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implemenation on the ai agents and registry

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This commit is contained in:
dharaneesh-r
2026-09-30 14:56:08 +05:30
parent aa3bb35733
commit 5a7d32cc04
15 changed files with 803 additions and 72 deletions
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42
agents/dispatch_agent.py
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@@ -12,7 +12,9 @@ import redis.asyncio as aioredis
from core.agent import SpecializedAgent
from core.types import AgentTask, MessageType
from core.logger import logger
from core.llm import decide_assignment_failure, build_assignment_failure_context
from core.llm import decide_assignment_failure, build_assignment_failure_context, LLM_MODEL
from core.registry import registry
from core.decisions import record_decision
from config.system_config import (
NATS_HOST, NATS_PORT, NATS_USER, NATS_PASSWORD,
REDIS_HOST, REDIS_PORT, REDIS_PASSWORD,
@@ -264,18 +266,12 @@ class DispatchAgent(SpecializedAgent):
f"miler={miler_id} hub={hub_id} confidence={confidence} "
f"reasoning={reasoning!r}"
)
await self.send_message(
recipient="HUB_AGENT",
message_type=MessageType.AGENT_TASK,
payload={
"task_type": "prepare_receiving",
"booking_id": booking_id,
"miler_id": miler_id,
"hub_id": hub_id,
},
correlation_id=str(booking_id),
)
# No longer forwarded to HUB_AGENT as `prepare_receiving`. HUB_AGENT
# is a simulation over eight fictional hubs keyed "XX-HUB-01"; handed a
# real backend hub id and booking id it answered "Hub not found" for
# every assignment, so the hand-off did nothing but log noise. Restore
# it only once HUB_AGENT reads real hubs (agent registry status:
# simulation).
except Exception as e:
logger.error(f"DispatchAgent booking.assigned handler error: {e}")
@@ -305,17 +301,25 @@ class DispatchAgent(SpecializedAgent):
f"reason={data.get('reason')!r}"
)
if not registry.skill_enabled("assignment_failure_triage"):
logger.info(
f"[DISPATCH] booking={booking_id}: skill assignment_failure_triage is disabled "
"in the agent registry; not triaging"
)
return
facts, count = await self._gather_assignment_facts(zone_id, lat, lon)
realert_every = int(registry.threshold("assignment_failure_triage", "realertEvery", DISPATCH_REALERT_EVERY))
logger.info(f"[DISPATCH] booking={booking_id} context facts: {facts}")
# Rate limit: once this zone has been alerted today, don't re-decide
# (and don't pay for an LLM call) on every further failure.
if facts.get("alert_already_sent_today"):
if DISPATCH_REALERT_EVERY and count % DISPATCH_REALERT_EVERY == 0:
if realert_every and count % realert_every == 0:
await self._ops_alert(
zone_id, booking_id,
f"Zone {zone_id}: {count} failed assignments today and still climbing "
f"(re-alert every {DISPATCH_REALERT_EVERY}; earlier alert already raised).",
f"(re-alert every {realert_every}; earlier alert already raised).",
severity="high",
)
else:
@@ -325,7 +329,11 @@ class DispatchAgent(SpecializedAgent):
)
return
decision = await decide_assignment_failure(build_assignment_failure_context(facts))
model = registry.model("DISPATCH_AGENT")
decision = await decide_assignment_failure(build_assignment_failure_context(facts), model)
if decision is not None:
record_decision("assignment_failure", booking_id, facts, decision.action, decision.confidence,
decision.reasoning, model or LLM_MODEL)
if decision is None:
logger.warning(f"No LLM decision for booking {booking_id}; falling back to count>=3 heuristic")
@@ -347,7 +355,7 @@ class DispatchAgent(SpecializedAgent):
pass # transient — the backend will retry
elif decision.action == "notify_customer":
if DISPATCH_AGENT_AUTONOMOUS:
if registry.autonomous("DISPATCH_AGENT", DISPATCH_AGENT_AUTONOMOUS):
await self._notify_customer_delay(booking_id)
else:
# Outward-facing action not authorised — raise an internal proposal instead.