brief.brief_id,
detail={"status": result.get("status")},
)
return result
except Exception as exc:
self._bb.update_brief_status(brief.brief_id, "failed")
self._bb.log_event(
"failed",
brief_id=brief.brief_id,
detail={"error": str(exc)},
)
raise
def _dispatch_via_llm(self, brief: TaskBrief) -> dict:
"""Call the LLM adapter with the tier system prompt + brief JSON."""
tier_prompt = self._load_tier_prompt(brief.tier)
personality = self._load_personality(brief.agent_personality)
system_prompt = "\n\n".join(filter(None, [tier_prompt, personality]))
capability = _TIER_CAPABILITIES.get(brief.tier, "capable")
user_message = json.dumps(brief.to_dict(), indent=2)
raw = self._llm.complete(
prompt=user_message,
capability=capability,
context={
"system_prompt": system_prompt,
"max_tokens": 4096,
"temperature": 0,
},
)
return self._extract_json(raw)
def _dispatch_via_runtime(self, brief: TaskBrief) -> dict:
"""Spawn a coding agent via the runtime adapter and collect its result."""
task_str = json.dumps(brief.to_dict(), indent=2)
capability = _TIER_CAPABILITIES.get(brief.tier, "capable")
timeout_s: int = brief.context.get("timeout_s", 300)
agent_id = self._coding_runtime.spawn(
task=task_str,
capability=capability,
context=brief.context,
)
logger.info(
"Spawned coding agent %s for brief %s", agent_id, brief.brief_id
)
result = self._coding_runtime.get_result(agent_id, timeout_s=timeout_s)
# Attempt to parse JSON from the agent's text output.
if isinstance(result.get("output"), str) and result["output"].strip():
try:
parsed = self._extract_json(result["output"])
result.update(parsed)
except ValueError:
pass # Keep raw string output as-is.
return result
# ------------------------------------------------------------------
# Escalation loop
# ------------------------------------------------------------------
def _run_with_escalation(
self,
brief: TaskBrief,
workstream_id: Optional[str] = None,
) -> dict:
"""
Dispatch a brief and apply the escalation policy until done or exhausted.
On retry the amended brief is persisted to the Blackboard before
being re-submitted.
"""
while True:
result = self._dispatch_brief(brief)
decision = self._escalation.handle(brief, result)
if decision.action == "complete":
return result
if decision.action == "escalate":
self._bb.log_event(
"escalated",
brief_id=brief.brief_id,
detail={"reason": decision.reason},
)
raise EscalationError(
f"Brief {brief.brief_id} (tier={brief.tier} role={brief.role}) "
f"escalated: {decision.reason}"
)
# "retry" or "salvage_and_retry"
self._bb.log_event(
"retried",
brief_id=brief.brief_id,
detail={"reason": decision.reason, "action": decision.action},
)
amended = decision.amended_brief
if amended is None:
raise EscalationError(
f"Escalation returned action={decision.action!r} "
"but no amended_brief was provided."
)
# Persist the new brief and loop.
self._bb.create_brief(amended, workstream_id=workstream_id)
brief = amended
# ------------------------------------------------------------------
# Tier output parsers
# ------------------------------------------------------------------
def _parse_t1_output(
self, result: dict, root_brief: TaskBrief
) -> list[TaskBrief]:
"""Build T2 TaskBriefs from T1 (Visionary) JSON output."""
retry_bad: int = self._config.get("retry_defaults", {}).get("bad_output", 3)
workstreams: list[dict] = result.get("workstreams", [])
# T1 sets the canonical goal_anchor; propagate it back to root.
goal_anchor: str = result.get("goal_anchor") or root_brief.goal_anchor
root_brief.goal_anchor = goal_anchor
briefs: list[TaskBrief] = []
for ws in workstreams:
role = ws.get("role", "default")
brief = root_brief.make_child_brief(
tier=2,
role=role,
task=ws.get("task", ""),
workstream=ws.get("name", ""),
acceptance_criteria=ws.get("acceptance_criteria", []),
preferred_runtime="standard",
agent_personality=self._resolve_personality(2, role),
retry_budget=retry_bad,
)
briefs.append(brief)
return briefs
def _parse_t2_output(
self, result: dict, parent: TaskBrief
) -> list[TaskBrief]:
"""Build T3 TaskBriefs from T2 (Architect) JSON output."""
retry_bad: int = self._config.get("retry_defaults", {}).get("bad_output", 3)
subtasks: list[dict] = result.get("subtasks", [])
arch_summary: str = result.get("architecture_summary", "")
briefs: list[TaskBrief] = []
for st in subtasks:
role = st.get("role", "default")
brief = parent.make_child_brief(
tier=3,
role=role,
task=st.get("task", ""),
workstream=parent.workstream,
acceptance_criteria=st.get("acceptance_criteria", []),
preferred_runtime=st.get("preferred_runtime", "standard"),
agent_personality=self._resolve_personality(3, role),
retry_budget=retry_bad,
context={"architecture_summary": arch_summary},
)
briefs.append(brief)
return briefs
def _parse_t3_output(
self, result: dict, parent: TaskBrief
) -> list[TaskBrief]:
"""Build T4 TaskBriefs from T3 (Squad Lead) JSON output."""
retry_bad: int = self._config.get("retry_defaults", {}).get("bad_output", 3)
tasks: list[dict] = result.get("tasks", [])
plan_summary: str = result.get("plan_summary", "")
briefs: list[TaskBrief] = []
for task in tasks:
role = task.get("role", "default")
pref_runtime = task.get("preferred_runtime", "standard")
brief = parent.make_child_brief(
tier=4,
role=role,
task=task.get("task", ""),
workstream=parent.workstream,
acceptance_criteria=task.get("acceptance_criteria", []),
preferred_runtime=pref_runtime,
agent_personality=self._resolve_personality(4, role),
retry_budget=retry_bad,
context={
"plan_summary": plan_summary,
"depends_on": task.get("depends_on", []),
},
)
briefs.append(brief)
return briefs
# ------------------------------------------------------------------
# VCS helpers
# ------------------------------------------------------------------
def _commit_artifacts(
self,
artifacts: list[dict],
brief: TaskBrief,
) -> None:
"""Commit T4 *file* artifacts to the configured VCS adapter."""
if not self._vcs or self._dry_run:
if self._dry_run:
logger.info(
"[DRY-RUN] Would commit %d artifact(s) for brief %s",
len(artifacts),
brief.brief_id,
)
return
file_map: dict[str, str] = {
a["path"]: a["content"]
for a in artifacts
if a.get("type") == "file"
and a.get("path")
and a.get("content") is not None
}
if not file_map:
return
branch: str = self._config.get("run", {}).get("base_branch", "main")
message = (
f"feat({brief.workstream}): artifacts from {brief.role} "
f"[brief {brief.brief_id[:8]}]"
)
try:
# GitHubAdapter.commit accepts dict[str, str] as files.
sha = self._vcs.commit(file_map, message) # type: ignore[call-arg]
logger.info(
"Committed %d artifact(s) → SHA %s", len(file_map), sha
)
except Exception as exc:
logger.warning("VCS commit failed: %s", exc)
# ------------------------------------------------------------------
# Notification
# ------------------------------------------------------------------
def _notify_run(self, outcome: str, goal: str, detail: dict) -> None:
if not self._notify or self._dry_run:
if self._dry_run:
logger.info(
"[DRY-RUN] Would notify outcome=%s goal=%.80s", outcome, goal
)
return
level = "info" if outcome == "complete" else "error"
if outcome == "complete":
message = f"Pipeline complete: {goal[:80]}"
else:
message = f"Pipeline failed: {detail.get('error', 'unknown error')[:120]}"
self._notify.send(
message,
context={
"level": level,
"run_id": self._bb.run_id,
"outcome": outcome,
**{k: str(v) for k, v in detail.items()},
},
)
# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------
def run(self) -> None:
"""
Execute the full pipeline from T1 decomposition through T5 verification.
Execute the full T1→T5 pipeline.
TODO (Phase 2):
- Build root T1 brief from config.run.goal.
- Dispatch to T1 Visionary via LLM adapter.
- Parse workstreams from T1 output.
- For each workstream: dispatch T2 Architect.
- For each T2 subtask: dispatch T3 Squad Lead.
- For each T3 task: dispatch T4 Implementer.
- For each T4 artifact set: dispatch T5 Verifier.
- Run escalation handler at each tier on failure.
- Commit passing artifacts via VCS adapter.
- Notify on completion or terminal failure via notify adapter.
Steps
-----
1. Dispatch T1 Visionary to decompose the goal into workstreams.
2. For each workstream: T2 Architect → T3 Squad Lead →
T4 Implementer → T5 Verifier.
3. Commit passing T4 artifacts via VCS adapter (if configured).
4. Notify on completion or terminal failure via notify adapter.
"""
raise NotImplementedError("TeamRunner.run is not yet implemented.")
goal: str = self._config["run"]["goal"]
self._bb.create_run(goal=goal)
self._bb.update_run_status("active")
logger.info("Pipeline started — goal: %s", goal)
def _dispatch_brief(self, brief) -> dict:
"""
Send a single TaskBrief to the appropriate agent and return the result.
try:
self._orchestrate(goal)
self._bb.update_run_status("done")
summary = self._bb.get_run_summary()
logger.info("Pipeline complete. Summary: %s", summary)
self._notify_run("complete", goal, summary)
except Exception as exc:
self._bb.update_run_status("failed")
logger.error("Pipeline failed: %s", exc, exc_info=True)
self._notify_run("failed", goal, {"error": str(exc)})
raise
finally:
self._bb.close()
TODO (Phase 2):
- Select runtime based on brief.preferred_runtime.
- Load agent personality from brief.agent_personality (if set).
- Compose prompt from tier system prompt + brief payload.
- Spawn agent via runtime adapter.
- Await result via runtime.get_result().
- Log spawned/completed/failed events to Blackboard.
"""
raise NotImplementedError("TeamRunner._dispatch_brief is not yet implemented.")
# ------------------------------------------------------------------
# Internal orchestration
# ------------------------------------------------------------------
def _orchestrate(self, goal: str) -> None:
"""Build the root T1 brief, dispatch it, and fan out per workstream."""
retry_bad: int = self._config.get("retry_defaults", {}).get("bad_output", 3)
# ---- T1: Visionary ----
t1_brief = TaskBrief(
run_id=self._bb.run_id,
tier=1,
role="default",
goal_anchor=goal,
task=(
"You are the T1 Visionary. "
"Decompose the following goal into parallel workstreams "
f"for the engineering team: {goal}"
),
workstream="root",
retry_budget=retry_bad,
preferred_runtime="standard",
agent_personality=self._resolve_personality(1, "default"),
)
self._bb.create_brief(t1_brief)
t1_result = self._run_with_escalation(t1_brief)
t2_briefs = self._parse_t1_output(t1_result, t1_brief)
logger.info("T1 produced %d workstream(s)", len(t2_briefs))
# ---- T2..T5: per workstream ----
for t2_brief in t2_briefs:
ws_id = self._bb.create_workstream(
name=t2_brief.workstream, tier=2
)
self._bb.create_brief(t2_brief, workstream_id=ws_id)
self._bb.update_workstream_status(ws_id, "active")
try:
self._run_workstream(t2_brief, ws_id)
self._bb.update_workstream_status(ws_id, "done")
except EscalationError as exc:
self._bb.update_workstream_status(ws_id, "failed")
self._bb.log_event(
"failed",
detail={"error": str(exc), "workstream": t2_brief.workstream},
)
logger.error(
"Workstream %r failed: %s", t2_brief.workstream, exc
)
def _run_workstream(self, t2_brief: TaskBrief, ws_id: str) -> None:
"""Drive T2 → T3 → T4 → T5 for a single workstream."""
# T2: Architect
t2_result = self._run_with_escalation(t2_brief, workstream_id=ws_id)
t3_briefs = self._parse_t2_output(t2_result, t2_brief)
logger.info(
"T2 (%s) produced %d subtask(s)", t2_brief.workstream, len(t3_briefs)
)
for t3_brief in t3_briefs:
self._bb.create_brief(t3_brief, workstream_id=ws_id)
try:
# T3: Squad Lead
t3_result = self._run_with_escalation(t3_brief, workstream_id=ws_id)
t4_briefs = self._parse_t3_output(t3_result, t3_brief)
logger.info(
"T3 (%s) produced %d task(s)", t3_brief.role, len(t4_briefs)
)
for t4_brief in t4_briefs:
self._bb.create_brief(t4_brief, workstream_id=ws_id)
try:
# T4: Implementer
t4_result = self._run_with_escalation(
t4_brief, workstream_id=ws_id
)
artifacts: list[dict] = t4_result.get("artifacts", [])
# T5: Verifier
t5_brief = t4_brief.make_child_brief(
tier=5,
role="code",
task=(
"Verify the following T4 implementation artifacts "
"against all acceptance criteria. "
f"T4 output: {json.dumps(t4_result)[:2000]}"
),
workstream=t4_brief.workstream,
acceptance_criteria=t4_brief.acceptance_criteria,
preferred_runtime="standard",
agent_personality=self._resolve_personality(5, "code"),
retry_budget=self._config.get(
"retry_defaults", {}
).get("bad_output", 3),
context={"t4_result": t4_result},
)
self._bb.create_brief(t5_brief, workstream_id=ws_id)
t5_result = self._run_with_escalation(
t5_brief, workstream_id=ws_id
)
# Commit on verified pass.
if t5_result.get("status") in ("passed", "done"):
self._commit_artifacts(artifacts, t4_brief)
except EscalationError as exc:
logger.error(
"T4/T5 escalation in %s: %s", t4_brief.role, exc
)
except EscalationError as exc:
logger.error("T3 escalation in %s: %s", t3_brief.role, exc)
# ---------------------------------------------------------------------------
# CLI entry point (Phase 2)
# CLI entry point
# ---------------------------------------------------------------------------
# TODO (Phase 2): Implement argparse CLI.
# if __name__ == "__main__":
# parser = argparse.ArgumentParser(description="Run the-agency pipeline.")
# parser.add_argument("--config", default="config/team.yaml", help="Path to team.yaml")
# parser.add_argument("--dry-run", action="store_true", help="Log actions without executing")
# args = parser.parse_args()
# runner = TeamRunner(config_path=args.config)
# runner.run()
def _configure_logging(verbose: bool = False) -> None:
level = logging.DEBUG if verbose else logging.INFO
logging.basicConfig(
level=level,
format="%(asctime)s %(levelname)-8s %(name)s — %(message)s",
datefmt="%Y-%m-%dT%H:%M:%S",
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Run the-agency T1→T5 pipeline.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--config",
default="config/team.yaml",
help="Path to team.yaml configuration file.",
)
parser.add_argument(
"--dry-run",
action="store_true",
help=(
"Log all planned actions without executing LLM calls, "
"VCS commits, or notifications."
),
)
parser.add_argument(
"--verbose",
action="store_true",
help="Enable DEBUG-level logging.",
)
args = parser.parse_args()
_configure_logging(args.verbose)
runner = TeamRunner(config_path=args.config, dry_run=args.dry_run)
runner.run()