Liu ZhuoQi

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.”