AI is driving a fundamental shift in software applications from consumption of standalone models as chatbots to multi-agent systems that can plan, act, and adapt in real time. However, in 2025, the agent landscape is the wild west. The process of building and deploying agents is hit or miss, undisciplined, and exposes businesses to new kinds of security vulnerabilities if agents are not built with the greatest of care. It is possible to create stellar agents, but it is neither easy, nor guaranteed, and it is expensive.

Hugging Face CEO Clem Delangue once said "when it works, it's software." In 2026, generative AI will "grow up" and the process of building and deploying AI will become more disciplined, more orderly, safer, and (in a good way) a little more boring. Middleware and frameworks will emerge that make agent building less art and more science, resulting in more predictable outcomes.

At IBM, we are pursuing an ambitious research agenda under the banner of "generative computing"--the idea that generative AI can be woven into software systems, and that deterministic traditional software and generative AI can not only coexist but can fundamentally complement each other's strengths and weaknesses. In generative computing, agents are just one example of generative programs, and new programming models will emerge to make usage of AI more predictable and secure.

The capabilities of models will continue to improve, following a Moore's Law-like progression where every 9-12 months we see a 10-fold reduction in the size of a model required to achieve a certain level of capability. This will make AI increasingly ubiquitous. We will see a broader range of software quietly using AI, often in less visible but equally powerful ways, as AI becomes commonplace enough to suffuse software of all kinds.

We expect all major IBM products to adopt agentic systems capable of reasoning and reaching into enterprise backend systems. For instance, in the context of data systems, agents will begin to autonomously tackle data systems design and operations tasks currently performed by engineers and analysts. For example, autonomously designing and validating novel data products based on demand, generating dataflow pipelines to discover, index, catalog, clean, and validate data for consumption by generative AI, and remediating dataflow issues as workload or resources evolve. Data systems will evolve to better address agentic data access patterns, which will be more iterative than current OLAP/OLTP queries, as the agents proactively assist users in refining their intent and the data to achieve their analytic goals. To address the cost overhead associated with data processing at enterprise scale, agents used at design time will produce efficient flows that minimize model and agent use at flow execution time.

Entire ecosystems of AgentOps will emerge, focusing on monitoring, debugging, and orchestrating agents at scale. Enterprises have embraced a hybrid platform and infrastructure strategy as fundamental to their IT strategy. Hybrid AI systems will integrate into hybrid applications, platforms, and infrastructure as agents will increasingly integrate with existing ecosystems via tools. To guarantee operational control, AgentOps will be integrated in existing observability and operations strategies and solutions.

Security concerns, including agent hijacking, prompt injection risks, credential theft, excessive permissions, tool manipulation, and insufficient monitoring and access controls will be mitigated via the use of ephemeral agent identities, just-in-time tokens, and delegation frameworks. Sandboxing, red/blue testing, and real-time policy enforcement will provide autonomous defense.