The advent of powerful Large Language Models (LLMs) has ushered in an ``Age of the Agent,'' enabling autonomous systems to tackle complex goals. However, the transition from prototype to production is hindered by a pervasive ``crisis of craft,'' resulting in agents that are brittle, unpredictable, and ultimately untrustworthy in mission-critical applications. This paper argues this crisis stems from a fundamental paradigm mismatch -- attempting to command inherently probabilistic processors with the deterministic mental models of traditional software engineering. To solve this crisis, we introduce a governance-first paradigm for principled agent engineering, embodied in a formal architecture we call ArbiterOS.

Despite the significant strides made by generative AI in just a few short years, its future progress is constrained by the challenge of building modular and robust systems. This capability has been a cornerstone of past technological revolutions, which relied on combining components to create increasingly sophisticated and reliable systems. Cars, airplanes, computers, and software consist of components-such as engines, wheels, CPUs, and libraries-that can be assembled, debugged, and replaced. A key tool for building such reliable and modular systems is specification: the precise description of the expected behavior, inputs, and outputs of each component. However, the generality of LLMs and the inherent ambiguity of natural language make defining specifications for LLM-based components (e.g., agents) both a challenging and urgent problem. In this paper, we discuss the progress the field has made so far-through advances like structured outputs, process supervision, and test-time compute-and outline several future directions for research to enable the development of modular and reliable LLM-based systems through improved specifications.

Aiming at the current problems of theory-oriented, practice-light, and lack of innovation ability in the teaching of postgraduate software engineering courses, a multi-stage feedback teaching mode for software engineering postgraduates based on competition project-driven is proposed. The model is driven by the competition project, and implementing suggestions are given in terms of stage allocation of software engineering course tasks and ability cultivation, competition case design and process evaluation improvement, etc. Through the implementation of this teaching mode, students' enthusiasm and initiative are expected to be stimulated, and the overall development of students' professional skills and comprehension ability would be improved to meet the demand of society for software engineering technical talents.

Cyber-physical systems (CPSs) are smart systems that depend on the synergy of cyber and physical components. They link the physical world (e.g. through sensors, actuators, robotics, and embedded systems) with the virtual world of information processing. Applications of CPS have the tremendous potential of improving convenience, comfort, and safety in our daily life. This paper provides a brief introduction to CPSs and their applications. The term "cyber-physical system" (CPS) was coined in 2006 by Helen Gill of the US National Science Foundation (Henshaw, 2016).

Machine learning has been around for decades, but for much of that time, businesses were only deploying a few models and those required tedious, painstaking work done by PhDs and machine learning experts. Over the past couple of years, machine learning has grown significantly thanks to the advent of widely available, standardized, cloud-based machine learning platforms. Today, companies across every industry are deploying millions of machine learning models across multiple lines of business. Tax and financial software giant Intuit started with a machine learning model to help customers maximize tax deductions; today, machine learning touches nearly every part of their business. In the last year alone, Intuit has increased the number of models deployed across their platform by over 50 percent.

In the first instalment of this new video series, Stephen José Hanson talks to Michael I Jordan, about AI as an engineering discipline, what people call AI, so-called autonomous cars, and more. To provide some background to this discussion, in 2018, Jordan published an essay on Medium entitled Artificial intelligence -- the revolution hasn't happened yet, in which he argues that we need to tone down the hype surrounding AI and develop the field as a human-centric engineering discipline. He adds further commentary on this topic in an interview published this year in IEEE spectrum, (Stop calling everything AI). Hanson wrote a rebuttal to the Medium article, AI: Nope, the revolution is here and this time it is the real thing, and the pair discuss the theme in more detail in this video discussion below. There is also a full transcript of the discussion below. This interchange was recorded on June 15th 2021. HANSON: Hi Michael, good to see you! So let's get into this. Let me just state what I think you said and you tell me where I'm wrong, if I am. So it appears to me that you're basically talking about that AI should arise from an engineering discipline that with start from well-defined science like chemistry and chemical engineering and this would allow the insights from the science to migrate their way into an engineering domain which had principles of design and control and risk management and many other good statistical quality control ideas that basically made AI the valuable and useful and have some utility and something actually went to calculate about the AI I actually being useful as opposed to the number hidden units it has…. JORDAN: Just to slow you down a little bit there, I mean historically I think the good points of reference or things like the development of chemical engineering or electrical engineering were that there was an existing science and understanding and there was an appetite to build real-world systems that have huge implications for human life. So chemical factories didn't exist initially, but when they started to exist, I don't think it was that the science was all worked out and they kind of applied it and it just happened.

Safety advocates call for a national AI testbed, with trust based on'engineering discipline'. The splashy unveiling of Tesla's robot assistant stokes the ongoing debate about AI safety and how automated systems can be tested and validated before they are unleashed on city streets and factory floors. The fear during the initial round of AI hyperbole focused on malevolent, self-replicating, HAL-like machines eventually overpowering their creators or roaming uncontrolled on battlefields. The debate has since become more pragmatic, with a sharper and welcome focus on safety. Specifically, how can we promote AI safety in ways the will allow human operators to trust autonomous systems in applications that for now remain well short of mission critical, requiring 99.999 percent reliability?

The growth of myriad cyber-threats continues to accelerate, yet the stream of new and effective cyber-defense technologies has grown much more slowly. The gap between threat and defense has widened, as our adversaries deploy increasingly sophisticated attack technology and engage in cyber-crime with unprecedented power, resources, and global reach. We are in an escalating asymmetric cyber environment that calls for immediate action. The extension of cyber-attacks into the socio-techno realm and the use of cyber as an information influence and disinformation vector will continue to undermine our confidence in systems. The unknown is a growing threat in our cyber information systems.

The Motto of the present period is Artificial Intelligence ( AI). As with several phrases that extend from specialized academic fields into general circulation, the use of the term is followed by substantial confusion. But this isn't the standard case where the public doesn't understand the scientists -- here the scientists are just as puzzled as to the public. The thought that our age is somehow seeing the rise of a digital intellect that rivals our own entertains us all -- enthusiasm us and scaring us in equal measure. There's a particular story about the present period that one can tell. Consider the following story which involves decisions about people, computers, data, and life-or-death, but where the emphasis is something other than fantasies about intelligence-in-silicon.

So, your company decided to invest in machine learning. You have a talented team of Data Scientists churning out models to solve important problems that were out of reach just a few years ago. All performance metrics are looking great, the demos cause jaws to drop and executives to ask how soon you can have a model in production. It should be pretty quick, you think. After all, you already solved all the advanced scienc-y, math-y problems, so all that's left is routine IT work.