标签 #build-vs-buy
6 篇文章。
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· Paul Lukic
AI Agents Cause Outages? Fix the Input, Not Just the Model
Engineers report ~17 hours a week debugging AI-generated code while median productivity gains sit near 7.7%. The bottleneck is context retrieval, not model quality.
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· Paul Lukic
McKinsey: 30% of Firms Lose Productivity With Agentic AI. Why?
Agentic AI spend is climbing while output flatlines. The usual culprit isn't the model — it's how much irrelevant code the agent reads before it does anything useful.
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· Paul Lukic
Agentic Coding is Here. Are You Ready for the Token Bill?
Autonomous coding agents multiply engineering output and API spend at the same time. Most of that spend is context waste, and a code graph cuts it by two thirds.
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· Paul Lukic
AI Tool Sprawl: Your Devs Are Drowning in Inefficient Agents
Running five AI coding tools doesn't give you five times the leverage. It gives you five agents grepping the same repo, and a token bill that quietly compounds.
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· Paul Lukic
Build vs. Buy Code Intelligence: The Real ROI
Building in-house code graphs costs $250k+ and 6 months. Adopting Coograph cuts AI spend 80% while freeing engineers to ship product.
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· Paul Lukic
MCP Server：让 AI 编程 agent 规模化，而不浪费
MCP server 把 AI agent 读取代码库的方式标准化。在一个代表性任务上，基于代码图谱的上下文把 token 砍掉约 80%、工具调用减少约 4 倍——本文讲清楚为什么这很重要，以及该如何思考自建还是采购。