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Writing MUnit tests is something I do regularly as part of my MuleSoft development work, and like many engineers, I'm always looking for ways to work more efficiently without cutting corners. So when AI-powered features started making their way into the MuleSoft ecosystem, I was genuinely curious to see what they could do in practice. Test creation felt like the natural place to start, as it's a workflow I know well, which meant I could quickly spot where the tooling added value and where it still needed work.

One of those tools is the MuleSoft Dev Agent, which introduces a new way to create tests, using natural language prompts to generate MUnit tests directly from your MuleSoft projects. So in this walkthrough, we put the Dev Agent straight to work on a simple Hello World API and see how far it can take us, from generating MUnit tests in seconds to revealing where it really adds value.

What decades on the frontline reveal about system-generated demand, avoidable work, and where meaningful change really starts.

Dr Simon Eccles has spent decades working in A&E as a senior doctor and serving on countless boards across London. He is also a thought leader on the best technology for the NHS, and he sat down with us to discuss ways the NHS can improve its use of Salesforce. With all his years of experience, he has seen winter pressures come and go, targets change, restructurings repeat, and countless workforce initiatives introduced with good intent. His view is not theoretical. It is lived.

Introducing AI-driven solutions like Agentforce into UK policing is not a decision to take lightly. These powerful technologies have the potential to completely transform the way forces operate, from back-office efficiency through to frontline support. But with this power also comes new risks, particularly in how sensitive personal data is processed, stored, and interpreted. While these risks are real, the potential to deliver faster, fairer, and more effective policing outcomes makes their careful navigation worthwhile.

In an environment governed by high standards of public accountability, ethical scrutiny, and strict legal obligations under the UK GDPR, Data Protection Act 2018, and Law Enforcement Processing provisions, a Data Protection Impact Assessment (DPIA) is not just a formality - it's essential. It provides a defensible framework for making informed decisions, protecting officers and the public alike.

The British model of policing has always rested on a delicate balance: local accountability, national legitimacy, and public consent. The Government’s proposal to create a National Police Service is the most radical attempt in a generation to rebalance that system for a digital age.

Having witnessed three decades of change in the police service, and as a retired senior officer, at first glance, the Home Office’s White Paper, From Local to National: A New Model for Policing, is about structures - fewer forces, clearer leadership, stronger central coordination.

Every public sector leader I speak to wants to unlock the promise of artificial intelligence (AI). The ambition is clear: to deliver services faster, give staff back precious time, and improve experiences for citizens. The challenge is that budgets are tight, scrutiny is high, and no one wants to waste money on the wrong investment.

That tension often leads to the question… how much should we actually spend on AI?

When people reach out to the police, it’s often at a moment of vulnerability, when something has happened that has shaken their sense of safety and left them unsure what to do next. They may feel anxious, overwhelmed, or uncertain about whether they are using the right channel, sharing the right information, or even raising the right issue at all. At the same time, forces are managing rising volumes of calls, messages, and digital contact under sustained pressure, yet every interaction still represents a person looking for reassurance and support. Meeting these moments in a way that feels timely, confident, and compassionate has become one of the most important challenges facing UK policing today.

But a more supportive, intelligent approach is beginning to take shape.

Salesforce events always bring a unique buzz. Whether it’s Dreamforce, World Tour or a specialist showcase, there’s an energy in the room that reminds you how fast the ecosystem moves and how much innovation is happening behind the scenes.

The Agentforce World Tour 2025 at ExCeL London carried that same momentum. Crowds, conversations and excitement aside, the real draw was seeing just how quickly Agentforce is evolving and what that means for both technical and business teams alike.

As a developer, I went in hoping for real implementation stories, deeper technical insight and the latest updates on Agentforce Vibes. The day delivered on all of that. I left with useful takeaways, fresh ideas to test and a stronger sense of where enterprise AI is heading next. To help set the scene and share what the day revealed, I've put together some key moments that stood out.

This year’s HMICFRS State of Policing Report, published in September, raises several important issues that caught my attention. For those who haven't caught up yet, the report highlights the significant pressure forces continue to operate under, with growing demand and legacy systems limiting their ability to respond effectively.

As a project manager, I’ve had the privilege of guiding all sorts of teams and initiatives, from complex digital transformations to small but mighty upgrades that make a real difference. But few things have brought me as much career satisfaction as working on IT projects that support the police here in the United Kingdom.

These projects stand out because they’re about more than just delivery timelines, sprint goals, or stakeholder reports. They’re about contributing to something bigger - supporting the people who keep our communities safe, and helping them do their jobs more effectively through better technology.

When people talk about artificial intelligence (AI) today, the conversation often drifts into mystique. We hear phrases like 'thinking machines' or 'AI replacing humans,' as if a spark of magic has suddenly entered our world. But history shows a different story: every wave of automation, whether industrial, digital, or now intelligent, has been shaped not by magic, but by clear thinking, human analysis and precise understanding of what needed to be achieved.

To see this, it helps to trace automation through three phases: achieve what I achieve, do what I do, and think what I think.