AI agents have gone from research curiosity to boardroom priority in the space of about eighteen months. Everyone wants to deploy them; far fewer are doing it well. To get a clearer view of what separates a useful agent rollout from an expensive science project, we sat down with Jixin, who leads our work on agentic AI at Agile Insights, to talk through Agile Agents, the platform he built to help customers actually put AI agents to work.
What Is Agile Agents?
Agile Agents is Agile Insights’ platform for helping organisations create, deploy, manage, monitor and safeguard AI agents.
The underlying idea, as Jixin puts it, is to treat AI agents as a genuine workforce. A way to get tasks done on customers’ behalf, either fully autonomously or with a human positioned “on top of the loop,” overseeing outcomes rather than being pulled into every individual transaction. The goal is not to remove humans from the business. It is to move people up the value chain.
The Real Barriers to Agent Adoption
Ask Jixin what’s actually holding companies back, and the answer isn’t ambition. It’s engineering. Letting an AI agent execute tasks automatically raises hard questions around risk, security, and whether the agent is reliably doing what it’s meant to do. Cost is another sticking point: agent-driven workflows can behave unpredictably, and that unpredictability shows up directly on the invoice.
None of this is a reason to wait. It’s the reason a structured platform, rather than a one-off prototype, matters.
Train Your Agents the Way You’d Train a New Hire
One of the more useful reframes Jixin offers is to stop thinking about AI agents as software and start thinking about them as staff. Imagine you had access to a full-time employee who could work 24/7. How would you train them to do the job well? You’d show them good examples and bad ones. You’d flag the mistakes to avoid and the small gotchas that trip people up. You’d give them a clear standard for what “done well” looks like.
That’s exactly the discipline Agile Agents is built around: a way to train AI agents at scale, paired with a monitoring and evaluation framework that continuously checks whether the agent workforce is producing the outcomes you actually want, and a mechanism to fine-tune them when it isn’t.
Knowing Where to Start: Good Fits vs. Hard Cases
Not every task is equally ready for an agent, and Jixin is upfront about where the easy wins are. The best return on investment tends to come from repetitive, time-consuming, somewhat messy work: tasks a human could do, but slowly. Even better are tasks with a deterministic workflow: a defined order of steps, with a clear, measurable definition of good versus bad performance. Those are the use cases Jixin recommends tackling first, partly because they’re the easiest to train an agent for, and partly because they’re the easiest to evaluate and improve over time.
The harder cases are open-ended, unpredictable scenarios with no single right answer. They’re difficult for humans to judge, which makes them difficult to constrain. Agents tend to get creative in those situations, a genuinely valuable trait in the right context, but not always one businesses are ready to give free rein to. Jixin’s advice is to treat those as a later-stage problem, once the more deterministic work is already running well.
Breaking the Cost/Growth Trap
There is a common misconception that the end goal of AI agents is to make everything fully autonomous.
For many businesses, the right model is not necessarily human-in-the-loop, but human-on-the-loop: agents handle the repetitive work, while people stay close to the outcomes, exceptions and handling decisions that matter.
That “in” versus “on” difference is important. It allows organisations to grow without increasing human capital costs at the same rate, helping break through the linear growth limitation.
Human oversight is a feature, not a limitation.
Hype or Genuine Shift?
Asked whether the AI agent boom is a bubble, Jixin doesn’t hesitate: Agile Insights is “all in,” and the trajectory points toward something durable rather than a passing trend.
The direction of travel, in Jixin’s view, is toward fully autonomous, end-to-end agents. Right now, most businesses are still instructing agents step by step: do this, then that, here’s how. The next phase looks different: agents that take a mission or objective, build their own plan, and carry it out: writing code, reading files, searching the web, whatever the task requires, before checking their own results against the original goal and deciding whether to keep going or hand the work back to a human for feedback.
Early versions of this kind of autonomy are already visible in today’s agentic coding tools, what the market has started calling “harness engineering.” Jixin is candid that nobody has fully figured this out yet: it’s exciting, a little unsettling, and still very much an open question how to make it both safe and commercially sound. As Jixin puts it: “No one has the answer yet, but it is the future.”
Where This Leaves Customers
For organisations weighing up where to start with AI agents, the takeaway from this conversation is fairly clear: begin with the deterministic, repetitive work where success is easy to define, build the training and evaluation discipline that scales, and keep an eye on where the technology is headed next. That’s the thinking behind Agile Agents, and it’s the conversation we’re having with customers every day.
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