#multi-phase-activation

#multi-phase-activation

The problem was not the agents. Every individual agent performed well within its domain. The problem was the missing coordination infrastructure between them, what I now call the 'Event Spine' that enables agents to work as a system rather than a collection of individuals competing for the same resources.

"We didn't know what we had," Michele Stansfield, co-founder and CEO of Cauldron Ferm, told TechCrunch. But eventually, Stansfield realized they had more than initially thought.

Our customers, ranging from large enterprises to AI research labs, are no longer just asking for AI features. They need a way to collect high-fidelity, synchronized robot and vision data to train AI models on the same robots they intend to deploy. Our AI Trainer is the industry's first direct lab-to-factory solution for AI model training.

We are now in a time of manufacturing where precision is more than a technical necessity; it's a business requirement. The more complex, globally dispersed and demanding things get, the less slack remains in the system. Under these circumstances tolerance management has become a decisive competence and affects competitiveness not only in terms of controlling costs, ensuring quality and improving production efficiency but also for long term market success.

Anthropic is expanding its push into the enterprise market with a new set of "coworker" plug-ins designed to embed its Claude AI directly into tools used by investment bankers, HR teams, and engineers, signaling a shift from standalone assistants toward AI agents that operate inside core business workflows.

Hakboian describes a pattern in which specialised agents: one for logs, one for metrics, one for runbooks and so on, are coordinated by a supervisor layer that decides who works on what and in what order. The aim, the author explains, is to reduce the cognitive load on the engineer by proposing hypotheses, drafting queries, and curating relevant context, rather than replacing the human entirely.

I'll be talking about holistic engineering or the practice of factoring in your technical decisions, designs, strategies, all the non-technical factors that are actually forces that influence your organic socio-technical problem space. As much as you can see in this canyon how natural forces have influenced the shape of the earth, so you can see the color. You can see all the different layers.

When I work on something, whether it's at Interfere or my personal projects, I like to experiment a lot. Design engineering is a lot about trial and error, and I often spend hours trying to find the "this feels right" moment. This is where AI helps. Instead of spending hours on a concept that I'm unsure of, I try that concept out in a matter of minutes, and throw it away if it doesn't feel right.

Last year I first started thinking about what the future of programming languages might look like now that agentic engineering is a growing thing. Initially I felt that the enormous corpus of pre-existing code would cement existing languages in place but now I'm starting to think the opposite is true. Here I want to outline my thinking on why we are going to see more new programming languages and why there is quite a bit of space for interesting innovation.

AI reveals a hidden, outdated assumption: that humans will continue to serve as the "digital glue," manually connecting disparate systems, teams, and decisions. For decades, enterprise software perpetuated a model of sequential handoffs, in which people managed data entry, reconciled conflicts, chased approvals via email, and updated spreadsheets. This structure was manageable when uncertainty was low and delayed decisions were affordable.

Agentic AI workflows sit at the intersection of automation and decision-making. Unlike a standard workflow, where data flows through pre-defined steps, an agentic workflow gives a language model discretion. The model can decide when to act, when to pause, and when to invoke tools like web search, databases, or internal APIs. That flexibility is powerful - but also costly, fragile, and easy to misuse.