For the last year or so, we’ve been flooded with AI “assistants” that basically act like slightly glorified search bars.
But the real shift—and where I’ve been spending a lot of my tracking and development time lately—is the move towards autonomous, agentic workflows. Instead of just prompting an LLM to summarise a PDF, we are starting to build agents that actually do things: query databases, monitor user behaviour, and trigger complex multi-step actions on autopilot.
The catch? If you’re deploying these in any serious B2B environment, you quickly hit a massive wall: trust.
You simply can’t let an AI agent loose on sensitive data and just hope for the best. If an agent makes an executive decision—like halting a workflow or flagging a compliance issue—your legal team is going to want to know exactly why and how it reached that conclusion.
This is especially true in highly regulated, complex sectors. Take private equity, for instance. Firms have to manage totally fragmented data across dozens of different portfolio companies. If you want a solid, practical look at how this plays out in the real world, my clients over at Squirro have put together a great piece on using secure AI for private equity to connect these disparate data silos, plug revenue leaks, and—crucially—maintain an immutable audit trail for every single action the AI takes.
For me, the takeaway is simple. If you are building custom agents for marketing, SEO, or operations, do not just focus on the prompts or the cool integrations. If you don’t build in auditability, data privacy, and solid governance from day one, your shiny new workflow is never going to make it out of the staging environment.