How Building AI Agents Has Changed in 2026

In recent years, the process of building AI agents has shifted significantly from requiring extensive infrastructure setup to a more streamlined approach due to advancements in software development kits (SDKs) and integrated tools. Previously, developers had to manually set up complex retrieval-augmented generation (RAG) pipelines and write significant amounts of custom code to enable agents to perform their tasks. However, the introduction of built-in tools like the Claude Agent SDK and OpenAI’s Codex SDK has simplified the process by providing essential functionalities such as file handling, shell commands, and web interactions out of the box. This change has reduced the need for middle-layer infrastructure, allowing developers to focus more on the agent's core capabilities rather than on setting up the environment. The shift has also led to the adoption of a skills-based approach, where agents load tools only when needed, resulting in more efficient use of resources. While traditional frameworks are still relevant for specific use cases such as multi-agent orchestration or deterministic typing, starting with an SDK is generally recommended for most projects, as it often covers all necessary functionalities without the overhead of a full framework. This transformation aligns with existing infrastructure practices, emphasizing integrated tools and governed actions, and allows developers to build more efficient and focused AI agents.