AI agents vs automation
Uncover the key differences between AI agents and automation. Learn how each technology can improve workflows and drive smarter decisions for Australian businesses.
Agentic AI marks a turning point in enterprise transformation, shifting AI from reactive tools to autonomous or semi-autonomous systems that act, adapt, and orchestrate workflows. In this article, experts from Canon Business Services ANZ explain what Agentic AI means for CIOs and IT leaders navigating 2026. They explore how to build AI-ready infrastructure, embed governance at scale, redesign workflows for autonomy, and prepare the workforce for this next evolution. With real-world insights from CBS’ Business Analyst as a Service (BAaaS) and AI strategy teams, the article offers a clear roadmap for turning Agentic Artificial Intelligence systems from pilot to platform before your competitors do.
Agentic AI is more than the next evolution of artificial intelligence. It’s a complete rethink of how enterprises operate.
In 2026, that shift will challenge the foundations of cloud architecture, data governance, and workforce dynamics across the enterprise landscape.
According to Canon Business Services ANZ (CBS) experts, this isn’t a question of if. It’s a question of how ready your business is to embrace Agentic AI systems without compromising security, quality, or employee trust.
Agentic AI refers to artificial intelligence systems that act independently, execute tasks across multiple systems, and adapt in real time—with minimal human intervention.
Unlike generative AI, which is largely an input (natural language prompts) and an output (how large language models (LLMs) respond), Agentic AI acts. These autonomous or semi-autonomous AI-powered agents don’t just generate content or analyse data, they interpret signals, make decisions, trigger workflows, and adapt in real time.
“We’re shifting from AI as a support channel to AI as an operating model,” explains Raji Haththotuwegama, CBS’s National Solutions Advisor, AI Business.
Where GenAI interacts, answering prompts and generating outputs, Agentic AI orchestrates. It operates with purpose, moving beyond prediction into action. These AI agents can:
“This evolution redefines AI as part of the enterprise nervous system,” Raji says. “Where Generative AI sat on the surface, Agentic AI embeds itself into the workflow fabric.”
Unlike traditional AI models that require human intervention to drive actions, agentic AI systems enable agents to tackle complex challenges across departments and data layers.
The shift toward Agentic AI systems impacts three major business dimensions:
“Agentic workflows signal a shift from process-driven organisations to self-optimising enterprises, where operations learn and adapt continuously,” Raji explains. This isn’t just automation. It’s transformation.
And unlike traditional AI approaches, Agentic AI systems learn and evolve, meaning enterprises can now deploy multi-agent systems to coordinate across departments—an approach that supports complex tasks like analysing market data, managing incident response processes, or streamlining software development.
The typical enterprise infrastructure stack isn’t built for autonomous agents that act continuously and unpredictably. Agentic systems expose existing weaknesses in three key areas:
“If you don’t modernise these three layers, Agentic AI will remain stuck in pilots,” he says. “But with them, it can become an enterprise-wide capability.”
Autonomy without oversight is a recipe for risk.
“You’re letting systems make decisions and take action. That comes with serious governance implications,” says Daniel D’Souza, CBS’ Head of Information Security Solutions. “This isn’t just about keeping sensitive data safe. It’s about making sure AI doesn’t act outside of your business risk appetite."
Governance for Agentic AI should therefore be embedded, not bolted on. That means:
“Cutting corners on governance might save time upfront, but it almost always leads to bigger problems down the road,” Daniel notes.
Across many organisations, employees are beginning to fall into two distinct groups when it comes to AI adoption: those diving in without clear guidelines, sometimes misusing the technology by exposing sensitive or diverse data or systems, and those holding back out of caution, missing out on efficiency gains and falling behind in fluency with natural language processing or other AI capabilities.
Without proactive education and structured guardrails, both approaches can limit the safe and effective use of AI in the workplace.
“The Replit database wipe in 2025 is a cautionary tale,” Raji agrees. “Without guardrails, one agent made a catastrophic call: no rollback, no oversight. That can’t happen in enterprise environments.”
Strong governance starts with strong data foundations.
“Data governance is your first line of defence,” says Daniel. “If you don’t know where your data is, who can access it, or how it’s used, you’re creating blind spots, both for attackers and for your AI.”
This means:
So, what does AI-ready infrastructure really look like? Raji breaks it down into five foundational shifts:
“Without these shifts,” he says, “Agentic AI will underperform, or worse, introduce risk. With them, infrastructure becomes the foundation of an autonomous or a semi-autonomous enterprise.”
Autonomy doesn’t mean removing humans. It means repositioning them.
“Agentic AI frees up human capacity for higher-value work,” says Annick De Silva, a product leader for Solutions at CBS. “But to unlock that, you need to map the workflows clearly.”
That’s where CBS’s Business Analyst as a Service (BAaaS) offering comes in.
“We don’t just document workflows, we stress-test them,” she says. “We identify where handoffs happen, where delays occur, and where AI agents can make a measurable impact.”
Effective process mapping does three things:
“Process data mapping becomes the bridge between today’s operations and tomorrow’s autonomous workflows,” Annick explains. “The most overlooked issue? Cultural readiness. If teams don’t understand how decision-making shifts when AI enters the workflow, adoption will fail, even if the tech works perfectly. AI agents learn, yes, but your people also need to learn how to guide them.”
Canon Business Services ANZ takes a holistic view of AI transformation, not as a software development project but as a business model shift.
CBS supports clients by:
“Our goal is to help clients move beyond GenAI experimentation,” says Raji. “Agentic AI isn’t just a feature. It’s an operating system for the future.”
Agentic AI won’t wait. As the technology matures, early adopters are already redesigning how work happens and what their organisations can deliver.
To lead in these dynamic environments, CIOs and business leaders should:
When empowered, people and AI agents can collaborate to adapt in real time. That’s what makes Agentic AI a business capability, not just a tech investment.
As Annick puts it: “Agentic AI is only as powerful as the environment it runs in, and the humans who guide it.”
The time to build that environment is now.
At Canon Business Services ANZ, we help forward-thinking organisations lay the foundations for autonomous operations, combining cloud, data, governance, and business process expertise to turn Agentic Artificial Intelligence systems from possibility into performance.
Whether you’re mapping workflows, modernising infrastructure, or designing governance frameworks, our team can guide you from pilot to platform safely, strategically, and at scale.
Ready to modernise for the agentic era? Get in touch to explore how our AI, cloud, and Business Analyst as a Service (BAaaS) offerings can accelerate your transformation.
Agentic AI refers to a new generation of AI systems that can act autonomously or semi-autonomously to complete tasks, make decisions, and adapt to real-time feedback, often with minimal human intervention. Unlike traditional artificial intelligence, which typically requires direct input and narrowly defined parameters, Agentic AI systems can monitor events, interpret signals, and trigger workflows independently. This makes them ideal for handling complex processes that evolve over time, such as supply chain orchestration or dynamic pricing.
Generative AI, powered by large language models (LLMs), excels at creating content and responding to natural language prompts. However, it remains reactive, waiting for user input. Agentic AI, by contrast, is proactive. It can perform specific tasks across systems without being prompted each time. While LLMs may be one component within an agentic AI model, the real distinction lies in how AI agents behave: Agentic AI acts, orchestrates workflows, and learns from outcomes in context.
Agentic AI work is best applied to high-volume, structured, and repetitive tasks that still require situational awareness or adaptation. Examples include processing insurance claims, managing service-level escalations, or coordinating logistics in real time. These AI agents can also assist in analyzing data, adapting to complex scenarios, and solving complex problems faster than human teams alone—especially when multiple agents are used in tandem (multiple AI agents) to coordinate outcomes.
By enabling AI agents to take on routine, logic-based coding tasks (such as testing, bug triaging, or deployment workflows) Agentic AI can significantly streamline development cycles. This not only reduces cognitive load but also helps in boosting developer productivity. When integrated with reinforcement learning and machine learning, these agents continuously improve their performance, learning from real-world outcomes to reduce errors and support agile innovation.
