fix(agents): collapse repeated waits queued inside one model turn (#979)

* fix(agents): collapse repeated waits queued inside one model turn

* fix(agents): state that one wait is enough in every prompt variant

---------

Co-authored-by: Ahmed Allam <ahmed39652003@gmail.com>
This commit is contained in:
devin-ai-integration[bot]
2026-08-06 00:07:30 +03:00
committed by GitHub
co-authored by Ahmed Allam
parent 6735a6f89e
commit 0abe82d622
4 changed files with 155 additions and 0 deletions
+1
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@@ -28,6 +28,7 @@ INTER-AGENT MESSAGES:
- Messages from other agents arrive prefixed with a header like `[Message from agent <name> | type=... | priority=...]`. Treat them as internal context — never repeat them verbatim in your own output.
- Treat agent identity / inherited-context preambles as internal metadata; do not echo them in outputs or tool calls.
- Minimize inter-agent messaging: only message when essential for coordination or assistance; avoid routine status updates; batch non-urgent information; prefer parent/child completion flows and shared artifacts over messaging
- wait_for_agents blocks and resumes you automatically, so it is never a poll you repeat: issue exactly ONE wait, then stop and react to what it returns. Never write out a wait/check loop (wait → view_agent_graph → wait → ...) ahead of time — those extra calls only strand you and are collapsed anyway
{% if interactive %}
INTERACTIVE BEHAVIOR:
+3
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@@ -20,6 +20,8 @@ if TYPE_CHECKING:
logger = logging.getLogger(__name__)
LLM_TURN_KEY = "llm_turn"
_STAGE_LABELS: tuple[str, ...] = ("NOTICE", "URGENT", "CRITICAL")
_TURN_WARN_BANDS: tuple[float, ...] = (0.70, 0.85, 0.95)
_ROOT_BUDGET_WARN_BANDS: tuple[float, ...] = (0.70, 0.85, 0.95)
@@ -144,6 +146,7 @@ class ReportUsageHooks(RunHooks[dict[str, Any]]):
system_prompt: str | None, # noqa: ARG002
input_items: list[TResponseInputItem],
) -> None:
context.context[LLM_TURN_KEY] = int(context.context.get(LLM_TURN_KEY, 0)) + 1
try:
self._maybe_warn_turns(context, input_items)
self._maybe_warn_budget(context, input_items)
+25
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@@ -14,6 +14,7 @@ from agents import RunContextWrapper, function_tool
from strix.core.agents import Status, coordinator_from_context
from strix.core.execution import notify_parent_on_terminal
from strix.core.hooks import LLM_TURN_KEY
from strix.skills import validate_requested_skills
@@ -224,6 +225,7 @@ _WAIT_DEFAULT_TIMEOUT_S = 300
# ``timeout_seconds`` the model asks for. One second of headroom lets the
# tool's own timeout fire first and return a clean result.
_WAIT_HARD_CEILING_S = _WAIT_DEFAULT_TIMEOUT_S + 1
_WAITED_TURN_KEY = "waited_llm_turn"
@function_tool(timeout=_WAIT_HARD_CEILING_S)
@@ -239,6 +241,11 @@ async def wait_for_agents( # noqa: PLR0911
completion reports. You resume the instant any message arrives, so
size ``timeout_seconds`` to the work you're awaiting.
**Issue exactly one wait, then stop and react to what it returns.**
This call blocks and resumes on its own; it is not a poll you repeat.
Do not write out a wait/check loop ahead of time — a second wait in
the same turn returns immediately without waiting.
**This tool is only for waiting on other agents.** Two things it is
NOT for:
@@ -290,6 +297,24 @@ async def wait_for_agents( # noqa: PLR0911
default=str,
)
turn = inner.get(LLM_TURN_KEY)
if turn is not None and inner.get(_WAITED_TURN_KEY) == turn:
return json.dumps(
{
"success": True,
"wait_outcome": "already_waited",
"reason": reason,
"note": (
"You already waited in this turn. A single wait_for_agents blocks and "
"resumes on its own, so queueing more waits only strands you — issue one "
"wait, then react to what it returns."
),
},
ensure_ascii=False,
default=str,
)
inner[_WAITED_TURN_KEY] = turn
async with coordinator._lock:
stopped = coordinator.statuses.get(me) == "stopped"
if stopped: