Feature
As AI becomes more entrenched in enterprise operations, a wave of marketing jargon is making it increasingly difficult to separate fact from fiction. The term ”agent washing” has emerged to describe the growing practice where vendors label simple automation or scripted workflows as AI agents.
This trend presents a real challenge for decision-makers and technology leaders: How can they distinguish genuine agentic intelligence from glorified macros? And what does innovation in this space look like in practical terms?
Planning for authentic agentic systems requires understanding four themes: the rise of agent washing, defining features of real AI agents, frameworks for evaluating these systems within business contexts and the risks and rewards involved in their responsible deployment.
Together, these themes serve as a guide through the evolving and complex technological landscape.
Agent Washing: When Hype Meets Reality
Much like greenwashing in environmental marketing, agent washing is a tactic designed to capitalize on current AI trends by falsely claiming intelligence where none exists. “Agent washing slaps an ‘AI agent’ label onto solutions that are little more than glorified scripts,” said Nimisha Mehta, senior software engineer at Confluent. This practice not only misleads buyers but also threatens to stall genuine progress by creating confusion among users.
“Companies often take old rule-based systems or hard-coded automation tools and call them ‘AI agents’ without anything resembling actual autonomy or learning,” agreed Piyanka Jain, CEO of Aryng. Such mislabeling risks eroding overall trust in AI technologies as users begin to question agentic claims.
This is part of a recurring pattern in tech hype cycles, said Rohan Sarin of Speechmatics. “It reminds me of the big data era, when everyone branded their work as big data — from companies crunching millions of rows to those working with just a hundred-row spreadsheet.” Inflated claims are hardly new, and the temptation to overpromise repeatedly challenges emerging technologies.
“Sometimes companies employ AI solely for PR and marketing purposes, which is misguided and potentially harmful to long-term strategic goals,” said Ilia Badeev, head of data science at Trevolution Group. The fear of missing out leads executives to adopt AI tools prematurely or superficially, sacrificing strategic coherence for short-term visibility.
What Do Real AI Agents Look Like?
True AI agents distinguish themselves through autonomy, adaptability and making decisions under uncertainty — traits that far surpass the limitations of scripted or rule-based systems.
Agency in AI is “a game-changer toward autonomous and self-healing systems, likening it to giving a brain the ability to use tools, process multiple streams of information and make decisions based on them in real time and complex environments,” Mehta said. This independent reasoning transforms AI from a simple instructor to an active problem solver.
Contrasting real AI agents with traditional automation, “An AI agent makes decisions and executes tasks autonomously without requiring explicit step-by-step instructions, unlike robotic process automation, which depends on specific, pre-programmed instructions,” said Ram Srinivasan, author of The Conscious Machine. This independence in decision-making lies at the heart of agentic intelligence.
Agentic AI goes beyond following predefined sequences, said Maitreya Natu, chief data scientist at Digitate. “Agents work independently rather than merely following a sequence of steps, adding value through original appreciation of context and information.” Such situational awareness helps AI agents navigate dynamic environments and respond to unforeseen changes.
How to Tell the Difference Between AI Agents and Automation
Distinguishing real AI agents from automation tools isn’t always straightforward. Leaders must ask tough questions and scrutinize claims to avoid costly missteps.
Autonomy, initiative and contextual intelligence are major differentiators. “A good indicator is how the perceived ‘agent’ operates,” Natu said. “Does it adapt to new inputs, context or feedback effectively? If not, it is unlikely to be a real AI agent.” Real agents respond fluidly to changing conditions rather than rigidly following scripts.
Due diligence is important, Jain said. “Leaders need to look beyond vendor slide decks and seek a clearer understanding of both their problem and the architecture behind the tools being pitched.” Without deeper insight, organizations risk investing in tools that don’t have agentic capabilities.
Open-source communities are valuable resources, Mehta said. “Agentic AI projects maintained by such communities often reflect cutting-edge research and are less likely to deceive users.” The transparency and collaborative review inherent in open-source projects counterbalance marketing hype.
Aligning AI investments with long-term strategy helps avoid agent washing. Organizations should “assess AI maturity, develop scalable capabilities and identify high-value use cases where AI can drive real impact rather than short-term hype,” said Ravindra Patil, VP of Data Science at Tredence.
Agentic AI Brings Real Risks and Real Value
Deploying agentic AI responsibly requires more than avoiding buzzwords. It demands a clear-eyed approach focused on safety and appropriate expectations.
“Insufficient training and ineffective guardrails pose the biggest risks in deploying AI agents, making it impossible to use such agents for any business-critical task without adequate safeguards,” Natu said. Without proper preparation, AI agents may introduce unacceptable operational risks.
Rather than replacing humans, agentic AI should augment human capabilities, including building effective human-agent interfaces and defining the right moments for hand-offs between humans and AI.
"False advertising — where anything from automation to chatbots is labeled ‘agentic AI’ — indicates a significant market maturity gap that could lead to misplaced trust and operational failures,” Srinivasan warned. This gap underlines the importance of skepticism and rigorous evaluation.
Sarin advises focusing on outcomes over flashy technology. “While it is easy to get caught up in tools, the ultimate measure is whether the technology delivers tangible results that improve processes or decision-making.”
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Finally, new roles such as trainers, explainers and sustainers of AI support ongoing learning and trust-building. They are important for the future of AI adoption, because responsible deployment is an ongoing process rather than a one-time event, Natu said.
Discernment Is the Differentiator
Without clear definitions, it’s easy to be swept up in spin and hype, losing sight of what truly matters.
“Real innovation is silent and steady, focusing on improving processes and productivity rather than headlines or marketing buzz,” Badeev said.
For technology leaders, being clear about what constitutes a real AI agent is an essential step toward building trust, creating value and making sure AI makes an impact.