Artificial Intelligence is rewriting the rules of software. What began as a wave of intelligent assistants that help users write, code, or summarize is now turning into something much bigger: intelligent systems that can reason, collaborate, and act.
This change goes far beyond adding a new feature or automating a task. It is transforming how software is designed, how it operates, and how it creates value.
From automation to intelligence
For decades, software automation was based on rules. If something happened, the system reacted exactly as programmed. It was predictable but limited. AI has broken that pattern. Instead of rigid scripts, we now build systems that understand intent, interpret context, and make decisions.
The first step in this evolution came with intelligent assistants: the copilots that help us write emails, generate code, or analyze data. The next step is Agentic AI – systems of autonomous agents that can reason, collaborate, and act on behalf of users or other systems.
Each agent focuses on a specific task. One observes, another decides, a third executes. Together they behave like distributed intelligence, capable of monitoring, coordinating, and adapting in ways that traditional software never could. The result is not a single chatbot or model but an ecosystem of specialized intelligences working together to solve complex problems.
Agent orchestration
Modern AI systems rarely rely on just one agent, model, or service. They combine multiple agents, models, and tools to complete a task. A language model might interpret a request, a reasoning agent decides what to do, and another component executes the action.
Platforms such as Microsoft Foundry and the Microsoft Agent Framework make this possible. They give developers tools to define agents, manage their permissions and connections, and ensure they collaborate securely. In practice, this creates the structure and control needed for agents to operate in harmony within an organization’s digital ecosystem.
Agentic integration platform
At Twoday, these ideas have become tangible in our AI-driven integration platform. Built on Azure Integration Services, it connects the systems companies rely on daily – CRM, ERP, HR, and many others – now with intelligence built into its core.
Inside the integrations, AI agents monitor data flows, validate data quality, and detect anomalies. If something looks unusual or breaks a rule, the system can involve a human automatically, creating a true human-in-the-loop experience. Instead of waiting for nightly syncs or manual error handling, the platform acts in real time and escalates only when necessary.
The platform also includes a chat-based interface that lets users interact directly with data. Instead of digging through dashboards or reports, they can simply ask, “Find all information about the property at Sundkaj 125 in Nordhavn,” or “Which suppliers haven’t updated their data?” The agents interpret the question, retrieve the information, and respond instantly in natural language.
Even the process of building integrations has changed. Developers use AI tools like GitHub Copilot to generate much of the repetitive code, documentation, and testing, freeing time to focus on architecture and governance. Intelligence is now embedded throughout the full lifecycle – from design to deployment and operation.
A new kind of software development
This shift fundamentally changes how software is built. Developers are no longer just writing logic; they are orchestrating intelligence. Software is designed as an ecosystem of agents that collaborate rather than a single monolithic program executing fixed instructions.
Learning and adaptation are built in. Cloud platforms like Microsoft Foundry handle versioning, access control, and monitoring of models and agents, ensuring that the system keeps improving. Software begins to behave more like an organization – a collection of specialized roles working within a shared structure.
What it means for leaders
For business and technology leaders, this shift brings both opportunity and responsibility. System architecture must evolve toward modular and event-driven designs that can host multiple AI components safely. Governance becomes more important, ensuring transparency, traceability, and alignment as decision-making is distributed across agents.
Teams will need new skills. Developers must understand both traditional engineering and emerging disciplines like prompting, evaluation, and model integration. As these capabilities mature, intelligent software will reduce friction between people and systems, improve response times, and enable new services built on real-time insight.
Organizations that begin exploring now gain a structural advantage. They learn to blend human judgment with machine reasoning, turning complexity into clarity and speed.
AI-native software
Our intelligent integration platform is one example of what’s coming. Over the next few years, more enterprise systems will move in the same direction: applications built as networks of agents, each responsible for reasoning, learning, or execution.
Software will become self-observing, adaptive, and collaborative across data sources and teams. The role of humans shifts from operating systems to supervising and guiding them.
This is the path toward AI-native software – solutions not just using AI, but designed around it from the ground up.
It’s still early, yet the direction is clear. AI isn’t just changing what software can do – it’s changing what software is.
Thomas Martinsen, Tech Evangelist at Twoday