Race Condition in Shared Agent State — Multiple Agents Corrupting Shared Data
Symptom
- Counter that should reach 100 only reaches 73 after 100 concurrent increments
- Shared task list loses items when multiple agents update it simultaneously
- Two agents both “claim” the same work item — work gets done twice
- Database record shows stale data after concurrent updates
- State is inconsistent in unpredictable ways — bug doesn’t reproduce deterministically
Root Cause
Read-modify-write without atomicity. Agent A reads value (5), Agent B reads value (5), A writes 6, B writes 6 — one increment is lost. Any shared mutable state accessed by concurrent agents without locking is vulnerable.
Fix
Option 1: asyncio.Lock for in-process shared state
import asyncio
class SharedAgentState:
def __init__(self):
self._lock = asyncio.Lock()
self._counter = 0
self._task_queue = []
async def increment_counter(self) -> int:
async with self._lock:
self._counter += 1
return self._counter
async def claim_task(self) -> dict | None:
async with self._lock:
if self._task_queue:
return self._task_queue.pop(0) # Atomic claim
return None
async def add_tasks(self, tasks: list[dict]):
async with self._lock:
self._task_queue.extend(tasks)
# Single shared instance across all agents
state = SharedAgentState()
async def agent_worker(worker_id: int):
while True:
task = await state.claim_task()
if task is None:
break # No more work
# Process task without holding the lock
result = await process_task(task)
await state.increment_counter()
Option 2: Redis atomic operations for distributed agents
import redis.asyncio as redis
class DistributedAgentState:
def __init__(self, redis_url: str):
self.redis = redis.from_url(redis_url)
async def increment_counter(self, key: str) -> int:
"""Atomic increment — no race condition possible"""
return await self.redis.incr(key)
async def claim_task(self, queue_key: str) -> str | None:
"""Atomic pop — only one agent gets each task"""
return await self.redis.lpop(queue_key)
async def set_if_not_exists(self, key: str, value: str, ttl: int = 60) -> bool:
"""Atomic check-and-set (distributed lock)"""
return await self.redis.set(key, value, nx=True, ex=ttl)
# Usage
state = DistributedAgentState("redis://localhost:6379")
async def distributed_agent_worker(worker_id: int):
while True:
task_json = await state.claim_task("agent:task_queue")
if task_json is None:
break
task = json.loads(task_json)
await process_task(task)
await state.increment_counter("agent:completed_count")
Option 3: Database optimistic locking
import sqlalchemy as sa
from sqlalchemy.ext.asyncio import AsyncSession
async def claim_work_item(session: AsyncSession, agent_id: str) -> dict | None:
"""Claim work item using optimistic locking"""
# Find an unclaimed item
result = await session.execute(
sa.select(WorkItem)
.where(WorkItem.status == "pending")
.limit(1)
.with_for_update(skip_locked=True) # Skip rows locked by other agents
)
item = result.scalar_one_or_none()
if item is None:
return None
# Mark as claimed by this agent
item.status = "in_progress"
item.claimed_by = agent_id
item.claimed_at = datetime.utcnow()
await session.commit()
return item
Option 4: Version-based optimistic concurrency
async def update_shared_config(key: str, new_value: dict, expected_version: int) -> bool:
"""Update only if version matches — raises if someone else updated first"""
async with db.transaction():
current = await db.get(key)
if current["version"] != expected_version:
return False # Someone else modified it — caller must retry
await db.update(key, {
**new_value,
"version": expected_version + 1
})
return True
# Usage with retry
async def safe_update(key: str, transform_fn):
for attempt in range(10):
current = await db.get(key)
new_value = transform_fn(current["data"])
success = await update_shared_config(key, new_value, current["version"])
if success:
return
await asyncio.sleep(0.1 * attempt) # Backoff on conflict
raise RuntimeError(f"Could not update {key} after 10 attempts — too much contention")
Option 5: Message queue — eliminate shared state entirely
import asyncio
async def coordinator(task_queue: asyncio.Queue, result_queue: asyncio.Queue):
"""Single coordinator owns state; agents communicate via queues"""
completed = 0
results = []
while True:
result = await result_queue.get()
results.append(result)
completed += 1
print(f"Completed: {completed}")
async def agent_worker(worker_id: int, task_queue: asyncio.Queue, result_queue: asyncio.Queue):
"""Agents only read from task queue, write to result queue — no shared state"""
while True:
try:
task = task_queue.get_nowait()
except asyncio.QueueEmpty:
break
result = await process_task(task)
await result_queue.put(result)
task_queue.task_done()
# Setup
task_q = asyncio.Queue()
result_q = asyncio.Queue()
for task in tasks:
await task_q.put(task)
await asyncio.gather(
coordinator(task_q, result_q),
*[agent_worker(i, task_q, result_q) for i in range(5)]
)
Shared State Danger Zones
| Pattern | Risk | Fix |
|---|---|---|
| Global dict/counter | High | asyncio.Lock or atomic ops |
| File read-modify-write | High | File lock (fcntl.flock) |
| DB row update without lock | High | SELECT FOR UPDATE or optimistic version |
Redis GET then SET |
Medium | INCR, SETNX, or Lua script |
In-memory queue .pop() |
Medium | asyncio.Queue |
| Logging to same file | Low | logging module (thread-safe) |
Expected Token Savings
Debugging race conditions is extremely hard — hours of debugging vs. 10 lines of locking code.
Environment
- Multi-agent systems with shared state; most critical in distributed deployments
- Source: direct experience, classic concurrent programming patterns
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