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.
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.
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.
The scaling model relies on several predictive factors of the system, including the underlying LLM's intelligence index; the baseline performance of a single agent; the number of agents; number of tools; and coordination metrics. The researchers found there were three dominant effects in the model: tool-coordination trade-off, where tasks requiring many tools perform worse with multi-agent overhead; capability saturation, where adding agents yields diminishing returns when the single-agent baseline performance exceeds a certain threshold; and topology-dependent error amplification, where centralized orchestration reduces error amplification.
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.
Human hands are incredibly dexterous tools - but they have their limits. They are asymmetric, they have only a single thumb and, fundamentally, they're connected to our arms. But none of that poses a problem for this robot claw. Its symmetrical design means it can seamlessly approach different tasks without having to twist to find the right angle, six-fingers mean the design can juggle multiple objects at the same time and, if needed, it can simply leave its arm behind, perfect for dangerous or hard to reach places.
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.
According to Tamas Cser, Founder and CEO of Functionize, the industry is on the verge of a structural shift. By 2026, development teams will transition from AI copilots to agentic fleets: coordinated groups of specialized AI agents operating semi-autonomously across the entire software lifecycle. In this new paradigm, engineering excellence is measured less by syntactic mastery and more by the ability to orchestrate intelligent systems-delegating, validating, and refining work continuously, at machine speed.
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.
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.
