🎉🚀 Ah, yes, another riveting read from the ivory towers of academia: "Tokenomics"—where the only thing more baffling than the title is the idea that someone might actually care about the token usage in "Agentic Software Engineering." 🤯 Spoiler alert: still no cure for #insomnia. 💤
https://arxiv.org/abs/2601.14470 #Tokenomics #AgenticSoftwareEngineering #Academia #TechHumor #HackerNews #ngated
Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering

LLM-based Multi-Agent (LLM-MA) systems are increasingly applied to automate complex software engineering tasks such as requirements engineering, code generation, and testing. However, their operational efficiency and resource consumption remain poorly understood, hindering practical adoption due to unpredictable costs and environmental impact. To address this, we conduct an analysis of token consumption patterns in an LLM-MA system within the Software Development Life Cycle (SDLC), aiming to understand where tokens are consumed across distinct software engineering activities. We analyze execution traces from 30 software development tasks performed by the ChatDev framework using a GPT-5 reasoning model, mapping its internal phases to distinct development stages (Design, Coding, Code Completion, Code Review, Testing, and Documentation) to create a standardized evaluation framework. We then quantify and compare token distribution (input, output, reasoning) across these stages. Our preliminary findings show that the iterative Code Review stage accounts for the majority of token consumption for an average of 59.4% of tokens. Furthermore, we observe that input tokens consistently constitute the largest share of consumption for an average of 53.9%, providing empirical evidence for potentially significant inefficiencies in agentic collaboration. Our results suggest that the primary cost of agentic software engineering lies not in initial code generation but in automated refinement and verification. Our novel methodology can help practitioners predict expenses and optimize workflows, and it directs future research toward developing more token-efficient agent collaboration protocols.

arXiv.org

...dislodging the ball from the most tricky places ever, mulling over the placement and probabilistic interaction of bumpers to hopefully achieve a better output, while losing the skill to roll yourself. Have fun aligning humans and agents with products and the overall organisation, while keeping the economic and systemic perspective in your view.

"Fun" times trying to bring the Engineering to #AgenticSoftwareEngineering and anything resembling #sustainability, #autonomy or #GreenComputing.

Ein KI-Agent erzeugt in wenigen Minuten Tausende Zeilen Code. Niemand kann das vollständig lesen. Trotzdem trägt jemand die Verantwortung dafür.

Torben Keller zeigt in seinem neuen Blogpost, dass dieses Spannungsfeld nicht neu ist. In Brownfield-Projekten übernehmen Teams seit jeher Ownership, ohne jede Zeile zu kennen.

📖 Jetzt lesen: https://www.innoq.com/de/blog/2026/04/vom-vibe-coder-zum-agentic-engineer/

#AgenticSoftwareEngineering #Softwareentwicklung

Agentic Software Engineering - The Future of Code

A comprehensive guide to Agentic Software Engineering. Learn how AI agents are transforming the software development lifecycle.