Large Australian banks and enterprises are deploying agentic AI at scale in 2026, transitioning from pilot programs to full production. The move reflects a broader trend: Gartner estimates 40% of enterprises will integrate AI agents into applications by the end of 2026, a significant increase from under 5% in 2025.
Most governance teams rely on traditional approaches—committees, policies, and periodic audits. These methods assume human oversight and centralized control, neither of which applies to AI agents.
The playbook is already outdated
Governance models designed for human decision-making fail in environments where agents operate autonomously. Policies drafted today may become obsolete before implementation.
Three key changes are necessary to adapt.
Visibility remains a core challenge
Enterprises struggle to measure AI agent behavior. What defines normal activity? How can drift from intended scope be detected? Incidents often result not from a single breach but from hundreds of micro-decisions leading to unintended outcomes.
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A 2026 Gravitee survey revealed only 24.4% of organizations have full visibility into agent communications. Yet 88% reported confirmed or suspected security incidents in the past year. Despite this, 82% of executives believe existing policies protect against unauthorized actions.
The gap isn’t due to oversight. Teams apply the same rigor used for traditional systems, but those systems weren’t built to detect AI-specific behaviors.
Without measurable data, policies lose meaning. Auditing becomes impossible when decisions can’t be investigated or understood. Observability requires instrumentation, baselines, and anomaly detection—tools most enterprises are still developing.
Governance must match machine speed
Scale introduces another hurdle. Salesforce’s 2026 Connectivity Report found the average enterprise runs 12 agents. Leading deployments, like IQVIA’s 150+ agents, make human oversight impractical.
The solution isn’t a new security framework but one adapted for non-human interactions. The only way to ensure this is to use AIs to govern other AIs, because it is not economical to rely solely on humans to do the work. This model must monitor agentic behaviour and respond within milliseconds when needed. It must provide situational awareness and key insights to support informed decision-making. Humans are responsible for establishing the parameters and guardrails, but they only intervene on demand and spend most of their focus on continuously improving governance capabilities.
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This approach demands real-time insights. Without them, governance collapses under complexity.
The transition resembles cloud adoption, where early governance investments reduced risks and created advantages. AI agents differ in speed, autonomy, and scale.
Vendors provide governance building blocks, but enterprises must develop custom AI-powered capabilities. Governance teams need engineering skills and environments integrated with security and compliance tools.
Professionals leading this shift have the expertise. They lack confidence in abandoning centralized control. The old model won’t scale, and retrofitting governance later will prove costly.
The time to establish a governance framework is now.
