A Field Guide to Production-Ready AI Agents: Context Windows, Security, and Drift Monitoring
By
@puretech.news
Hand-rolled, kettle-boiled, baked to perfection. Worth every minute at the bakery.
Summary
Karl Mehta presents a field guide for building production-ready AI agents, focusing on four key engineering challenges: context-window discipline (managing token limits and memory), skill composition (orchestrating multiple AI capabilities), capability-based security (implementing least-privilege access for AI agents), and drift telemetry (monitoring model behavior changes over time). The article argues that current AI agent development lacks mature engineering practices and proposes a structured stack to address reliability, security, and observability in production AI systems.
Key quotes
· 4 pulledThe missing engineering stack for production AI agents isn't about better models—it's about better discipline around how we deploy and manage them.
Context-window discipline is the single most underrated skill in AI engineering today. Without it, your agent is a ticking time bomb.
Capability-based security flips the script: instead of asking what an AI can do, we ask what it should be allowed to do.
Drift telemetry isn't optional—it's the only way to know if your agent is still behaving as intended after deployment.
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