Building AI Agents in 2026: Templates, Evaluation, and Production Lessons
This article discusses the evolution of building AI agents, highlighting the benefits of using templates versus building from scratch, and sharing lessons on evaluating and deploying AI agents in production.
Why it matters
This article provides practical guidance on building and deploying production-ready AI agents, which is crucial as AI becomes more widely adopted across industries.
Key Points
- 1The decision is whether to build from scratch or use a template, depending on the use case
- 2Key evaluation metrics include accuracy, latency, cost, and safety
- 3Common pitfalls include using the wrong model, prompt injection, hallucination at scale, and runaway costs
Details
The article outlines how building AI agents has become a commodity, with templates and tools like AgentKit saving significant time compared to building from scratch. The author shares their experience of shipping 12 production AI agents in 8 months, highlighting when to build versus use a template. Building from scratch is best for custom domain logic, unique tool integrations, and proprietary evaluation, while templates work well for standard use cases like retrieval-augmented generation, customer support, and lead qualification. The author also shares their 4-part evaluation framework (accuracy, latency, cost, safety) and common pitfalls to avoid, such as using the wrong model, prompt injection vulnerabilities, hallucination at scale, and runaway costs. The article concludes that AI agents are now solved problems for many use cases, and the key decision is whether to template or customize.
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