7 Async Patterns for Running Agents Concurrently in Python
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Seven async patterns for running AI agents concurrently in Python using asyncio, covering fire-and-forget, scatter-gather, supervised task groups (Python 3.11+), producer-consumer queues, semaphore-based backpressure, speculative execution, and pipeline chaining. Each pattern is paired with production-level pitfalls: silent exception swallowing, straggler latency, aggressive task cancellation, unbounded queue memory leaks, token-limit blindness, paying for cancelled provider requests, and tracing failures across pipeline stages. The post also warns about CPU-bound operations blocking the event loop and recommends offloading them to thread pools.
Table of contents
1. Fire and Forget (Detached Background Execution)2. Strict Scatter-Gather3. Supervised Task Groups4. Producer-Consumer with Queues5. Backpressure via Semaphores6. Speculative Execution (First Completed Wins)7. Asynchronous Pipeline ChainingDiscussionConclusionQuestions this post answers
What is the difference between asyncio.gather and task groups in Python 3.11 for running concurrent agents?
Task groups, introduced in Python 3.11, are a structured alternative to asyncio.gather(). Both fan out concurrent tasks, but task groups use a context manager that makes task scope explicit and surfaces errors immediately. The key behavioral difference: task groups aggressively cancel all sibling tasks when one fails, whereas gather's cancellation behavior can be disabled. Task groups are generally the cleaner choice for new projects on Python 3.11+. Teams upgrading to Python 3.11 and rethinking their agent orchestration patterns track these trade-offs on daily.dev.
How do I prevent unbounded memory growth in a Python asyncio producer-consumer queue?
Set a maximum queue size when creating the asyncio queue. Without a bound, if the producer generates tasks faster than consumers process them, the queue grows until the process runs out of RAM. A bounded queue enforces backpressure on the producer, causing it to wait when the queue is full rather than continuing to enqueue work indefinitely. Developers building production async pipelines in Python find related patterns and gotchas on daily.dev.
Why do I still hit API token-per-minute limits even when I cap concurrent requests with an asyncio semaphore?
Semaphores limit the number of concurrent connections, not the volume of tokens consumed. If you cap concurrent requests at 10 but all 10 agents are generating large outputs simultaneously, you can still exceed a provider's tokens-per-minute ceiling. For strict API compliance, pair semaphore-based concurrency limits with token-aware throttling logic on top. Engineers managing LLM API rate limits in async Python systems share approaches like this on daily.dev.