Why agentic AI could soon make ChatGPT look like a simple calculator

THE next wave of artificial intelligence won't just generate text, images, code and videos - it will make autonomous decisions and pursue goals.

As remarkable as tools like ChatGPT are, they represent just the beginning of AI's true potential.

Enter agentic AI - the next evolution of AI that will fundamentally change how machines interact with our world.

The key distinction between generative and Agentic AI lies in their approach to tasks and decision-making. Generative AI, which powers popular tools like ChatGPT, Google Gemini and Claude, works like an incredibly sophisticated pattern-matching and completion system. When you prompt it, it analyses vast amounts of training data to generate appropriate responses, whether that's writing a poem, creating an image, or helping debug code.

While this is hugely impressive, these systems are reactive; they respond to specific prompts without understanding context or long-term objectives.

Agentic AI operates with a degree of autonomy. These systems can set their own goals, develop strategies to achieve them and adapt their approach based on changing circumstances. Consider generative AI as a highly skilled assistant waiting for instructions, while agentic AI is more like a colleague who can take the initiative and work independently toward broader objectives.

For example, a generative AI might help you write an email when asked. In contrast, an agentic AI could proactively monitor your inbox, identify essential messages that need attention, draft appropriate responses based on past communications, and even schedule follow-up meetings -- while adapting its approach based on your feedback and changing priorities.

The building blocks of intelligence and purpose

What makes agentic AI truly revolutionary is its architecture. Unlike generative AI systems that primarily process and produce content based on patterns in training data, agentic systems incorporate several sophisticated components:

1. Planning modules: AI can decompose complex objectives into actionable steps, create contingency plans, and optimise actions.
2. Memory systems: Agentic AI utilises different types of memory:
   
   • Short-term memory for handling immediate tasks
   • Episodic memory for recalling specific events and interactions
   • Procedural memory for storing learned skills and processes
   • Long-term memory for maintaining persistent knowledge
3. Decision-making frameworks: These allow systems to evaluate options against multiple criteria, handle uncertainty, and make value-aligned choices.
4. Self-monitoring mechanisms: Agentic AI continuously evaluates its performance, identifies errors, and adapts strategies when current approaches are not yielding desired results.

This architectural complexity enables agentic AI to maintain context over extended periods, pursue objectives persistently, and operate more independently than today's systems.

The convergence of generative and agentic AI

We see the first signs of convergence between generative and agentic capabilities in mainstream AI tools.

OpenAI's recent introduction of scheduled tasks in ChatGPT represents an early step in this direction. This feature allows the AI to operate semi-autonomously, performing scheduled actions and maintaining ongoing responsibilities without constant user prompting. While still in its early stages, it points to a future where AI systems combine generative AI's creative and analytical capabilities with the autonomous decision-making of agentic AI.

The movement toward more agentic capabilities may accelerate, with recent reports suggesting various AI labs are exploring ambitious new directions.

According to Bloomberg reports, OpenAI has been rumoured to be working on a project called 'Operator', which could enable autonomous AI agents to control computers independently. Tech observers have also noted references to a project called 'Caterpillar' in OpenAI's systems, which some speculate might be aimed at enabling AI to proactively search for information, analyse problems, and navigate digital environments with minimal human oversight. These projects hint at broader ambitions for more autonomous AI systems.

Real-world applications and implications

The practical applications of agentic AI are potentially far-reaching and transformative. Imagine an AI system that doesn't just help schedule meetings but actively manages your entire workflow, anticipating bottlenecks, suggesting process improvements, and autonomously handling routine tasks without constant supervision. In manufacturing, agentic AI could manage entire production lines by following pre-programmed routines, actively optimising processes, and responding to unexpected challenges in real-time.

However, these advances also raise concerns about workforce displacement. As AI systems become more autonomous, specific roles focused on routine decision-making and process management may be transformed. This transition will require thoughtful approaches to workforce reskilling and creating new roles that leverage uniquely human capabilities alongside AI systems.

The future of human-machine collaboration

As agentic AI systems become more sophisticated, we will likely see a fundamental shift in how we interact with artificial intelligence.

Rather than simply issuing commands and receiving outputs, we'll develop more collaborative relationships with AI systems that can engage in genuine back-and-forth dialogue, propose alternative solutions, and even challenge our assumptions when appropriate.

This evolution could lead to unprecedented levels of human-machine synergy, where AI becomes less of a tool and more of a partner in problem-solving and innovation.

This shift may also introduce new challenges in human-machine dynamics. As AI systems gain more agency, questions about authority, responsibility, and appropriate deference between humans and machines will become increasingly important.

Developing frameworks for productive collaboration that respect human autonomy while leveraging AI capabilities will be critical.

Core challenges in agentic AI development

The development of agentic AI systems faces several fundamental challenges that require careful consideration:

Termination, confluence, and stability

Three classic problems in agentic AI systems require particular attention:

1. Termination: Ensuring agentic systems can reliably complete tasks without getting caught in endless loops or decision cycles. While single-pass approaches can address termination, they often compromise other aspects of performance.
2. Confluence: Guaranteeing that different agents working toward the same goal will converge on compatible or equivalent solutions rather than working at cross-purposes.
3. Stability: Maintaining consistent performance even as tasks, environments, and information change over time.

Current approaches to these challenges include mediator systems that oversee agent interactions, hierarchical agent architectures with clear lines of authority, and formal verification methods that mathematically prove specific properties of agent behaviour.

However, finding solutions that preserve genuine peer-to-peer agent autonomy while ensuring these properties remains an open challenge.

As agentic AI systems make more independent decisions, ensuring transparency in their reasoning becomes increasingly difficult yet crucial. Unlike simpler systems where outputs directly relate to inputs, agentic AI may make decisions based on complex chains of reasoning and accumulated knowledge that aren't immediately apparent to users.

It remains a significant technical challenge to develop mechanisms for these systems to explain their reasoning in human-understandable terms without oversimplifying the actual complexity of their decision processes.

Value alignment and safety

Ensuring agentic systems remain aligned with human values becomes more complex as their autonomy increases. These challenges include:

• Preventing goal distortion, where systems optimise for specified metrics in ways that undermine intended outcomes
• Handling conflicting human values and priorities appropriately
• Maintaining appropriate safety constraints even when pursuing ambitious objectives
• Preventing systems from developing emergent behaviours that designers didn't anticipate

Monitoring and measuring impact

How do we evaluate whether agentic AI systems are genuinely beneficial? Beyond simple efficiency metrics, we need sophisticated frameworks to assess their broader impact:

1. Innovation metrics: Can we distinguish between AI systems that generate new insights versus those that merely repackage existing information in more accessible forms?
2. Knowledge contribution: How do we measure whether AI systems expand human knowledge rather than create an echo chamber of existing information?
3. Human-AI synergy: What frameworks can help us evaluate whether these systems enhance human capabilities and well-being rather than simply replacing human activity?
4. Distributional effects: How do we monitor whether the benefits of agentic AI are broadly shared or concentrated among specific groups?

Developing robust impact assessment methodologies is essential to ensure agentic AI delivers on its promise of augmenting human capabilities rather than merely automating existing processes.

Shaping tomorrow's intelligence today

The shift from purely generative to more agentic AI represents a fundamental reimagining of what artificial intelligence can be. As these systems become more sophisticated and widespread, they have the potential to transform industries, enhance human capabilities, and open new frontiers in human-machine collaboration.

However, realising this potential requires more than technological innovation. It demands thoughtful governance frameworks, ongoing research into AI safety and alignment, and inclusive dialogue about how these technologies should be developed and deployed.

Call to action

As agentic AI systems evolve, their development should not be left solely to technologists. We all have a stake in ensuring these powerful technologies serve human flourishing:

1. Stay informed: Follow developments in agentic AI and understand their implications for your field or industry.
2. Participate in dialogue: Discuss increasingly autonomous AI systems' ethical and societal implications.
3. Advocate for responsible development: Support research and policies that promote transparency, safety, and beneficial applications of agentic AI.
4. Prepare for transition: Consider how your organisation or profession might change as agentic AI systems become more prevalent and develop strategies to leverage these technologies effectively.

The future of agentic AI is not predetermined. We can shape these technologies through thoughtful engagement and proactive governance to augment human capabilities, address pressing challenges, and create a more prosperous future.

Conclusion: Navigating the agentic frontier

The evolution from generative to agentic AI represents more than a technological upgrade - it marks a fundamental shift in how we conceptualise artificial intelligence and its role in society. At this inflexion point, we face extraordinary promise and profound responsibility.

Agentic AI systems will likely transform our relationship with technology in ways we're only beginning to understand.

They offer the potential to amplify human creativity, solve complex problems that have long resisted our efforts, and free us from mundane tasks that consume our time and attention. Yet their development also demands unprecedented vigilance to ensure they remain aligned with human values and priorities.

The most critical insight may be that agentic AI is not inevitably beneficial or harmful - our choices will determine its impact today. The architectural foundations, governance frameworks, and ethical guidelines we establish now will shape how these systems evolve and their role in our collective future.

What distinguishes this moment in AI development is the transition from tools we explicitly control to partners with whom we collaborate. Navigating this transition successfully will require new thinking about human-machine relationships, redefined notions of appropriate autonomy, and innovative approaches to maintaining human agency in a world increasingly shaped by intelligent systems.

The future of agentic AI is not predetermined. Through thoughtful engagement, proactive governance, and a commitment to keeping humans at the centre of technological development, we can shape these systems to augment our capabilities, address our most pressing challenges, and create a more prosperous future for all.

The calculator gave way to the computer; the chatbot gave way to the agent - but humans must remain the authors of our technological destiny.