Data integration connects systems and moves data between them. Workflow orchestration coordinates what runs, in what order, and what happens when a step fails. The data integration vs workflow orchestration split is simple to state: connecting systems is not the same as coordinating the work that runs across them.
… The connectors serve different jobs. Integration uses them to move and reshape data. Orchestration uses them to trigger and coordinate tasks, then to recover when one fails. Same plugin to one system, two different reasons to call it.
This post draws the line between data integration and workflow orchestration, shows where the two overlap, and explains why one control plane across categories is becoming the way enterprises manage both. — Read More
Daily Archives: July 28, 2026
Where Should Your Company’s AI Brain Live?
I have been working with a lot of companies that are trying to become AI-first.
At first, that usually means helping individuals get much better at using AI.
Soon the AI knows that when someone says MRR, they do not just mean the generic finance definition.
… It is no longer just automation. It is institutional memory – the company’s knowledge, history, processes, workflows, and way it uniquely does work.
And it likely will be the new backbone and most significant software infrastructure in the AI era. … AI company brains will become the next version of indispensable software. — Read More
The Data Pyramid in Robotics
Frontier LLMs were pre-trained on trillions of tokens of text, most of it at least initially taken from the web. The internet was, in effect, a free pre-training corpus that humanity had collectively assembled.
In robotics, there’s no such equivalent. That generally means all attempts to solve and get to physical AGI in some form involve working through this idea of the data bottleneck. In this piece, I’ll go deeper into the bottleneck, and introduce a version of the robot data pyramid highlighting the various kinds of data used in robot learning that can be combined to help address it. — Read More
What systems thinking looks like for PM’ing AI products
Software is designed, but models are grown, not designed. As such, AI products are organic. PM’ing AI products has an organic nature, akin to growing a garden. It’s more about creating the system for your plants to grow in rather than specifying exactly how an individual plant will look like. (see Alex Komoroske’s Gardening Platforms about platform PM’ing, the same concept applies for all PMs now.)
This now makes systems thinking tablestakes for PMs.
Donella Meadows’ Thinking in Systems spelled out a few truths for applying systems thinking in building AI/agents in existing products. I only understood these better after living through building agent systems in a platform PM role. — Read More
How to structure a log
You’ve decided to step up your logging game and start sending more valuable, structured logs that you can query, aggregate, and use for debugging in production. Go, you!
…We’re not going to spend much time on what you should log. We’ve covered that already, a few times before.
…[W]e treat logs like real application data that we need to be able to search, filter, and aggregate to understand broad trends and debug specific incidents. — Read More
AI Is Profitable: The Real Question Is How Much
You’ve been told AI can’t make money. I’ve said it myself, and that thinking AI will make money one day was more a matter of faith than numbers.
But now, I can confidently say I would be wrong to keep thinking that, because something has changed.
Over the next months, we’re going to see markets adjusting to this new reality: AI is no longer about whether it can make money, but how much and by whom. — Read More
Prompt Caching In Agents
Large language models are often thought of like functions: send in some text, receive some text. That is a useful abstraction, but it ignores one of the most important parts of running a coding agent: most of the input is the same as last time. In other words we mostly append to it.
A coding agent sends the model its system prompt, tool definitions, project instructions, conversation history, tool calls, and tool results. On the next turn it sends almost all of that again, plus a small amount of new material. Once a session has grown to tens or hundreds of thousands of tokens, recomputing the whole prompt for every turn is slow and expensive.
Prompt caching is what makes this somewhat economic, but it is also quite fragile. A changed tool definition, a model switch or a provider routing decision can turn what one would expect to be a cheap incremental request into a full replay of the context. — Read More
More On An Internal OpenAI Model Hacking Into HuggingFace
We now have more details of what happened. Every time we learn more details, it somehow makes things seem worse.
… Amanda Long summarizes what we believe the model actually did, with over 17,000 complex actions coordinated over several days, including a self-migrating command-and-control and a wide array of decoys.
The WSJ’s Robert McMillan and Sam Schechner offer their own version. Mostly correct, but there is an important detail it is easy to get the wrong impression about from the way this is presented: The attack succeeded. GLM-5.2 did not prevent Galaxy from achieving its goals.
… Many things went wrong. We can start with the most obvious.
This was a failure of oversight, in the sense of a total lack of oversight. — Read More
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