I don’t tend to post links to videos here, as I can’t stand watching videos to learn about things. But some talks are worth a watch, and I do suggest this overview on how organizations are currently using AI by Laura Tacho. There’s various nuggets of data from her work with DX:
These are interesting numbers, but most of them are averages, and those who know me know I teach people to be suspicious of averages. Laura knows this too:
average doesn’t mean typical.. there is no typical experience with AI
Different companies (and teams within companies) are having very different experiences. Often AI is an amplifier to an organization’s practices, for good or ill.
Organizational performance is multidimensional, and these organizations are just going off into different extremes based on what they were doing before. AI is an accelerator, it’s a multiplier, and it is moving organizations off in different directions. (08:52)
Some organizations are facing twice as many customer incidents, but others are facing half.
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Rachel Laycock (Thoughtworks CTO) shares her reflections on our recent Future of Software Engineering retreat in Utah.
On the latter:
One of the most interesting and perhaps immediately applicable ideas was the concept of an ‘agent subconscious’, in which agents are informed by a comprehensive knowledge graph of post mortems and incident data. This particularly excites me because I’ve seen many production issues solved by the latent knowledge of those in leadership positions. The constant challenge comes from what happens when those people aren’t available or involved.
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Simon Willison (one of my most reliable sources for information about LLMs and programming) is starting a series of Agentic Engineering Patterns:
I think of vibe coding using its original definition of coding where you pay no attention to the code at all, which today is often associated with non-programmers using LLMs to write code.
Agentic Engineering represents the other end of the scale: professional software engineers using coding agents to improve and accelerate their work by amplifying their existing expertise.
He’s intending this to be closer to evergreen material, as opposed to the day-to-day writing he does (extremely well) on his blog.
One of the first patterns is Red/Green TDD
This turns out to be a fantastic fit for coding agents. A significant risk with coding agents is that they might write code that doesn’t work, or build code that is unnecessary and never gets used, or both.
Test-first development helps protect against both of these common mistakes, and also ensures a robust automated test suite that protects against future regressions.
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Aaron Erickson is one of those technologists with good judgment who I listen to a lot
As much fun as people are having with OpenClaw, I think the days of “here is my agent with access to all my stuff” are numbered.
Fine scoped agents who can read email and cleanse it before it reaches the agentic OODA loop that acts on it, policy agents (a claw with a job called “VP of NO” to money being spent)
You structure your agents like you would a company. Insert friction where you want decisions to be slow and the cost of being wrong is high, reduce friction where you want decisions to be fast and the cost of being wrong is trivial or zero.
I’ve posted here a lot about security concerns with agents. Right now I think this notion of fine-scoped agents is the most promising direction. Last year Korny Sietsma wrote about how to mitigate agentic AI security risks. His advice included to split the tasks, so that no agent has access to all parts of the Lethal Trifecta:
This approach is an application of a more general security habit: follow the Principle of Least Privilege. Splitting the work, and giving each sub-task a minimum of privilege, reduces the scope for a rogue LLM to cause problems, just as we would do when working with corruptible humans.
This is not only more secure, it is also increasingly a way people are encouraged to work. It’s too big a topic to cover here, but it’s a good idea to split LLM work into small stages, as the LLM works much better when its context isn’t too big. Dividing your tasks into “Think, Research, Plan, Act” keeps context down, especially if “Act” can be chunked into a number of small independent and testable chunks.
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Doonesbury outlines the opportunity for aging writers like myself. (Currently I’m still writing my words the old fashioned way.)
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An interesting story someone told me. They were at a swimming pool with their child, she looked at a photo on a poster advertising an event there and said “that’s AI”. Initially the parents didn’t think it was, but looking carefully spotted a tell-tale six fingers. They concluded that fresher biological neural networks are being trained to quickly recognize AI.
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I carefully curate my social media streams, following only feeds where I can control whose posts are picked up. In times gone by, editors of newspapers and magazines would do a similar job. But many users of social media are faced with a tsunami of stuff, much of it ugly, and don’t have to tools to control it.
A few days ago I saw an Instagram reel of a young woman talking about how she had been raped six years ago, struggled with thoughts of suicide afterwards, but managed to rebuild her life again. Among the comments – the majority of which were from men – were things like “Well at least you had some”, “No way, she’s unrapeable”, “Hope you didn’t talk this much when it happened”, “Bro could have picked a better option.” Reading those comments, which had thousands of likes and many boys agreeing with them, made me feel sick.
My tendencies are to free speech, and I try not to be a Free Speech Poseur, but the deluge of ugly material on the internet isn’t getting any better. The people running these platforms seem to be “tackling” this problem by putting their heads in the sand and hoping it won’t hurt them. It is hurting their users.
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