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The article shows how real-world agentic AI deployments can blow through budgets because multi-step workflows use 5–30× more tokens per task than simple chatbots. It breaks down four hidden cost layers—LLM inference with re-sent context, context rot, tool orchestration, and infrastructure—and offers strategies to curb runaway spending before your production bill arrives.
+ agentic-ai
+ token-economics
+ context-management
+ ai-costs
inference-efficiency
+ tldr-a-byte-sized-daily-tech-newsletter
GPT-5.5 outperforms GPT-5.4 in real-world coding tasks, from debugging and large merge operations to interactive app development. It also serves as a research partner—critiquing manuscripts, proposing analyses, and generating reports on complex datasets—all while running at GPT-5.4 latency through integrated inference optimizations.