Controlled AI-assisted delivery, DevSecOps and compliance engineering, written from inside environments where somebody eventually has to answer for the outcome. 84 posts and counting.
Microsoft has shipped an AI tool that reads your Salesforce estate and tells you what to move, change and redesign. That is aimed squarely at the one thing that has always kept customers in place: nobody knows what they have built. A look at the numbers behind the incumbent, the footnotes in the earnings release, and why this fight is different.
Plenty of firms will audit your pipelines and tell you your release process is a liability, while running build pipelines of their own that would not survive the same review. So here are ours with the actual numbers: 21 deployments averaging 82 lines, 29 shared templates, 14 gates that fail the build, and the one that sat written but unwired for weeks.
Merging Salesforce security metadata does not resolve the disagreement between two branches. It just makes the disagreement happen automatically, on the way to production, with nobody watching. Keep an approved Golden Set in its own branch, overwrite the promotion branch before Salesforce sees it, and there is nothing left to merge.
A good AWS reference architecture will find you the passage that mentions Acme. It will not tell you whether three of those documents are about the same Acme, which one is out of date, or that two of them disagree. Finding something is not the same as knowing something, and the gap between them is where the next few years get interesting.
Salesforce did not raise margin guidance this year because it is covering token spend. That is the first visible instance of a large, sophisticated customer looking at the invoice and asking whether the frontier model was worth it. Behind it sits a 3 to 5 trillion dollar financing requirement and a lot of buildings about to switch on.
Your organisation holds a huge amount of knowledge. That is not the same as your AI being able to consume it. Information survives text extraction while meaning does not, and most retrieval failures are created upstream, at the moment a document became a pile of characters. Why the real transition is documents to knowledge, not documents to AI.
Everyone is wrapping an existing API catalogue in MCP and calling it an agent platform. Anthropic's own figures put a large tool surface at 77K tokens before the model does any work, and tool-selection accuracy drops with it. Integration and agent capability are different design problems, and they should not have the same owner.
Twelve months building Scout taught us that answering a question and knowing whether you can answer it are two very different problems - and only one of them is the actual agent problem. Here is what we built, what we got wrong, and the one thing left to solve.
Change management keeps getting reduced to spreadsheets and impact assessments written before anything is built. Real change management is a human exercise - it starts with how the work actually changes for the people who have to use the system.
After 3 years of GAR we've seen AI everything - horror stories and fairytales in every news article, so where are we heading with LLMs and AIs?
MCP introduced the ability to give Agents tools and data; A2A gives Agents the ability to use other Agents - interconnectivity is a known driver for innovation, so what does a network for Agents look like? Coming soon to a future near you!
With Atlas, Salesforce have driven architecture into Agents - that's critically important for performance and cost. Atlas is not another agent workflow; it's a mature and well-considered architecture that we all can use.
