Agent Architecture

Why We Moved from Celery to Temporal for Production Agent Pipelines

2026-05-16·1647 words· 8 min

Agent Architecture Agent Engineering Temporal Celery Workflow Engine Production Backend Python

In April 2026, we migrated seo-project’s task queue from Celery to Temporal. We dropped exactly one dependency (celery), wrote 11 new files (src/infrastructure/temporal/), and renamed our containers from api/worker/beat to api/temporal_worker_blue/green with blue-green deployment. The most common question afterward: why not just keep using Celery? If it’s already running, what’s the point? This article is the answer. It doesn’t come from documentation comparisons. It comes from production bugs we hit running Agent pipelines at scale.

RAG vs LLM Wiki vs Plain Text — A Decision Framework for Agent Long-Term Memory

2026-05-11·1234 words· 6 min

Agent Architecture AI Agent Memory RAG Context Engineering

Every Agent builder hits this question eventually: where do I store user data so the agent remembers it next session? Three approaches dominate the landscape: RAG (vector retrieval), LLM Wiki (structured knowledge injection), and plain-text context memory (the CLAUDE.md / Cursor Rules pattern). Each has vocal advocates. But picking wrong is expensive — do RAG too light and it’s a noise generator; do plain text too heavy and it’s a token incinerator.

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