Background: The Cost Problem in Agent Tool Calling # In traditional agent tool-calling, every tool invocation requires a full cycle of “model inference → tool execution → result return → model re-inference.” This seemingly natural loop breaks down at scale in three ways:
Context Pollution: Every tool result is injected verbatim into the context window. Fetch expense reports for 20 employees, and 2,000+ line items enter context — even though you only need to know “which 3 people exceeded their budget.” Inference Overhead: Each tool call demands a full model inference pass. Five tools = five inference passes, each costing hundreds of milliseconds to seconds. Noise Degrades Accuracy: When the context window is packed with intermediate results, the model must find signal in noise. Context Rot research shows LLM performance on complex tasks drops 50-70% as context grows. As Florian Bruniaux puts it in the Claude Code Architecture Guide: “The Outer Loop — everything outside the model: context management, tool invocation, verification, memory consolidation — increasingly determines system quality more than model inference itself.”
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.
In May 2026, the Chinese AI community went wild over a wave of ChatGPT Business discounts: £11/month for 2 seats in the UK, $20 in the US, AU$25 in Australia, locked in for 48 months. Mysterious codes like codestonegb, thealloynetwork, and firstfocus spread across forums and blogs at breakneck speed.
One question nobody was asking: where did these codes actually come from?
I spent several days cross-referencing sources across five platforms and three languages. The answer is messier—and more interesting—than “they leaked on linux.do.”
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.
This is not AI科普. This is a cross-validated research sprint backed by 67 primary sources — vendor docs, arXiv papers, and researcher interviews — on a question every Agent builder hits: why don’t LLMs remember anything?
→ Full report: 14-product comparison table, 9 engineering takeaways, 3-year paradigm roadmap
The One-Liner # Four independent constraints — O(n²) attention + KV cache VRAM + catastrophic forgetting + GDPR right-to-be-forgotten — stacked together leave “stateless” as the only viable engineering solution. Every “Memory” feature you’ve seen (ChatGPT, Claude, Cursor) is structured text injected into the system prompt. Zero weight modification. The next 1–3 years belong to stateless LLM kernels + stateful Agent memory layers.
Hugo shortcodes make it easy to embed live code demos. Here are three ways:
1. Inline CSS Demo (No External Service) # A spinning loader animation, right in the article:
Pure CSS Spinner
A gradient text animation:
Why Hugo # When picking a framework for a personal blog, my top criterion was low maintenance cost — I didn’t want to abandon writing three months later because of npm dependency hell.
Hugo is a single binary, requires no Node.js, builds thousands of posts in 1-2 seconds, and the Blowfish theme comes with dark mode, full-text search, multilingual support, RSS, Open Graph, and reading time estimates out of the box. Day-to-day writing only requires touching Markdown files.