Agentic automation is the latest wave of AI technology promising to transform our lives with much-needed capabilities and desirable qualities to help us better navigate everyday tasks.
Unlike traditional automation, AI agents can plan, reason, adapt, predict and coordinate multiple tasks in pursuit of a defined goal. These capabilities are reshaping everything from marketing operations to financial analysis.
I can’t help but compare some of these valuable attributes to those of a woman, which is why, as part of International Women’s Day, I’ve chosen to take a deeper look at agentic AI and how some of its traits seem typically female.
Planning
The fact that a staggering 81% of workers in the event planning industry are female is truly a testament to the innate resourcefulness and expertise that females have in planning and organising. And this goes beyond the office, with recent research showing women schedule 71% of family tasks.
In the same way, AI agents use large language models (LLMs) to achieve a high-level goal (such as preparing a marketing campaign) and determine the most logical and efficient order to carry it out. It can evaluate multiple possible paths and select the one that best balances constraints like cost, speed, and risk.
The LLMs will address the master plan by establishing manageable sub-tasks like data gathering, content creation and performance analysis. If an agent encounters a “closed road” or a delayed team member, it can engage in replanning - automatically recalculating the downstream impact and suggesting a new path forward - just like the female worker jumping into action to find emergency childcare when her sick child is sent home from school just before an important presentation.
Multi-Tasking
Is it a myth that women are better at multi-tasking than men? A study in Human Physiology found men require more brainpower than women when multi-tasking as the female brain does not need to mobilise extra resources in switching attention. Women were better apt to jump between incoming emails, phone calls, and assignments, while running in and out of meetings.
Fortunately, AI agents have no problem with switching tasks either – thanks to their ability to decompartmentalise. The agent can simply shift from ‘single thread’ processing to Multi-Agent Systems (MAS) to work simultaneously on different parts of a problem.
In fact, a ‘boss girl’ agent, or “orchestrator,” can take control by coordinating lower-tier specialised agents, assigning roles and synchronising their outputs into a final result. This can reportedly improve performance by over 80% on tasks like financial reasoning – where agents were able to simultaneously analyse revenue trends, cost structures and market comparisons.
Context Awareness
The well-known phrase of ‘call it women’s intuition’ didn’t develop out of nowhere. A woman’s heightened, instinctive ability to perceive, understand, or sense situations and emotions rapidly has often acted as a self-preservation mechanism as well as a powerful tool for gaining insight. According to scientists, the female brain is optimised for rapid intuitive decision-making. A woman’s corpus callosum, the white matter that connects our left and right brain hemispheres, is thicker than a man’s. This gives women better and faster abilities to access each hemisphere, further integrating their emotions and gut feelings with the more logical decision-making functions of the left hemisphere. In the business world, this can be translated into interpreting situational nuances such as customer sentiment or real-time market data to determine the best next step.
Similarly, AI agents can interpret environmental cues, such as time, location, or user sentiment, to adjust their responses in real-time. However, they can’t always do it alone. Often, to uncover the best context, understand data and access trustworthy information, agents will need to augment with another technology – more of a hybrid approach. For example, they may need retrieval augmented generation (RAG) or Document AI to access external knowledge bases or documents to enrich their understanding of specific, non-training data. While LLMs and AI agents allow systems to extract intent and content from freeform text, it introduces new concerns around reliability and accuracy. Document AI can bridge that gap – allowing access to trustworthy, reliable, unstructured data that may be trapped in hidden documents such as PDFs, emails, images, audio files and chatbots – providing context and valuable insights for better decision-making.
While I’m delighted to recognise the close similarities between women and AI agents, I believe that the human race is still far superior, and we are a long way off being replaced by these autonomous software systems.
The only way forward is leveraging an AI agent’s powers with human strengths, and together they can make a better world.
