Before I get started on this one, it’s largely a thought exercise on where we could be taking Agents and the impact on the traditional API integration model.
The API landscape will shift. Just as APIs revolutionised how applications communicate, AI Agents are likely to emerge as the next solution for system integration. This transformation isn’t just about new technology – it’s about fundamentally changing how our software systems interact, learn, and evolve together.
Mulling over why systems like ChatGPT, Perplexity, Gamma, Gemini, Claude etc. are so successful – it’s all about the interface. It isn’t just about their perceived intelligence – it’s about how they interact with us – it’s about the interface; the natural language interface . It’s a shift in how humans and machines collaborate. What does this mean as an approach to business.
I couldn’t believe the timing this week. As I’m working through the course material for the Microsoft AI Engineer exam a post dropped from Rajani Janaki Ram with a Framework on Adopting AI on Azure – brilliant! I thought I’d review the framework this week and what it means.
Generative AI (GenAI) is full of potential, but like any rapidly evolving technology, it carries the risk of technical debt. Making the right decisions now can prevent future headaches. Let’s explore how you can be shrewd about your GenAI investments.
(This week is an experiment to see what you think about the new ChatGPT o1-preview model output. I iterated through prompts to ask o1-preview to introduce itself and explain its abilities using my usual article format. The title of this article and all of the main article text below are 100% generated by the o1-preview model. What do you think? My opinion is at the end. For the record it took 29 seconds to generate the content.)
Understanding the risks in any organisation or project takes time and usually involves one or more risk workshops, more often than not starting with a blank sheet of paper. Massachusetts Institute of Technology (MIT) have provided us with a short cut to identify risks associated with artificial intelligence using a new resource, the AI Risk Repository – save time and improve the breadth and depth of risks.
In niche non-profit grant-making, informed decisions are essential. Relying on accurate data helps align granting strategies with the organisation mission, vision, and goals, as well as addressing community funding gaps.
No surprise – it’s a (‘it’ in this case being the deployment of AI from a corporate perspective) a challenge and an opportunity. From the Artificial Intelligence perspective, businesses have access to a variety of tools and frameworks designed to facilitate AI adoption….that often operate in silos.
These two subjects, with separate sections below are intertwined – understanding AI risk and the management of AI systems go hand in hand. Luckily NIST and ISO provide us with some tools to help with both.