Modern enterprise automation is transitioning from rigid, scripted workflows to agentic systems that use observability data and AI to detect anomalies, exercise judgment and self-correct, writes Julie Banfield, an IBM technical product manager and data scientist. Find out how long it will be before these autonomous agents achieve mainstream reality.

In other technology news we’re covering today:

• Most organizations focus on improving model intelligence, but enterprise AI success actually depends on the structural quality of the APIs that agents must use to execute actions, according to Frank Kilcommins, who heads enterprise architecture at AI infrastructure company Jentic. In this contributed post, Frank explains the six dimensions of API AI-readiness.
• As organizations move into 2026, they must transition from ad hoc AI experimentation to strategic integration. This means prioritizing centralized governance, unified data architectures and end-to-end agentic systems, writes Michelle Gill, who heads DevOps engineering at GitLab. In this sponsored post, Michelle explains why AI success will require shifting away from simple task automation to reimagining core business processes.

Cloud spend compounds quietly. Shift left by building cost control and governance into engineering workflows. Join us on January 15 to learn how to keep Kubernetes aligned, eliminate drift, and enforce best practices by default.