As AI becomes more woven into the fabric of software, the lines between software and AI will blur. Developers already use AI to create software today, but increasingly, AI will write software--including software that uses AI. AI will generate programs to solve problems better suited to traditional software, but it will also generate software to orchestrate its own operation, allowing it to overcome some of its own inherent weaknesses, accelerating progress.

Application development will take new forms. Development might start with the specification of an agent, but that agent will produce code as it operates, leaving behind a trail of deterministic software as it charts a path to meeting the applications needs. As an application matures, the agent will only resurface when corner cases are goals, patching and hardening code to fill gaps. Human developers and AI will collaborate on the creation of applications, but the balance will shift over time until most human developers play an increasingly high-level supervisory role, along the lines of a product manager. Average workers will increasingly produce ephemeral automations as a routine part of their work, though it won't seem any less natural than using a spreadsheet.

Legacy software will be tended by AI systems that first encircle these legacy software systems and eventually become a part of them (though never fully managing to eradicate them). Agent-to-agent communications will move beyond being novelties and will be become the de facto way that IT systems interact with one another.

We have already seen AI perform at the level of a silver medalist in the International Mathematical Olympiad; address benchmark problems in math, science, and programming; help find conjectures and rule out counterexamples; check proofs; and power agents to improve pieces of algorithms by changing code. We will see this trend gain steam as these tools are further developed to be used to guide human intuition when tackling science and engineering problems.

Security will become a sophisticated arms race, with new attack surfaces, new insider threats, and the equivalent of social engineering attacks, but aimed at AI systems, will become commonplace. The agent landscape will become a battlefield of increasingly active countermeasures, and some critical systems might begin to exclude the use of AI or even be partially air-gapped to protect them, forming a paradoxical retrograde trend.

As multi-agent systems become prevalent in the enterprise, we will provide advances in agent-to-agent authentication and encrypted inter-agent protocols.

Multi-agent observability will track issues of multi-agent collusion and coordination attacks, providing anomaly detection and consensus validation. Agents will continue to operate in existing enterprise ecosystems, hence the need for true hybrid operations and observability will remain. Agents will determine their own observability needs, based on feedback from the environment that they operate in, and auto-instrument themselves for operational visibility and action.

Agents with pervasive observability and memory will lead to a much more dynamic and optimized environment for data which blurs the line between data and APIs. Agents will use this memory to learn the user's needs and will prepare the needed data in a just-in-time approach, minimizing effort expended. Further, the end user will be unaware of whether data is coming from a data repository or an API as agents autonomously determine the best way to provide users with needed information.