From Prototype to Production: Engineering Dependable AI Agents in 2026

Description and use

<p>AI agents are becoming an important part of modern software engineering. They can interact with APIs, retrieve information, execute multi-step workflows, and assist with complex tasks that previously required significant manual effort. However, developing an agent that performs well in a controlled demonstration is very different from deploying one that operates reliably in production.</p> <p>Real-world environments introduce unpredictable inputs, external service failures, incomplete data, security risks, and changing business requirements. Developers must therefore treat AI agents as soft

Problem

<p>AI agents are becoming an important part of modern software engineering. They can interact with APIs, retrieve information, execute multi-step workflows, and assist with complex tasks that previously required significant manual effort. However, developing an agent that performs well in a controlled demonstration is very different from deploying one that operates reliably in production.</p> <p>Real-world environments introduce unpredictable inputs, external service failures, incomplete data, security risks, and changing business requirements. Developers must therefore treat AI agents as soft

Outcome

<p>AI agents are becoming an important part of modern software engineering. They can interact with APIs, retrieve information, execute multi-step workflows, and assist with complex tasks that previously required significant manual effort. However, developing an agent that performs well in a controlled demonstration is very different from deploying one that operates reliably in production.</p> <p>Real-world environments introduce unpredictable inputs, external service failures, incomplete data, security risks, and changing business requirements. Developers must therefore treat AI agents as soft

Evidence

From Prototype to Production: Engineering Dependable AI Agents in 2026

Source