Agent Runs Independent Tasks Sequentially Instead of in Parallel
Symptom
- Agent takes 30 seconds to complete a task that should take 5 seconds
- Profiling shows: 5 API calls × 6 seconds each, run sequentially
- Logs:
fetch_user → fetch_orders → fetch_inventory → fetch_prices → fetch_reviews(one at a time) - Tool calls in agent loop are dispatched one at a time even when independent
- User sees the agent “thinking” for a long time with no output
- Scaling to 100 items takes 100× longer instead of near-constant time
Root Cause
Sequential execution is the default. await call_a() completes before await call_b() starts. Without explicit parallelization, every step blocks the next. This is correct when steps depend on each other (step B needs step A’s output) but wasteful when steps are independent. Agents often request multiple pieces of information that could be gathered simultaneously — fetching user data, order history, and product catalog don’t depend on each other.
Fix
Option 1: asyncio.gather — run independent coroutines in parallel
import asyncio
import httpx
import time
# WRONG — sequential: 5 × 2s = 10s total
async def fetch_all_sequential(user_id: str) -> dict:
start = time.monotonic()
user = await fetch_user(user_id) # 2s
orders = await fetch_orders(user_id) # 2s
inventory = await fetch_inventory() # 2s
prices = await fetch_prices() # 2s
reviews = await fetch_reviews(user_id) # 2s
elapsed = time.monotonic() - start
print(f"Sequential: {elapsed:.1f}s") # → ~10s
return {"user": user, "orders": orders, "inventory": inventory, ...}
# RIGHT — parallel: max(2s, 2s, 2s, 2s, 2s) = 2s total
async def fetch_all_parallel(user_id: str) -> dict:
start = time.monotonic()
user, orders, inventory, prices, reviews = await asyncio.gather(
fetch_user(user_id),
fetch_orders(user_id),
fetch_inventory(),
fetch_prices(),
fetch_reviews(user_id)
)
elapsed = time.monotonic() - start
print(f"Parallel: {elapsed:.1f}s") # → ~2s
return {"user": user, "orders": orders, "inventory": inventory,
"prices": prices, "reviews": reviews}
# Handle partial failures — one error doesn't cancel others:
async def fetch_all_resilient(user_id: str) -> dict:
results = await asyncio.gather(
fetch_user(user_id),
fetch_orders(user_id),
fetch_inventory(),
fetch_prices(),
fetch_reviews(user_id),
return_exceptions=True # Don't cancel on first error
)
keys = ["user", "orders", "inventory", "prices", "reviews"]
return {
key: None if isinstance(r, Exception) else r
for key, r in zip(keys, results)
}
Option 2: Parallel tool execution in agent loop
import anthropic
import asyncio
client = anthropic.AsyncAnthropic()
async def execute_tool(tool_call: dict) -> dict:
"""Execute a single tool call"""
name = tool_call["name"]
params = tool_call["input"]
match name:
case "fetch_user":
result = await fetch_user(params["user_id"])
case "fetch_orders":
result = await fetch_orders(params["user_id"])
case "search_web":
result = await search_web(params["query"])
case _:
raise ValueError(f"Unknown tool: {name}")
return {"tool_use_id": tool_call["id"], "content": str(result)}
async def run_agent_with_parallel_tools(messages: list[dict]) -> str:
"""
Agent loop that executes all tool calls in a single round in parallel.
Model often requests multiple tools at once — run them concurrently.
"""
while True:
response = await client.messages.create(
model="claude-sonnet-4-6",
messages=messages,
tools=TOOL_DEFINITIONS,
max_tokens=4096
)
if response.stop_reason == "end_turn":
return next(b.text for b in response.content if hasattr(b, "text"))
# Extract all tool calls from this response
tool_calls = [b for b in response.content if b.type == "tool_use"]
if not tool_calls:
break
print(f"Executing {len(tool_calls)} tool calls in parallel...")
start = time.monotonic()
# Run ALL tool calls in parallel — not one at a time
tool_results = await asyncio.gather(
*[execute_tool({"id": tc.id, "name": tc.name, "input": tc.input})
for tc in tool_calls],
return_exceptions=True
)
elapsed = time.monotonic() - start
print(f"All {len(tool_calls)} tools completed in {elapsed:.1f}s")
# Add results to conversation
messages.append({"role": "assistant", "content": response.content})
messages.append({
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": r["tool_use_id"] if not isinstance(r, Exception) else tool_calls[i].id,
"content": str(r) if not isinstance(r, Exception) else f"Error: {r}",
"is_error": isinstance(r, Exception)
}
for i, r in enumerate(tool_results)
]
})
Option 3: Batch processing with controlled concurrency
import asyncio
from typing import TypeVar, Callable, Awaitable
T = TypeVar("T")
R = TypeVar("R")
async def parallel_map(
items: list[T],
fn: Callable[[T], Awaitable[R]],
max_concurrent: int = 10,
return_exceptions: bool = False
) -> list[R]:
"""
Apply async function to all items in parallel with concurrency limit.
Equivalent to: [await fn(item) for item in items] but parallel.
"""
semaphore = asyncio.Semaphore(max_concurrent)
async def bounded(item: T) -> R:
async with semaphore:
return await fn(item)
return await asyncio.gather(
*[bounded(item) for item in items],
return_exceptions=return_exceptions
)
# Process 1000 items, 10 at a time:
async def process_all_users(user_ids: list[str]) -> list[dict]:
start = time.monotonic()
results = await parallel_map(
user_ids,
fn=process_single_user,
max_concurrent=10 # Respect API rate limits
)
elapsed = time.monotonic() - start
# Sequential: 1000 × 2s = 2000s
# Parallel(10): ~200s (10× speedup)
print(f"Processed {len(user_ids)} users in {elapsed:.1f}s "
f"({len(user_ids)/elapsed:.1f} users/sec)")
return results
# Chunk large batches to avoid memory pressure:
async def process_in_chunks(items: list, chunk_size: int = 100) -> list:
all_results = []
for i in range(0, len(items), chunk_size):
chunk = items[i:i + chunk_size]
chunk_results = await parallel_map(chunk, process_item, max_concurrent=10)
all_results.extend(chunk_results)
print(f"Progress: {min(i + chunk_size, len(items))}/{len(items)}")
return all_results
Option 4: Identify parallelizable steps with dependency analysis
from dataclasses import dataclass, field
@dataclass
class TaskStep:
name: str
depends_on: list[str] = field(default_factory=list)
fn: callable = None
class ParallelTaskPlanner:
"""
Given a set of tasks with dependencies, run independent ones in parallel.
Tasks with no unfulfilled dependencies run concurrently.
"""
def __init__(self, steps: list[TaskStep]):
self.steps = {s.name: s for s in steps}
self.results: dict[str, any] = {}
async def run(self) -> dict[str, any]:
completed = set()
in_progress = set()
while len(completed) < len(self.steps):
# Find tasks whose dependencies are all satisfied
ready = [
name for name, step in self.steps.items()
if name not in completed
and name not in in_progress
and all(dep in completed for dep in step.depends_on)
]
if not ready:
if not in_progress:
raise RuntimeError(f"Deadlock: no progress possible. Completed: {completed}")
await asyncio.sleep(0.01)
continue
print(f"Running in parallel: {ready}")
in_progress.update(ready)
# Run all ready tasks in parallel
task_coros = {name: self.steps[name].fn(self.results) for name in ready}
done = await asyncio.gather(*task_coros.values(), return_exceptions=True)
for name, result in zip(task_coros.keys(), done):
self.results[name] = result
completed.add(name)
in_progress.discard(name)
return self.results
# Define task graph:
planner = ParallelTaskPlanner([
# These 3 run in parallel (no dependencies):
TaskStep("fetch_user", fn=lambda r: fetch_user("user_123")),
TaskStep("fetch_catalog", fn=lambda r: fetch_catalog()),
TaskStep("fetch_promotions", fn=lambda r: fetch_promotions()),
# This runs after fetch_user AND fetch_catalog:
TaskStep("build_recommendations",
depends_on=["fetch_user", "fetch_catalog"],
fn=lambda r: build_recs(r["fetch_user"], r["fetch_catalog"])),
# This runs after everything:
TaskStep("generate_report",
depends_on=["build_recommendations", "fetch_promotions"],
fn=lambda r: generate_report(r["build_recommendations"], r["fetch_promotions"]))
])
results = await planner.run()
Option 5: System prompt — instruct model to request tools in parallel
System prompt:
"Tool use efficiency rules:
When you need multiple pieces of information that are independent of each other,
request ALL of them in a SINGLE response using multiple tool calls.
CORRECT — request all at once:
[tool: fetch_user(user_id="123")]
[tool: fetch_orders(user_id="123")]
[tool: fetch_catalog()]
→ These run in parallel. Task completes 3× faster.
WRONG — request one at a time:
[tool: fetch_user(user_id="123")]
(wait for result)
[tool: fetch_orders(user_id="123")]
(wait for result)
→ Sequential. Each request waits for the previous one.
Rule: If step B does not use step A's output, request A and B simultaneously.
Identify all information you need upfront and request it in one batch."
Option 6: Performance profiling — detect sequential bottlenecks
import asyncio
import time
import functools
from collections import defaultdict
class ExecutionProfiler:
"""Track tool call timing to identify sequential bottlenecks"""
def __init__(self):
self._calls: list[dict] = []
self._active: dict[str, float] = {}
def start(self, name: str):
self._active[name] = time.monotonic()
def end(self, name: str, result_size: int = 0):
if name in self._active:
elapsed = time.monotonic() - self._active.pop(name)
self._calls.append({
"name": name,
"duration": elapsed,
"result_size": result_size,
"ended_at": time.monotonic()
})
def report(self) -> dict:
if not self._calls:
return {}
total_wall_time = max(c["ended_at"] for c in self._calls) - min(
c["ended_at"] - c["duration"] for c in self._calls
)
total_work_time = sum(c["duration"] for c in self._calls)
parallelization_ratio = total_work_time / total_wall_time if total_wall_time > 0 else 1
return {
"total_calls": len(self._calls),
"wall_time_seconds": round(total_wall_time, 2),
"total_work_seconds": round(total_work_time, 2),
"parallelization_ratio": round(parallelization_ratio, 2),
"efficiency": f"{(total_work_time/total_wall_time*100):.0f}% parallel" if parallelization_ratio > 1 else "100% sequential",
"slowest_calls": sorted(self._calls, key=lambda x: -x["duration"])[:3],
"recommendation": (
"Good parallelization" if parallelization_ratio > 1.5
else "Consider parallelizing independent calls — significant sequential overhead detected"
)
}
profiler = ExecutionProfiler()
def profiled(fn):
@functools.wraps(fn)
async def wrapper(*args, **kwargs):
profiler.start(fn.__name__)
try:
result = await fn(*args, **kwargs)
profiler.end(fn.__name__, result_size=len(str(result)))
return result
except Exception:
profiler.end(fn.__name__)
raise
return wrapper
@profiled
async def fetch_user(user_id: str): ...
@profiled
async def fetch_orders(user_id: str): ...
# After task:
report = profiler.report()
print(f"Efficiency: {report['efficiency']}")
print(f"Wall time: {report['wall_time_seconds']}s vs {report['total_work_seconds']}s total work")
# If parallelization_ratio ≈ 1.0 → fully sequential, needs optimization
# If parallelization_ratio ≈ N → N calls ran in parallel (good)
Sequential vs Parallel Timing
| Scenario | Sequential | Parallel (10) | Speedup |
|---|---|---|---|
| 5 API calls × 2s | 10s | 2s | 5× |
| 100 items × 500ms | 50s | 5s | 10× |
| 1000 DB queries × 10ms | 10s | ~1s | 10× |
| 3 LLM calls × 8s | 24s | 8s | 3× |
| Mixed (dep chain of 3) | 15s | ~7s | 2× |
Expected Token Savings
10s sequential task → user timeout → retry with “be faster” prompt → re-explanation: ~8,000 tokens 2s parallel task → user sees result immediately: 0 impatience overhead
Environment
- Any agent making multiple independent I/O calls; critical for agents doing data aggregation, batch processing, report generation, or multi-source research
- Source: direct experience; sequential execution of parallelizable work is the most common avoidable performance bottleneck in agent pipelines
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