Eight reads on building software in the age of AI agents: from designing multi-agent teams and rethinking mobile architecture to securing tool calls, creating agent-ready applications, and building better developer tools. A mix of original articles, a research guide and one video.
AI engineering
Andrew Brogdon · The Flutter Blog · 20 August 2026
Andrew Brogdon describes a practical way to split software work between specialised agents: an architect writes the blueprint, a tester creates failing tests, a coder implements the solution, and a coordinator manages verification and commits. The strongest lesson is not simply to add more agents; it is to give each one clear responsibilities and limits. Particularly useful for teams experimenting with agentic development in Dart, Flutter or any codebase where reviewability matters.
Developer tools · Video
IBM Technology · Video
This explainer looks at how coding agents gather context about a project and use developer tools to move beyond autocomplete. Watch it with a practical question in mind: what information does an agent need to understand your repository, and which tools let it inspect, change and validate the system safely? It is a useful primer for developers deciding how much autonomy to give an agent.
Mobile architecture
Mark Kazakov · Original post on daily.dev · 15 May 2026
Does AI-generated code make architecture less important, or more? Mark Kazakov argues that agents still need boundaries, conventions and clear data flows, while warning that they can also produce layers of boilerplate that are expensive for humans to understand. The useful middle ground is architecture that makes a codebase easy for both people and agents to navigate, review and extend.
Flutter · AI development
Michael Thomsen · Flutter Blog · 20 May 2026
This piece makes the case that Flutter’s shared codebase is a useful fit for AI-assisted development: agents work against one implementation, Dart’s type system helps catch mistakes, and hot reload shortens the feedback loop. It is worth reading if you are weighing Flutter against separate native codebases for a product that will run on multiple platforms. Treat the argument as a framework advantage to evaluate, not a guarantee that agents will write correct code.
AI security · Research
Cloud Security Alliance · Research artifact · 22 June 2026
Agent systems introduce more than a model call: they have identities, tools, permissions, execution environments and control paths that need to be governed. This Cloud Security Alliance architecture guide is a useful reference for mapping those pieces and thinking about where policy and oversight belong. It is aimed at developers and architects designing systems that need to be secure beyond a proof of concept.
Firebase · Security
Alexander Nohe · Firebase Blog · 2 December 2025
Giving an agent tools means giving it the ability to act, not just answer. This Firebase article is a practical reminder to secure the boundary between the model and the services it can invoke: validate requests, enforce permissions outside the prompt, and avoid treating model output as trusted input. It is an older but still relevant security read for anyone connecting an assistant to application data or actions.
Product engineering
Hassan Djirdeh · Telerik · 18 August 2026
Applications are increasingly used by people, AI assistants acting on their behalf, and autonomous agents. This article explores what that shift means for full-stack developers and the interfaces and capabilities an application may need to expose. A useful prompt for product teams: are your workflows designed only for a person clicking through screens, or can an authorised assistant complete them safely too?
Developer productivity
Dre Solorzano · Atomic Object · 21 August 2026
AI can help developers build tools that remove friction from their own workflows, not just ship end-user features. This article is a useful reminder to look for repetitive tasks in the development process that could be made easier with a small, focused tool. The best opportunities are often specific: automate a tedious check, make project knowledge easier to find, or shorten a feedback loop rather than adding another general-purpose assistant.
How we pick: each week we go through the developer feeds we follow, read the originals, and keep the few a developer in Kenya would want to know about. Every link goes to the author’s own page.
