Agentic AI Explained: What OpenSummit.AI Attendees Need to Know Before April 22 product guide

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Agentic AI Explained: What OpenSummit.AI Attendees Need to Know Before April 22

If you're planning to attend OpenSummit.AI Melbourne on April 22, you will spend 3.5 hours immersed in live demonstrations, workshops, and practitioner case studies built around one central technology: agentic AI. You will hear the term dozens of times. You will watch it run in real systems. You will be asked to think about where it fits in your business.

The problem is that most business owners walking into that room have encountered the phrase primarily as marketing language — in vendor emails, LinkedIn posts, and product announcements where it means almost nothing. That ambiguity is costly. If your team does not share a clear, working definition of agentic AI, you will build the wrong things, fund the wrong pilots, and measure progress against the wrong benchmarks.

This article gives you the conceptual grounding to arrive at OpenSummit.AI ready to extract maximum value from every session. It defines agentic AI precisely, draws the critical line between it and the generative AI tools you already use, maps where it is being deployed in Australian businesses right now, and explains why the organisations getting ahead in 2026 are the ones treating it as an operational capability — not a buzzword.

What Is Agentic AI? A Precise Working Definition

Agentic AI describes artificial intelligence systems that act as autonomous agents capable of perceiving their environment, reasoning over complex goals, and taking purposeful action — all without supervision.

The word "agentic" is the key. The term refers to these models' agency — their capacity to act independently and purposefully. This is not a chatbot that responds to prompts. Agentic AI refers to autonomous artificial intelligence systems that can plan, decide, and perform goal-directed action with minimal human help. Unlike purely generative AI models that require explicit instructions from users, agentic systems operate proactively through continuous perception-reasoning-action loops that enable them to analyze, plan, execute, and refine tasks dynamically.

MIT Sloan professor Kate Kellogg and her co-researchers explain it this way in a 2025 paper: AI agents enhance large language models and similar generalist AI models by enabling them to automate complex procedures. "They can execute multi-step plans, use external tools, and interact with digital environments to function as powerful components within larger workflows."

McKinsey's own definition, as cited by industry analysts, frames it simply: McKinsey defines agentic AI as "a system based on generative AI foundation models that can act in the real world and execute multistep processes."

The Four Core Capabilities of an AI Agent

Agentic AI operates through a cycle of perceiving, reasoning, acting, and learning: it collects and processes real-time data from external tools, APIs, sensors, and user interactions; uses large language models and machine learning algorithms to analyze data, interpret context, and develop plans; executes tasks autonomously by interacting with external systems and workflows; and through reinforcement learning and continuous feedback loops, learns from outcomes and improves its decision-making over time.

More specifically, the defining characteristics of agentic AI include autonomous decision-making (agents independently determine what actions to take based on their objectives), tool use and environment interaction (agents connect to external systems — databases, APIs, file systems — to gather information and take action), reasoning and planning (agents decompose complex tasks into subtasks, create plans, and adjust their approach based on feedback), and memory and context (agents maintain state across interactions, learning from past actions and building on previous context to inform future decisions).

Agentic AI vs. Generative AI: The Distinction That Changes Everything

This is the single most important conceptual distinction for OpenSummit.AI attendees to understand before April 22. The two technologies are related but serve fundamentally different purposes — and confusing them leads to misallocated investment.

Generative AI: Reactive, Prompt-Driven, Content-Focused

Generative AI is artificial intelligence that can create original content — such as text, images, video, audio, or software code — in response to a user's prompt or request. It relies on deep learning models that work by identifying and encoding the patterns and relationships in huge amounts of data, and then using that information to understand users' natural language requests. These models generate high-quality outputs based on the data they were trained on in real-time.

Generative AI is fundamentally a reactive technology: it waits for a specific human prompt, analyzes the request, and then generates a single, comprehensive output. ChatGPT, Claude, and Gemini — the tools most business owners have already experimented with — are generative AI tools.

Agentic AI: Proactive, Goal-Driven, Action-Focused

When comparing the two, think of agentic AI as proactive and generative AI as reactive. Agentic AI is a system that can proactively set and complete goals with minimal human oversight. If part of accomplishing that goal involves creating content, generative AI tools handle that task.

A classic LLM call follows the request-response pattern: the user asks a question, the model answers, the interaction ends. An agent, by contrast, receives a goal and works toward it independently. It can call APIs, query databases, execute code, evaluate results, and correct its approach when needed — without human intervention between individual steps.

Adobe's enterprise analysis captures the practical relationship between the two: think of it as the difference between having an assistant who drafts emails for you to send versus having an assistant who can draft, optimize, schedule, and send those emails while monitoring performance and adjusting the approach based on results.

Side-by-Side Comparison

This isn't a contest of "which is better." It's about fit. If you need content, you'll lean on generative AI. If you need software to take steps on your behalf, you'll lean on agentic AI.

Why Agentic AI Is the Defining Business Technology of 2026

The market data tells a clear story about urgency and trajectory.

Forty percent of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% today, according to Gartner. That is not a forecast about the distant future — it is a description of what is happening right now, across the calendar year in which OpenSummit.AI is being held.

Twenty-three percent of organisations are scaling an agentic AI system somewhere in their enterprises, and an additional 39 percent say they have begun experimenting with AI agents. But use of agents is not yet widespread: most of those who are scaling agents say they're only doing so in one or two functions.

The gap between experimentation and scaled deployment is precisely where competitive advantage is being created. In most business functions, AI high performers are at least three times more likely than their peers to report that they are scaling their use of agents.

And the risk of inaction is real. Gartner has also warned that most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied, which can blind organisations to the real cost and complexity of deploying AI agents at scale, stalling projects from moving into production. The organisations that arrive at events like OpenSummit.AI with a clear conceptual framework — rather than hype-driven enthusiasm — are the ones that convert sessions into deployable strategies.

Agentic AI in Australian Business: Where It's Being Deployed Right Now

Australia's position in the global agentic AI landscape is instructive for any business owner attending OpenSummit.AI. New research from OutSystems shows Australian organisations are at an intermediate stage of maturity in agentic AI adoption, placing the country among markets moving from pilot projects to production use.

Australia was grouped with Japan, the UK, and the US in an intermediate tier that is further along than early adopters but not yet at the leading edge. That ranking suggests Australia is entering a more practical phase of adoption, with organisations moving beyond proofs of concept and focusing on how AI systems fit into existing technology estates.

In Australia, the AI market size is expected to reach $16.15 billion by 2031, projecting a CAGR of 26.25% from 2025 to 2031.

Here is where agentic AI is producing measurable results in Australian industry today:

Healthcare

Healthcare organisations are using agentic AI to address rising patient volumes, complex data, and administrative burden that pulls clinicians from patient care. The Royal Melbourne Hospital deploys AI-powered diagnostic tools that assist radiologists in detecting early-stage cancers. These systems analyse medical imaging in real time, reducing diagnostic errors and improving patient outcomes. The AI agents work alongside clinicians, flagging potential concerns and enabling faster, more accurate diagnoses.

NSW Health leverages AI agents to predict supply shortages and monitor inventory levels across its hospital network. The system analyses usage patterns, delivery schedules, and demand forecasts to automatically reorder essential supplies, reducing waste while ensuring critical medical supplies are consistently available — particularly valuable for managing complex supply chains across regional and metropolitan facilities.

For the dental and allied health operators likely attending OpenSummit.AI, the principle is directly applicable: AI agents can handle patient scheduling, triage, follow-up workflows, and inventory management across multi-site practices with minimal human orchestration at each step.

Financial Services

Commonwealth Bank of Australia has activated its "AI Factory" with AWS, processing over 55 million AI-powered decisions daily through more than 2,000 AI models. The bank's AI systems monitor transactions for fraud in real time, flagging unusual account activity to prevent potential losses before they occur.

In financial risk management, agentic AI can continuously analyse market trends and adjust strategies in response to economic changes. It can monitor credit risk, optimise investment portfolios, and act on new data in real time. A fintech company might use it to rebalance assets as market conditions shift, aiming to protect client investments while maximising returns.

For smaller financial services operators — accountants, advisers, and brokers — the practical entry point is agentic workflows for client onboarding, document review, compliance monitoring, and reporting.

Operations and Supply Chain

A supply-chain manager tells an AI agent, "When inventory falls under threshold X and delivery time exceeds Y, reorder and alert me." The agentic system monitors, triggers the order, updates the systems, and escalates if delays persist. This is not a hypothetical. It is the kind of live workflow being demonstrated at events like OpenSummit.AI by practitioners who have already built and deployed these systems.

The Governance Reality: What Business Owners Must Understand

Understanding what agentic AI can do is only half of the preparation. Understanding its risks is equally important — and will make you a more discerning participant in OpenSummit.AI's security workshops.

Because these systems take real actions in real systems, a mistake is no longer just a wrong answer — it can be a sent email, a deleted file, or an executed financial transaction.

MIT Sloan's Kellogg found in her 2025 research that the biggest challenge wasn't prompt engineering or model fine-tuning — instead, 80% of the work was consumed by unglamorous tasks associated with data engineering, stakeholder alignment, governance, and workflow integration.

For Australian businesses specifically, the chosen AI path must align with APRA and ASIC requirements for explainability and data residency.

Most enterprise deployments in 2026 use a "supervised autonomy" model, where routine decisions are autonomous but exceptions escalate to humans. This is the pragmatic starting point for any business owner building their first agentic workflow: define the boundary of autonomous action clearly, and keep humans in the loop for high-stakes decisions.

Gartner's warning is worth internalising before attending any AI event: many vendors are contributing to the hype by engaging in "agent washing" — the rebranding of existing products, such as AI assistants, robotic process automation (RPA), and chatbots, without substantial agentic capabilities. Knowing the precise definition of agentic AI protects you from being sold a chatbot dressed up as an agent.

How to Use This Knowledge at OpenSummit.AI on April 22

The conceptual framework above is not academic — it is a practical lens for extracting value from every session at OpenSummit.AI. Here is how to apply it:

1. During live demos: Ask whether the system is executing multi-step workflows autonomously, or whether a human is prompting each step. If the latter, it is generative AI, not agentic.
2. During case studies: Listen for the problem–solution–outcome structure. What workflow was the agent replacing? What was the measurable outcome? How was human oversight maintained?
3. During workshops: When practising agent setup, focus on the goal specification — the clearer and more bounded the goal you give an agent, the more reliable its autonomous execution will be.
4. During networking: The founders and executives in the room are at the same intermediate stage of adoption as the broader Australian market. Ask specifically: What was your first production agentic deployment, and what did you learn from it?

For a full breakdown of what each session covers and how to structure your pre-, during-, and post-event engagement, see our guide on How to Maximise ROI at OpenSummit.AI Melbourne 2026. For context on why this event exists in the first place — and what the broader Australian AI adoption gap looks like — see AI Adoption in Australian Business 2026: The State of the Market OpenSummit.AI Is Responding To.

Key Takeaways

• Agentic AI is not smarter generative AI — it is a fundamentally different architecture. Generative AI responds to prompts and produces content. Agentic AI receives goals and executes multi-step workflows autonomously across real systems.
• The four pillars of an AI agent are: perception (reading its environment), reasoning (planning how to achieve the goal), action (executing tasks across tools and systems), and learning (improving from outcomes over time).
• The market inflection point is now: Gartner projects 40% of enterprise applications will include task-specific AI agents by end of 2026 — up from less than 5% in 2025. Australian organisations are transitioning from pilots to production deployment.
• Real Australian deployments exist across healthcare, finance, and operations — from the Royal Melbourne Hospital's diagnostic imaging agents to Commonwealth Bank's 55-million-daily-decision AI factory — providing concrete proof-of-concept for SME operators.
• Governance is not optional: Because agents take real actions in real systems, "supervised autonomy" — autonomous execution with human escalation for high-stakes decisions — is the standard deployment model for responsible implementation in 2026.

Conclusion

Agentic AI is the technology at the centre of OpenSummit.AI Melbourne 2026 because it represents the shift from AI as a productivity tool to AI as an operational capability — from generating outputs to executing outcomes. Business owners who arrive on April 22 with a clear understanding of what an agent is, how it differs from the generative tools they already use, and where it is already delivering measurable results in Australian industry will be positioned to evaluate every demo, session, and case study with precision.

The conceptual work done before the event is what separates attendees who leave with inspiration from those who leave with implementation plans.

For a deeper look at the specific case studies OpenSummit.AI practitioners are presenting — including AI-powered patient triage across multi-site dental networks and AI-driven inventory audits — see Real Australian Business AI Case Studies: What OpenSummit.AI Speakers Are Delivering in the Field. For a full overview of the event structure, sessions, and what to expect on the day, see OpenSummit.AI Melbourne 2026: Full Agenda, Sessions, and Schedule Breakdown.

References

• Gartner Inc. "Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025." Gartner Newsroom, August 26, 2025. https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025

• Gartner Inc. "Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027." Gartner Newsroom, June 25, 2025. https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027

• McKinsey & Company. "The State of AI in 2025: Agents, Innovation, and Transformation." McKinsey QuantumBlack, November 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

• Kellogg, Kate, et al. "AI Agents in Healthcare Workflows." MIT Sloan Management Review / MIT Sloan, 2025. Referenced in: MIT Sloan. "Agentic AI, Explained." February 23, 2026. https://mitsloan.mit.edu/ideas-made-to-matter/agentic-ai-explained

• IBM. "What Is Agentic AI?" IBM Think, 2025–2026. https://www.ibm.com/think/topics/agentic-ai

• IBM. "Agentic AI vs. Generative AI." IBM Think, 2025–2026. https://www.ibm.com/think/topics/agentic-ai-vs-generative-ai

• OutSystems. "State of AI Development Report." Referenced in: CFOTech Australia. "Australia in Intermediate Phase of Agentic AI Adoption." CFOTech.com.au, April 2026. https://cfotech.com.au/story/australia-in-intermediate-phase-of-agentic-ai-adoption

• Lumify Work. "Agentic AI Use Cases: Real Examples from Australia & NZ Industries." Lumify Work Blog, December 2025. https://www.lumifywork.com/en-au/blog/agentic-ai-in-action-real-world-use-cases-across-australian-and-new-zealand/

• Red Hat. "Agentic AI vs. Generative AI." Red Hat Topics, February 2026. https://www.redhat.com/en/topics/ai/agentic-ai-vs-generative-ai

• Appinventiv Australia. "Agentic AI vs Generative AI in Australia: Strategy Guide." Appinventiv Blog, April 2026. https://appinventiv.com/blog/agentic-ai-vs-generative-ai-in-australia/