The agentic AI shifts of 2026: Orchestration, governance and scale

Yes, AI is everywhere, and to no one’s surprise it will continue to be in 2026. But while the past few years have been dubbed the ‘AI era’ by many, we need to remember that this first AI wave was actually more about trial and error. The years since ChatGPT made generative AI readily available to everyone was really an era of pilots and prototypes.

McKinsey reports that approximately 80% of companies use generative AI, and yet most still aren’t seeing a material earnings contribution, because scaling practices and operating models lag the hype.

This gap will likely start being filled this year. 2026 is set to become the year where we move beyond the pilot era; the year where we focus on orchestration, governance and scale, particularly in the field of agentic AI. But that’s if Australian organisations and tech leaders are ready to lean into the following five AI shifts.

1. Agentic AI orchestration: empower people to become ‘managers of agents’, not just prompts

We are seeing the centre of gravity shift from single-shot prompts to multi-agent workflows that plan, call tools, verify and hand off to humans where it counts.

This means that organisations will need to redesign processes to help people manage agents. Defining new roles like ‘Agent Ops Lead’ and ‘AI Product Owner’ for example will be key. It will also be important to empower those new ‘managers of agents’ with runbooks, approval steps and dashboards tied to real business KPIs.

2. From isolated bots to a connected AI agent workforce

This year, AI will move past the era of ‘brittle’ custom plugins and one-off integrations. We are entering the age of interoperable AI.

Instead of risky, manual connections between AI and the organisation’s data, new protocols such as Model Context Protocols (MCPs) are allowing AI assistants to access apps and tools with strict permissions, ensuring data is handled with full oversight and auditability.

On the other end, seamless teamwork between agents (commonly called Agent-to-Agent) is becoming a reality. Different assistants — even those from different vendors — will soon be able to delegate tasks to one another, verify each other’s work and coordinate complex projects across departments.

All this reinforces my earlier point about the need to empower teams to become managers of agents and not just prompts.

3. Smaller is better

While the trend in AI has often focused on building ever-larger and more powerful models, there is value in recognising the benefits of smaller, more specialised models. These compact models can be trained or fine-tuned on a company’s own data, making them highly relevant to specific business needs and internal processes.

Because they require less computational power and storage, smaller models are also less expensive to run and can be deployed in a wider range of environments. This allows for faster, more secure and more private AI operations, as sensitive data can remain within the organisation’s own infrastructure rather than being sent to the cloud or third-party providers.

4. Security, governance and controls harden: build it as part of your AI

Regulatory clarity is no longer theoretical, and like most other countries, Australia is narrowing in on stricter compliance requirements when it comes to data security and privacy. In particular, 2026 will mark the first-ever security compliance sweep by the Office of the Australian Information Commissioner, while the recently released Australia’s National AI plan has put AI safety as a priority on businesses’ responsibility list. All of these raise the bar for risk management, procurement and rights-impacting use.

For enterprises, this means re-evaluating their AI foundations, looking at the ‘AI plumbing’ by updating retrieval, governance and audit systems to match new regulations and threats. Instead of building complex systems from scratch, teams should adopt platforms where incident reporting, data-handling rules and human-in-the-loop controls are already built-in.

This is ultimately what will also deliver what organisations are in demand of: AI systems that are transparent, auditable and fair, especially in regulated or sensitive industries. For example, consider a financial services firm that must comply with strict regulatory requirements. Instead of deploying a black-box AI model for loan approvals, the company implements an AI platform that provides clear explanations for each decision, maintains a complete audit trail and allows internal and external reviewers to trace every step in the process.

5. Agentic browsing enters the enterprise, starting with low-risk, high-value pilots

The browser itself is becoming an agentic workspace. Recent launches include Perplexity’s Comet and OpenAI’s ChatGPT Atlas. For business, this opens up curated pilots.

A key to success in this field will be to start with pilots such as competitive intel digests, procurement pre-reads or research watchlists, using guardrails and audits before expanding to sensitive domains. The technology is here, but the level of controls to determine what helps or hurts is paramount here.

Embracing these shifts and following these recommendations is what can help organisations turn pilots into platforms, allow compliance to become a force multiplier and empower agents to evolve from clever demos to accountable teammates. It is about adopting a more disciplined and strategic approach to building and deploying AI tools and particularly AI agents. The focus needs to be on shipping quick wins now, compounding them on a trusted foundation and only scale AI that reliably supports, innovates and transforms the business.

Image credit: iStock.com/tadamichi