The useful question is what changes for users, developers or buyers, and whether the announcement stays industry context or becomes something people can actually use.

Agentic workflows represent a departure from traditional automation by enabling AI agents to operate autonomously across multiple steps, rather than responding to single prompts or rigid sequences. These systems chain reasoning, tool use, and feedback into a continuous loop, allowing organizations to complete complex objectives with minimal human intervention. The distinction from generative AI lies in their ability to adapt decisions based on context and intermediate results, rather than relying on static rules or predefined outputs.

The architecture of agentic workflows consists of several components that enable autonomous execution, including data ingestion, reasoning engines, tool integration, and orchestration layers. Unlike conventional automation, which fails or succeeds in a single pass, agentic workflows evaluate context at each step, adjust their approach when unexpected conditions arise, and self-correct to handle edge cases. This iterative structure supports processes that involve ambiguity, changing conditions, or coordination across multiple systems.

Organizations are projected to rapidly adopt agentic workflows, with Gartner estimating that 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024. The shift reflects a move toward software that acts on behalf of users rather than merely responding to commands. While existing models, data pipelines, and governance frameworks can be repurposed, the primary change involves reorchestrating these components to enable planning, decision-making, and autonomous action within workflows.

Despite their potential, agentic workflows introduce governance challenges, particularly around auditability, explainability, and alignment with organizational policies. Deloitte’s 2026 State of AI report highlights a gap between adoption intent and governance readiness, with only 21% of companies reporting mature agent governance models. Production-grade governance remains an active area of development as enterprises seek to mitigate risks associated with autonomous decision-making in critical processes.