How the rise of agentic AI is changing enterprise software

On a fundamental level, software architecture needs to be configured to support the direct execution capability of agentic AI. This essentially means moving beyond constraints that keep AI limited to suggestions, and designing safe ways for it to carry out approved actions.

In an enterprise coding environment, for example, AI currently works like this. It swiftly completes the labour-intensive tasks of mapping out data flows and categorising key components. But then it hits an obstacle, as it can only feed this back to a human operator, who must then apply the coding changes. This creates a backlog, eating into the time savings achieved by the AI solution.

An agentic-focused architecture reduces this bottleneck by allowing the AI to carry out the change within defined permissions, with the right review and rollback points where needed.

Handing over execution to agentic AI can be a significant source of anxiety for enterprise management. And there’s a good reason for this: mistakes and miscalculations are expensive for enterprises, and need to be prevented wherever possible.

However, on the other hand, not using the execution capability of the agentic AI solution means not experiencing its full benefits.

Agentic AI can help alleviate some of this stress, but only if software architecture is configured to allow this to happen. The new architecture must support systematic quality control and results analysis, allowing the AI to make ongoing assessments and adjustments that improve results, and to flag uncertainty for human review when appropriate.

This in turn generates a continuous feedback loop, further enhancing the capabilities of the agentic AI.

Agentic AI presents enterprises with a wide range of capabilities and potential outcomes. What’s more, the way in which enterprises use agentic solutions to achieve these outcomes is going to be wildly different.

Software architecture will need to reflect this plurality. Solution developers will need to move away from standardised ‘one-size-fits-all’ solutions and instead provide the flexibility and personalisation that their enterprise clients need. Enterprise teams and developers will work closely with one another to develop architecture that meets specific operational needs.

A move away from standardisation is also necessary for other reasons. Operational needs are not static. They will evolve over time, even over a single year, or perhaps even a quarter. Software architecture needs to meet these evolving needs in real time, expanding capacity and capability while keeping the user experience seamless.

And this user experience also needs to be personalised. The age of AI has increased consumer expectations in terms of individualised journeys and outcomes. Agentic AI needs to be flexible and agile enough to provide this, supported by the right underlying software architecture.

AI software architecture must be focused on transparency and clarity. All stakeholders need to be aware of what agentic AI solutions are doing, why they are doing it, and what value they are bringing to the enterprise.

This is vital from an operational perspective. Flexibility and agility can result in swift changes and results, and it is critical that auxiliary solutions and support teams can identify current operational modes so they remain in step with the agentic strategy.

On top of this, software architecture is growing more complex and more varied. Before the current wave of AI, the trend was typically towards simplification and streamlining, but this is not always possible with flexible, highly capable AI solutions. Transparency and clarity help ensure that operations remain on track, even as software architecture grows in complexity.

It’s also crucial from a human perspective. Human personnel need to understand what their own role is within the agentic AI landscape, rather than feeling like they are being pushed out by evolving AI capability. If the software architecture is developed with transparency and clarity in mind, it becomes easier for personnel at all levels to adjust to significant operational changes.

Traditional enterprise software architecture was human-focused. It was designed to be easy for humans to use and interact with via specially designed interfaces, and humans themselves developed much of the code required to build these solutions. However, this has changed. Agentic AI software architecture does not replace human personnel, but it does redefine their roles.

From the software development perspective, the software architect becomes the key point of connection between agentic platforms and C-suite executives. While the architect’s coding workload is often reduced, it’s this professional who will demonstrate the value of the solution to enterprise decision makers.

From an internal operations perspective, human teams become safeguards and creative decision makers. Agentic AI will handle many of the roles traditionally associated with in-house teams, but human teams will still need to oversee the transition and ongoing viability of AI solutions. They will also be required to provide the decision making that dictates how agentic AI is deployed and how to get the best out of the technology.

Agentic AI is often about saving time, money and resources for your enterprise while delivering high-quality results to your users.

To get the very best from agentic AI in 2026, and beyond, it helps to start now. Reach out to our team today.