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2026
Recheck the permission boundary when the model changes
OpenAI has put GPT-6 Astra at its Critical cybersecurity capability threshold. If you're moving to a model that much more capable, check the identity, tools and approvals around it before you treat it as a drop-in swap.
Coding agents are changing the economics of technical debt
Some migrations sit in the backlog because doing them by hand costs more than they seem worth. Parallel coding agents can change that maths, although only in a repo with clear conventions, tests you can trust and a definition of done a reviewer can actually check.
What I changed in agent memory
I now keep an agent's working context separate from durable records and searchable history. I changed it after watching old material get pulled into sessions where it was technically related but no use for the task the agent was actually doing.
A task-board export needs an experiment index
A task board can show that work moved and still lose the experiment behind it. I'd add an index that links each test to its question, evidence and result, so later work can tell what's safe to reuse.
Write the handoff before clearing the agent context
Before I end an agent session, I write down where the work actually stands and the command that should run next. Otherwise the next session has to piece it all back together and might redo changes that are already done.
A score needs a methodology readers can inspect
If your software gives an organisation a capability score, the person reading it needs to see what was measured and which version of the rules produced it. I'd keep that explanation right next to the scoring logic so it changes when the method does.
Write the evaluation set while the AI feature is still moving
Teams tend to leave evaluation until the prompt and interface feel finished. I'd rather collect the hard cases during development, because they show whether a change fixes the task or just tidies up the latest demo.
Govern the whole agent system
When I review an agent I trace what it can read, what it can change and how a person can stop or reverse it, which means looking at the harness and credentials as well as the model.
What embedded AI delivery uncovers in the workflow
Sit with the team actually using an AI system and you'll find things the brief never mentioned: fields nobody trusts, exceptions handled outside the software and approvals nobody wrote down.
2025
2024
Designing AI products around actions and state
Gemini 2.0 is being pitched around native tool use and agent-style tasks. If you're building in that direction, the product has to show what the model is doing, which action is waiting and what the user can still stop.
What I am testing with o1-preview
OpenAI's o1-preview spends more time reasoning before it answers. I want to test it on tasks where I can check the result without trusting the model, because the extra reasoning only helps if the answer holds up.
AI governance has entered the product backlog
The EU AI Act is now in force, with obligations arriving in stages. If you build product, I'd start by writing down where AI is actually used and who's responsible for each use.
Using voice and images in an existing workflow
GPT-4o puts text, images, audio and live video into one interaction. I'm more interested in the ordinary workflow side of that: what gets quicker to show, what still needs confirming, and what you need to keep.
When I would test an open-weight model
Llama 3 is worth putting into a real product evaluation. I'd run it against a hosted model on the same cases, then add up the hardware and the work of keeping it running.
How I decide whether an AI idea deserves a build
A convincing demo is easy to mistake for a product. I want to know who'd notice a wrong result, what they could check it against and whether the time saved is still there once you count the review work.
What applied AI means in my work
For me, applied AI starts with a piece of work that already exists. Before I pick a model or build a screen, I want to know the decision, what information is there, what delay costs and what happens when the answer's wrong.