Gartner on Agentic AI: Insights That Echo Our Experience

At Enterprise RPA, we’ve been deeply involved in AI development for years,  Gartner’s latest report on agentic AI validates much of what we’ve consistently observed and advised: these technologies are powerful, but only when adopted thoughtfully and for the right reasons. 

Agentic AI refers to the broader trend of AI systems acting more independently, while AI agents are the practical tools built on that idea—programs that can observe a situation, make decisions, and carry out actions with little or no direct guidance. These agents build on advances such as generative AI and promise to automate work that once required human oversight, extending automation into more complex or dynamic areas. 

Beware of “Agent Washing” 

With all the excitement, some vendors are quick to label existing products as “AI agents” or market anything using large language models (LLMs) as agentic AI. Gartner warns that this “agent washing” can mislead buyers: products may appear cutting-edge but deliver far less than promised or hide the true cost and complexity of proper deployment. 

This warning mirrors our own long-held stance. Drawing on years of AI research and development, Enterprise RPA has consistently emphasised genuine capabilities, rigorous evaluation, and a clear fit for each business scenario.

(For those interested in the full details, Gartner’s complete report can be downloaded here.) 

When to Pause or Reconsider 

Even when working with authentic AI agents, organisations still need to consider whether a given use case is suitable. Gartner highlights several signs that an AI agent might not be the right choice: 

• Straightforward, Stable Processes: If the work is routine and well-defined, traditional automation is usually cheaper and more reliable. 

• High Costs: Usage-based fees and LLM pricing can add up quickly if there is no clear return on investment. 

• Need for Human Judgement: Creativity, empathy and nuanced decision-making remain firmly human strengths. 

• Reliability Risks: AI agents can make mistakes or “hallucinate”, which is risky in areas such as healthcare or customer service. 

• Speed Requirements: Real-time tasks, such as fraud detection or industrial control, may not tolerate the delays of complex AI reasoning. 

• Regulatory or Ethical Concerns: Transparency and fairness are essential, and not every AI agent can meet those standards. 

• Readiness Gaps: Lacking the right data, infrastructure, or governance can derail any AI project. 

  
  

AI agents offer strong potential but aren’t a one-size-fits-all solution. Organisations must assess their value carefully and integrate them with the right expertise to avoid added complexity or risk. 

Turning Insight into Action 

At Enterprise RPA, we help organisations determine when and how AI agents can be applied effectively. By combining industry insight with hands-on experience, we guide businesses in designing approaches that improve processes, maximise value, and avoid costly missteps.  

If you are exploring agentic AI, now is the time to bring in expert support. Contact us today to see how we can help you create a practical, high-impact strategy for your AI initiatives.