AI agents have crossed the line that separates experiment from operation. They are no longer innovation demos — they are part of the workflow, and that changes the priorities of everyone who leads.
What changed
Gartner projects that 40% of enterprise applications will feature task-specific agents by the end of 2026, up from less than 5% in 2025. Market research already shows more than 70% of companies running agents in production rather than in pilots.
Where the bottleneck is
Adoption moved faster than control. Around 60% of organizations run agents without formal governance. In Brazil, Deloitte data shows 95% of companies plan to use agentic AI within two years, but only 27% have mature governance models.
Why it matters
An agent that executes tasks in sequence and triggers other systems expands autonomy — and, with it, responsibility. Without audit trails, action limits and human review at critical points, the productivity gain turns into operational, legal and reputational risk.
Entercast's read
Scaling agents is not just about choosing the model: it is about designing the flow, defining guardrails, instrumenting observability and training the team to operate alongside the machine. Those who treat governance as part of the project — not as a later patch — move from pilot to real operation.