In the ever-growing hype around artificial intelligence, one of the biggest misconceptions being marketed today is the idea that Agentic AI—AI systems capable of operating with full autonomy and adaptability—is already within reach for everyday businesses. The promise of AI agents that can think, reason, and make decisions without human oversight is captivating, but the reality is far less advanced.
In truth, what most businesses are deploying today under the label of “AI agents” are not truly autonomous systems but rather highly specialised task executors. These AI-powered tools can automate predefined functions, but they are fundamentally limited in their ability to act independently beyond the scope of their programming. The best analogy for these so-called agents? Task monkeys.
AI agents as task monkeys
When businesses attempt to integrate AI agents into their workflows, they aren’t unleashing intelligent, self-directed entities capable of nuanced decision-making. Instead, they are training digital monkeys to complete specific, repetitive tasks. These monkeys do not improvise or adapt beyond their defined parameters—they simply follow orders.
Here’s how the process typically unfolds:
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You train a monkey to do a specific task – In AI terms, this means you program an agent to execute a narrowly defined function, such as document summarisation or term extraction in legal processes.
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The monkey completes the task – The AI agent processes inputs, applies its learned patterns, and delivers outputs. But crucially, it does not understand the broader context of the task or deviate from its instructions.
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You reward the monkey for doing the task correctly – AI models, particularly those using machine learning, rely on reinforcement mechanisms like feedback loops and fine-tuning to improve performance. However, these improvements remain task-specific.
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The monkey can be trained to do different tasks – While AI agents can be retrained for additional functions, each new task requires deliberate intervention and reprogramming. The monkey doesn’t evolve into a fully independent thinker; it simply learns new tricks under controlled conditions.
Why true Agentic AI is still out of reach
The gap between today’s task-oriented AI and the vision of fully autonomous Agentic AI is vast. Businesses currently rely on structured processes and standardised outputs to mitigate risk, particularly in industries like legal services, finance, and compliance. If AI agents were to make independent decisions without oversight, the risk of incorrect, biased, or legally non-compliant outcomes would skyrocket.
For instance, an AI agent tasked with document summarisation in a legal setting is not making judgement calls about the significance of different clauses—it is following a programmed method to extract predefined key points. Similarly, term extraction agents operate within rigid parameters, ensuring consistency and predictability. This controlled approach is necessary because businesses need reliability, not AI-driven improvisation.
The Future: Smarter task monkeys, not autonomous minds
As AI technology advances, task monkeys will undoubtedly become more sophisticated. They will handle increasingly complex workflows, integrate better with human decision-making, and reduce manual labour in more nuanced ways. But the fundamental principle remains the same: these systems are designed to perform tasks, not to think for themselves. However, their real value emerges when they are embedded into end-to-end processes, ensuring efficiency and consistency across entire workflows. By integrating these AI-driven tools into structured systems, businesses can maximise their benefits while maintaining necessary oversight and control.
True Agentic AI—the kind that makes independent decisions without human intervention—requires breakthroughs in reasoning, adaptability, and contextual awareness that today’s AI simply does not possess. Until those breakthroughs occur, businesses must remain grounded in the reality that AI agents are not free-thinking entities but rather well-trained monkeys performing defined tasks within a larger system.
Conclusion
The marketing noise around Agentic AI often oversells its current capabilities, making it seem like businesses can deploy fully autonomous digital workers today. In reality, AI agents remain highly structured, task-specific tools that function within tightly controlled environments. The key to successful AI integration is to recognise these limitations, leveraging AI for process automation while maintaining the necessary oversight to ensure accuracy, compliance, and business value.
So, the next time you hear about an AI agent that claims to operate autonomously, ask yourself: is this truly an intelligent agent, or just another well-trained monkey performing its assigned task?
