Building AI Agents in 2026: Templates, Evaluation, and Production Lessons

Two years ago, building an AI agent meant assembling your own orchestration layer, writing prompt templates by hand, and praying your tool calling worked. Today in 2026, it's a commodity. I've shipped 12 production agents in the last 8 months. Here's what I learned about templates, evaluation, and avoiding expensive mistakes. Build vs. Use a Template The question isn't "Can I build an agent?" It's "Should I?" I built from scratch for: Custom domain logic Unique tool integrations Proprietary evaluation criteria I used templates for: Retrieval-augmented generation (RAG) Customer support agents Internal documentation assistants Lead qualification chatbots AgentKit saved me 16 hours on my fifth agent. It includes prompt templates, tool calling scaffolding, evaluation harness, and deployment configs. Evaluating Agent Quality I evaluate every agent across four dimensions: 1. Accuracy — Benchmark against gold-standard answers, measure semantic similarity 2. Latency — p50, p95, p99 response ti