14. Observability in AI Systems – How to Know What Your AI Is Actually Doing
Track logs, metrics, and costs in AI systems to detect failures, improve reliability, and build production-ready observability. Continue reading on Medium »
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GraphQL Is the Native Language of AI Agents
Your APIs were designed for humans. That’s about to be a problem. When Facebook’s engineering team designed GraphQL in 2012, they were solving a mobile problem: REST endpoints were returning too much data over slow networks, and iOS clients were paying the cost in latency. The solution — let the client declare exactly what it needs, enforce that contract through a typed schema, and expose everything about the API through introspection — turned out to solve a different problem entirely, one Facebook couldn’t have anticipated. Twelve years later, the most constrained consumer of your API isn’t a mobile client on a 3G network. It’s an AI agent with a finite context window. The constraint is different, but the logic is identical. Every field your API returns that an agent doesn’t need is a was
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Your APIs were designed for humans. That’s about to be a problem. When Facebook’s engineering team designed GraphQL in 2012, they were solving a mobile problem: REST endpoints were returning too much data over slow networks, and iOS clients were paying the cost in latency. The solution — let the client declare exactly what it needs, enforce that contract through a typed schema, and expose everything about the API through introspection — turned out to solve a different problem entirely, one Facebook couldn’t have anticipated. Twelve years later, the most constrained consumer of your API isn’t a mobile client on a 3G network. It’s an AI agent with a finite context window. The constraint is different, but the logic is identical. Every field your API returns that an agent doesn’t need is a was
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