Are we getting bored of all the talk about agents and agentic AI yet? You can’t glance at LinkedIn or tune into a business podcast without ‘agentic’ being positioned as the future of everything: the technology that will streamline operations, maximise profits and delight both customers and your internal teams.
To be honest, I’m all for it. Not simply because building agentic solutions is a big part of our business (although that’s a factor, of course), but also because the hype, in this case, is justified. We’re already seeing clients’ processes transformed by agents and automation, and we’re helping other clients take their first steps on that journey, understanding where agents can make a difference for them. It’s an exciting time.
And as the concept of ‘agentic AI’ matures, we’re already seeing clients’ concerns and requirements shifting.
In recent years, creating AI-powered applications and agents has become increasingly simple and accessible. You don’t need to be a software developer to build an agent, and the tools themselves are becoming more affordable by the day.
So the challenge is no longer how to build effective agents. The challenge is how to build robust, production-ready agents that deliver value to the business without exposing it to unnecessary risk. And then to make that process repeatable.
For me, this is where the conversation around enterprise AI is getting interesting.
Governance, Guardrails and Control
As organisations begin to move beyond experimentation, questions around governance and control are becoming more important.
Of course, we want AI agents to reason and act autonomously; that’s their big selling point. But large organisations can’t afford agents that breach policies or behave unpredictably. The ability to define clear restrictions around what agents can access and what actions they can take is therefore becoming a fundamental part of enterprise AI adoption.
This is an area where platforms such as OutSystems are evolving quickly. For full disclosure, Systems iO is an OutSystems Gold Partner, so we’re clearly big fans of the platform, but with very good reason! Its Agent Workbench, for example, gives organisations a way to define and enforce restrictions consistently across agents, helping ensure that sensitive information remains protected and that applications operate within established security and governance policies.
The businesses that really harness the power of agents will be the ones that can take them through a reliable and repeatable process, then deploy them securely and with confidence.
Closing the Enterprise AI Gap
The challenges around governance connect closely with what OutSystems describes as the “enterprise AI gap”.
Most organisations are not short of AI ideas. If anything, they have the opposite problem. Prototypes, pilots and proofs of concept are appearing everywhere. Yet many businesses still struggle to translate that activity into scalable solutions that create lasting value.
The gap between demonstrating what AI can do and embedding it into the everyday workings of an organisation remains significant.
Closing that gap requires organisations to think beyond the initial use case. They need to consider how solutions will be governed once they are in production, how they will evolve as underlying models change and how they will fit into the wider technology landscape.
This is one of the reasons platforms such as OutSystems have an important role to play in the enterprise space. They provide a practical route from experimentation to enterprise adoption, while giving organisations a framework in which AI applications can be developed and managed over time.
Pragmatic Openness
With a market that’s evolving so quickly, developers and innovation teams need to keep trying new approaches and technologies in order to move with the times. That makes it risky for companies to put all their eggs in one AI basket.
The idea of ‘openness’, and of interoperability between AI models and platforms, is therefore becoming increasingly important. It gives organisations more freedom to adapt as the market changes and reduces the risk of vendor lock-in or eventual obsolescence.
This is where OutSystems has really played a blinder, positioning itself as the solid framework that secures your agents and applications while making the development tools used within the platform hot-swappable, depending on developers’ preferences.
Rather than forcing teams into a single approach, OutSystems has increasingly opened its platform to allow developers to work with their preferred AI coding tools while still operating within a common development and governance environment.
I think of this as a kind of ‘pragmatic openness’. It recognises the inevitable truth that developers will continue to experiment with different AI coding tools, but provides the reassurance that they can experiment freely, with security and governance already taken care of.
The same principle applies to the AI models that sit behind individual agents. Teams building agents in OutSystems can select from a range of models on a per-agent basis, recognising that not every problem requires the largest or most expensive frontier LLM.
In many cases, a smaller or more specialised model can deliver excellent results at a fraction of the cost. Being able to match the model based on its cost-effectiveness is likely to become increasingly important as businesses begin operating large numbers of agents, and as AI token costs increase.
Looking Ahead
Enterprise appetite for AI agents continues to grow rapidly, and the opportunity ahead is undoubtedly huge.
But as the market matures, the conversation is changing. It’s getting easier to build an effective one-off agent, but if an organisation can’t get that agent into production in a way that’s reliable and secure, then all that innovation is likely to go to waste.
The road ahead will require a different mindset from the one that has driven much of the experimentation we have seen so far. The businesses that strike the right balance between innovation and governance will be the ones that unlock the greatest value from AI and agentic technology in the years ahead.
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