Agentic AI has significantly extended the capabilities of large language models (LLMs) by enabling complex reasoning and tool use. However, most existing frameworks are tailored to domains such as mathematics, coding, or web automation, and fall short on geospatial tasks that require spatial reasoning, multi-hop planning, and real-time map interaction. To address these challenges, we introduce MapAgent, a hierarchical multi-agent plug-and-play framework with customized toolsets and agentic scaffolds for map-integrated geospatial reasoning. Unlike existing flat agent-based approaches that treat tools uniformly-often overwhelming the LLM when handling similar but subtly different geospatial APIs-MapAgent decouples planning from execution. A high-level planner decomposes complex queries into subgoals, which are routed to specialized modules. For tool-heavy modules-such as map-based services-we then design a dedicated map-tool agent that efficiently orchestrates related APIs adaptively in parallel to effectively fetch geospatial data relevant for the query, while simpler modules (e.g., solution generation or answer extraction) operate without additional agent overhead. This hierarchical design reduces cognitive load, improves tool selection accuracy, and enables precise coordination across similar APIs. We evaluate MapAgent on four diverse geospatial benchmarks-MapEval-Textual, MapEval-API, MapEval-Visual, and MapQA-and demonstrate substantial gains over state-of-the-art tool-augmented and agentic baselines. We open-source our framwork at https://github.com/Hasebul/MapAgent.

In the Generative Age, the nature of knowledge work is transforming. Traditional models that emphasise the organisation and retrieval of pre-existing information are increasingly inadequate in the face of generative AI (GenAI) systems capable of autonomous content creation. This paper introduces the Knowledge Sculptor (KS), a new professional archetype for Human-GenAI collaboration that transforms raw AI output into trustworthy, actionable knowledge. Grounded in a socio-technical perspective, the KS is conceptualised through a framework of competencies, including architecting a vision, iterative dialogue, information sculpting, and curiosity-driven synthesis. A practice-based vignette illustrates the KS role in action, and in a self-referential approach, the paper itself serves as an artefact of the sculpting process it describes.

Explainability is important for the transparency of autonomous and intelligent systems and for helping to support the development of appropriate levels of trust. There has been considerable work on developing approaches for explaining systems and there are standards that specify requirements for transparency. However, there is a gap: the standards are too high-level and do not adequately specify requirements for explainability. This paper develops a scoresheet that can be used to specify explainability requirements or to assess the explainability aspects provided for particular applications. The scoresheet is developed by considering the requirements of a range of stakeholders and is applicable to Multiagent Systems as well as other AI technologies. We also provide guidance for how to use the scoresheet and illustrate its generality and usefulness by applying it to a range of applications.

The fast adoption of new technologies forces companies to continuously adapt their operations making it harder to predict workforce requirements. Several recent studies have attempted to predict the emergence of new roles and skills in the labour market from online job ads. This paper aims to present a novel ontology linking business transformation initiatives to occupations and an approach to automatically populating it by leveraging embeddings extracted from job ads and Wikipedia pages on business transformation and emerging technologies topics. To our knowledge, no previous research explicitly links business transformation initiatives, like the adoption of new technologies or the entry into new markets, to the roles needed. Our approach successfully matches occupations to transformation initiatives under ten different scenarios, five linked to technology adoption and five related to business. This framework presents an innovative approach to guide enterprises and educational institutions on the workforce requirements for specific business transformation initiatives.

New Zealand is developing an approach to supporting the ethical adoption of AI -- one that is focused on building an AI ecosystem on a foundation of trust, equity and accessibility right from the onset. A crucial part of this approach is to involve key stakeholders in the planning. And that is exactly the reason why the government has designed the system so every New Zealander and every technology expert who matters can contribute. The success of this ITP requires us to form a consensus view on the scope of our ambition and how this can be achieved with actions and initiatives that are sufficiently realistic to bring about meaningful change – both short and longer-term. Wellington published a draft that should jumpstart its pursuit of an ethical AI ecosystem: the Industry Transformation Plan (ITP) which covers its overall digital transformation road map.

It's been a long time since there was a hard line between a fundamental active manager and a quantitative investor using advanced computing techniques to uncover ideas. Even as the line has blurred recently, Michael Masdea, head of Wellington Management's Investment Science Group, said his number one job is to preserve the art in investing while bringing in sophisticated computing capabilities to support the firm's active managers. "We think that the balance of art and science is critical. Machines can't pick securities, but they can help a lot with the process around picking securities," said Masdea, who is a former semi-conductor analyst at Wellington as well as an equity portfolio manager. The Investment Science Group is focused on applying scientific techniques, such as data analytics, to everything from how portfolio managers come up with new ideas and implementing them to maximize returns, to developing professional investors, which includes uncovering and mitigating the downside of their behavioral biases.

The police have unveiled their first AI officer, with hopes she'll soon be smiling and blinking out of screens in stations all around New Zealand. Ella, the artificial intelligence cop at the centre of the police's new digital services, was revealed at the police national headquarters in Wellington this morning. Ella, which stands for Electronic Lifelike Assistant, is part of two new digital kiosks police have designed to help reduce queues in stations and to provide a modern way to connect with the public. Designed as a mix of 26 different people, Ella is the brainchild of project manager Erin Greally, and will primarily be available only at the headquarters building in Molesworth St, where users can ask for information or be connected to whoever they're visiting. If the three-month pilot goes well, police hope to have Ella's friendly, CGI face spread across kiosks throughout the country.

Artificial intelligence (AI) is clearly a growing force in the technology industry. AI is taking center stage at conferences and showing potential across a wide variety of industries, including retail and manufacturing. New products are being embedded with virtual assistants, while chatbots are answering customer questions on everything from your online office supplier's site to your web hosting service provider's support page. Meanwhile, companies such as Google, Microsoft, and Salesforce are integrating AI as an intelligence layer across their entire tech stack. Yes, AI is definietely having its moment.

Connection science brings the computational sciences together with other disciplines such as psychology, neuroscience, philosophy, linguistics, sociology and cognitive science. This transdisciplinary approach seeks to leverage what's technically possible in a way that is inclusive and improves lifes.

Artificial intelligence (AI) is clearly a growing force in the technology industry. Chatbots and virtual assistants are becoming a key part of new products, and robots are taking center stage at conferences and showing potential in their roles in various industries like retail and manufacturing. Meanwhile, companies such as Google, Microsoft, and Salesforce are integrated AI as an intelligence layer into the entire tech stack. Yes, AI is now having its moment. This isn't the AI that pop culture has conditioned us to expect; it's not sentient robots or Skynet, or even Tony Stark's Jarvis assistant.