Topic
Long-Running & Asynchronous Agent Workflows
12 posts on long-running & asynchronous agent workflows — part of ai agents & automation on the n4n AI blog.
Why long-running agents need idempotent tool calls
Long-running AI agents face retries and replays. Learn why idempotent agent tool calls are mandatory for reliability and how to implement them safely.
Sync vs async agent architectures: a practical comparison
A practitioner's head-to-head comparison of sync vs async agent architecture across latency, cost, ergonomics, and limits, with a clear verdict for each use case.
Queue-based agent pipelines: Celery, Redis, and LLM calls
Build a resilient queue based agent pipeline with Celery, Redis, and LLM calls. Step-by-step tutorial with runnable code and retry patterns.
Polling vs webhooks for async agent completion
A practical engineering comparison of polling vs webhooks agents for async completion: latency, cost, ergonomics, limits, and which to use per use case.
How to run multi-hour research agents without timing out
Build resilient multi-hour research agents: avoid long running research agent timeout with async queues, state checkpointing, and LLM provider fallback.
Designing agent workflows that survive a server restart
A practical guide to designing durable agent workflows that survive server restarts via state machines, checkpointing, and idempotent steps.
Building a job queue for LLM agent tasks with Postgres
Step-by-step tutorial: build a Postgres-backed job queue for LLM agent tasks in Python with SKIP LOCKED, retries, and concurrent workers.
What are asynchronous AI agents, and when do you need them
Asynchronous AI agents run LLM-driven tasks without blocking callers, enabling long-running workflows. This explainer details architecture, use cases, and pitfalls.
How to build long-running agents with Temporal and LLMs
Step-by-step guide to building temporal long-running agents with LLMs: durable workflows, human signals, resilient inference fallback, Python.
Background agents: OpenAI's approach vs custom orchestration
Compare OpenAI's managed background agents with custom orchestration across capabilities, cost, latency, and ergonomics to decide which fits your async AI workload.
Async agent orchestration: lessons from distributed systems
Practical lessons for building resilient async agent orchestration by applying distributed systems patterns: durable queues, state machines, and backpressure
5 patterns for retrying failed steps in async agent chains
Practical retry patterns async agents require: exponential backoff, idempotent steps, checkpointing, provider fallback, and dead-letter queues.
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