Systems | Development | Analytics | API | Testing

Most finance leaders have now tried an AI assistant on real data. Most of them had a similar experience. Here is what is actually happening, and what trustworthy finance AI looks like when it works.

AI chats are often a rather solitary experience: just you and ChatGPT, sitting there together, solving a problem. But so many of the tasks that we perform day to day are ones that benefit from, or often even require, collaboration with other people such as colleagues, family members, or friends. So, if AI agents are helpful, and other people are helpful, then how can we provide a space for multiple people to collaborate with each other and with AI agents?

AI agents are no longer a pilot-stage bet. As of 2026, 80% of enterprises have at least one production AI agent deployed. The global AI agents market has crossed $10.91 billion and is sprinting toward $52.62 billion by 2030. The cost-per-task economics are staggering: a human-handled customer support ticket costs $4.18 on average. An AI agent resolves the same ticket for $0.46. That is a 9x cost reduction, right there.

AI adoption programs tend to end in the same place. Tools are accessible, usage is up, and there's a dedicated Slack channel for wins. Six months later, nothing about how the team works has fundamentally changed. People are doing the same things – just slightly faster. And it’s easy for programs to stall when you’re measuring the wrong thing. Adoption (whether people have access and whether they're using the tools) is visible and easy to report.

Live polls are a staple of conferences, streams, and all-hands: a question goes up on the big screen, everyone votes from their phone, and the bars race each other in realtime. There's a lot of different ways you could implement this.

GitHub Copilot, Claude, and Cursor have become genuine superpowers for API development. They draft OpenAPI definitions, generate endpoints, propose schema changes, and write test cases — all from inside the IDE, in real time. Teams using these tools are generating API definitions faster than most thought possible even a few years ago. That velocity is real, and it’s reshaping how engineering teams think about their toolchain.

When automated test script generation consumes a significant chunk of engineering capacity, your roadmap was already behind the eight ball before the sprint even began.

Rubber duck debugging allows us to discover our own coding errors by retracing our steps. Instead of relying on complex black-box tools, we simply explain our own logic until the problem reveals itself. This is one of the most straightforward debugging techniques around, and it can be easily enhanced by AI tools.

Despite years of progress in cloud testing platforms, network latency remains the most stubborn – and often ignored – variable in load testing reliability. A recent study highlights that network latency can skew load test results by as much as 30%. That’s not a rounding error; it’s the difference between a site that passes in the lab and one that buckles under real-world traffic.

Token optimization is the new tokenmaxxing. Here's why burning fewer tokens produces better software and why the economics of AI make this shift inevitable.