What are the core components required to build a robust AI agent in 2026?

Aiden, a physical AI agent device built by the team at Aiden (aidenai.io), connects to a host computer as a standard USB HID peripheral, capturing the screen via HDMI and sending keyboard/mouse/touch inputs, so the host sees only a keyboard and mouse. The device, based on the Luckfox Pico Zero (RV1106) with a Go-based LLM agent runtime, sidesteps permissions, installs, and API negotiation for computer use agents. The discussion also highlights core components for building robust AI agents in 2026, including framework selection, system prompts, evaluations, and a shared behavioral test environment.

What basic parts are needed to build a strong and reliable AI agent? - You first need to select a framework and SDK , such as, lang chain, crewAI, vercel AI SDK, etc. - Build your system prompts - Run evaluations with system prompts plugged in. - Collect metrics and logs and understand your Agent behavior. - Repeat step 3 until you get expected results from your agent by fine tuning your prompt. Next steps, decide if RAG is required, adapter to be built or fine a model. This requires data to fine tune models. I can help with performance and reliability of your agent. I guess what’s usually being omitted is the action layer. Most frameworks treat tool use as an API call, then the agent sends a request, and gets a response. That works quite well for software tools, but it breaks down when the agent needs to interact with a device or an app for instance. My team actually has been building a physical AI agent device at Aiden aidenai.io http://aidenai.io that approaches this quite a bit differently. Instead of installing software on the host device or requiring API access, the device connects as a standard USB HID peripheral same protocol as a keyboard and mouse . It captures the screen via HDMI, processes full-duplex audio on-device, and sends keyboard/mouse/touch inputs back to the host. So, the host has no idea there’s an AI agent on the other end. It basically only sees a keyboard and a mouse. This sidesteps the biggest production friction for computer use agents: permissions, installs, and API negotiation. If a human can use the device, then the agent can use it too. It is built on Luckfox Pico Zero RV1106 with a Go-based LLM agent runtime. Full architecture at AidenAI-IO/aiden-hardware-demo | DeepWiki http://deepwiki.com/AidenAI-IO/aiden-hardware-demo , more that happy to discuss the design decisions if useful I would add one more component after evals/logs: a shared behavioral test environment. Unit tests and traces tell you whether the agent executed a path. A public or semi-public room tells you how it behaves around other agents, humans, incentives, interruptions, and messy context. That is where a lot of weird failure modes appear.