Why AI Agents Need Real Software Architecture? - Week 5 Research Lab Update (Mid March 2026)

In this week’s task, we tasked the researchers with creating architecture diagrams to visualize their solutions. An architecture diagram is visual representation of the key components, processes, inputs, and outputs involved in a system, and how they interact end-to-end. From the moment you press run or trigger a workflow, what happens?

The most popular AI agents have several different workflows and processes wound up into a single execution which makes the need for effective software architecture and design even more important. Being able to understand how these workflows interact is key to ensuring consistent and predictable performance whilst reducing the likelihood of an unexpected errors.

How To Think About Architecture?

Purpose over Exhaustiveness

You’ll never model every outcome. Design the agent around a sharp and minimal purpose and let everything else be ‘best effort’ as bad as it sounds.

Break it Down

Think about small orthogonal capabilities (skills, tools, and memories) that can be recombined, rather one giant, bespoke workflow.

Policies not Scripts

Swap out hard-coded examples and step-by—step flow examples for policies and guardrails that shape how the agent behaves under uncertainty.

Progressive Complexity

As they say, “Rome wasn’t built in a day”! Start with the thinnest viable agent that reliably serves the core use case, then evolve the architecture in response to real-time needs for improvement instead of imagined edge cases.

This is also incredibly relevant to developers, researchers and programmers who are trying to build the perfect agent from the first prompt. It won’t work. Start basic and build up.