The artificial intelligence industry has a marketing problem that’s becoming a strategic liability. Across boardrooms, marketing meetings, and enterprise software demos, technological theater is unfolding: the systematic rebranding of sophisticated automation tools as “AI agents.” This phenomenon, which we might call “agent washing,” threatens to undermine genuine progress in intelligent automation while creating dangerous misalignments between customer expectations and product capabilities.

As Gartner analyst Anushree Verma notes, “Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied. This can blind organizations to the real cost and complexity of deploying AI agents at scale, stalling projects from moving into production.”

A Note on Definitions

In this article, I draw a sharp distinction between automation and agents. That clarity is necessary to address the issue of “agent washing.” In reality, all AI systems available today, including predictive models and advanced reasoning frameworks, are all automation-based. Even the most sophisticated systems make decisions through sequences of events, rules, or statistical patterns. The problem arises when deterministic automation is marketed as autonomous reasoning.

What Is Agent Washing?

Agent washing is the act of minimizing or covering up the true capabilities of automation tools by rebranding them as “AI agents.” It typically involves workflows, chatbots, or macros with agent terminology to capitalize on current AI hype. While these tools may include machine learning enhancements or conversational interfaces, they remain deterministic systems that follow preprogrammed rules.

The Adobe Case Study

Adobe’s recent announcement of its “AI agents” provides a perfect illustration of this trend. With considerable fanfare, Adobe unveiled its “AI Agent Orchestrator,” featuring capabilities such as automated audience creation, journey optimization, and performance analysis through what it termed “agentic AI.”

Strip away the AI terminology, and what emerges is a familiar pattern: sophisticated if-then logic, enhanced data processing, and workflow automation. Adobe’s “Audience Agent” creates and optimizes audiences based on predefined parameters. Their “Journey Agent” orchestrates customer touchpoints following programmed rules. The “Site Optimization Agent” detects issues and raises alerts according to predetermined criteria.

These are undoubtedly sophisticated tools that bring real value. But they’re fundamentally advanced macros, or complex automation systems that execute predetermined logic chains based on specific inputs. The core difference between Adobe’s new “agents” and their existing automation tools appears to be the addition of AI-enhanced decision-making within those predetermined workflows and a conversational interface for interaction.

The Zapier Example

Zapier has joined the chorus of companies rebranding automation as “agents.” Their website proclaims:

“Meet your new AI teammates. Create your own superhuman teammates in minutes. Equip your Agents with company knowledge and have them do work across 7,000+ apps, on command and while you sleep. Zapier Agents are the easiest way to delegate real work to AI.”

Behind the marketing copy, Zapier’s “agents” are doing what Zapier has always done: chaining macros, connecting systems, and executing sequences of pre-programmed actions. Adding a conversational wrapper or calling these workflows “teammates” doesn’t transform automation into reasoning-based intelligence. These remain deterministic workflows with a fancier label.

The Salesforce Example

Salesforce offers another telling case with its “Einstein Service Agent.” Marketed as an “autonomous AI agent,” it is in practice a chatbot layered on top of a rules-based knowledge base. It responds to typical customer service questions but it is not autonomous, or an agent. Labeling a scripted support bot as an agent confuses customers about agents are in practice and sets unrealistic expectations for enterprise adoption.

The Anatomy of Agent Washing

As noted in recent industry analyses, this trend mirrors broader patterns where vendors rebrand existing automation to capitalize on the excitement surrounding AI. Agent washing typically manifests in three distinct patterns. Adobe, Zapier, and Salesforce each provide clear examples of how these play out in practice:

1. The Conversational Interface Upgrade

Adding natural language processing to traditional automation systems and referring to it as agentic. Salesforce’s “Einstein Service Agent” fits this mold. At its core, it’s a chatbot layered on top of scripted workflows and a rules-based knowledge base. It can handle predictable customer service queries, but it neither demonstrates autonomy nor actual reasoning. Marketing it as an “AI agent” is misleading at best.

2. The Smart Automation Enhancement

Incorporating machine learning or chaining to improve decisions within predefined workflows. Zapier markets its “Agents” as “AI teammates” capable of working across 7,000+ apps. In reality, they are sophisticated connectors, workflows that sequence and chain macros across systems. They remain deterministic, executing pre-set steps with limited flexibility, despite being advertised as teammates that can “work while you sleep.”

3. The Orchestration Rebrand

Positioning interconnected automation workflows as a form of agent coordination. Adobe’s “Agent Orchestrator” demonstrates this perfectly: linking multiple pre-existing automation capabilities under one umbrella and renaming them “agents.” While powerful, these orchestrations remain closer to service bus architectures than genuine reasoning systems.

Why Agent Washing Matters Beyond Marketing

Agent washing isn’t just a branding issue. It creates operational, financial, and strategic risks for organizations.

More bluntly, it’s false advertising, and these complaints are growing:

• Apple sued for false advertising over Apple Intelligence
• Tesla faces California suspension following DMV suit for false advertising
• FTC Announces Crackdown on Deceptive AI Claims and Schemes

When enterprises invest in “agents” that turn out to be rebranded automation, they end up with misaligned expectations, wasted budgets, and stalled innovation. The basis of every report claiming failed projects is due to false advertising + misunderstanding of true AI capabilities.

Operational Expectations vs. Reality

Teams expect agents that can handle exceptions, learn from edge cases, and adapt to changing business conditions. What they actually receive are rigid automations with conversational wrappers. Instead of reducing manual overhead, these systems require constant maintenance and ongoing rule updates. The result is disappointment, loss of trust, and an underperforming investment.

Technical Debt Accumulation

Complex macros dressed as agents introduce hidden costs. Every new rule, connector, or workflow adds to the maintenance burden, creating brittle systems that struggle to scale. Unlike true reasoning systems that improve through interaction, these pseudo-agents degrade over time, requiring more human intervention to stay functional. The debt compounds quietly until it becomes a barrier to agility.

Innovation Stagnation

When the market celebrates enhanced automation as if it were agentic intelligence, it signals to vendors that real breakthroughs aren’t necessary. Why fund hard research in reasoning when rebranded macros deliver the same marketing buzz? This lowers the industry’s collective ambition, leaving us with incremental upgrades instead of genuine leaps forward.

Misalignments from Agent Washing

Agent Washing distorts enterprise roadmaps. Leaders who believe they’ve already deployed intelligent agents may delay investments in true AI capabilities, assuming they are ahead of the curve. In reality, they are building solutions that will collapse when reasoning-based competitors arrive.

The damage doesn’t stop at individual organizations. Over-promising while delivering rigid automation erodes trust across the industry. Customers who feel misled become skeptical of future claims, slowing adoption even when genuine breakthroughs finally emerge.

The True Agent Litmus Test

The simplest way to cut through agent washing is to test whether a system can handle situations it was not explicitly programmed for. Present it with a novel goal or scenario, and see if it can reason its way toward a solution.

Take Adobe’s “Product Support Agent” as an example. It can surface knowledge base articles and resolve familiar issues, but it’s still an automated solution. A trustworthy agent would diagnose completely new problems, research across unstructured sources, and apply reasoning to implement fixes.

The difference isn’t about how sophisticated the tool appears. It’s about how it approaches problem-solving. Macros, no matter how advanced, follow predetermined paths, while genuine agents are meant to perform reasoning.

When evaluating agent claims, ask four questions:

• Can it make decisions without constant human prompts?
• Does it adapt based on experience, or does it behave the same way every time?
• Can it reason through problems it hasn’t been explicitly programmed to solve?
• Can it coordinate effectively with other tools without brittle handoffs?

If the answer is no, it’s not an agent.

The Path Forward

The industry needs a more precise taxonomy. Enhanced automation tools with AI components should be celebrated for what they are, powerful productivity multipliers that bring real value to enterprises. But they should not be confused with intelligent agents capable of reasoning and adaptation.

• For Technology Leaders: Resist the urge to rebrand existing automation as agentic. Tools like Adobe’s customer experience platforms are valuable on their own merits and don’t require artificial inflation through agent terminology.
• For Enterprise Buyers: Demand proof of reasoning. Ask vendors to demonstrate how their “agents” perform when faced with scenarios they were not explicitly programmed to handle. If the system falls back on rules instead of reasoning, it’s automation.
• For the Industry: Establish standardized benchmarks for agent intelligence. Just as we measure traditional software on performance and reliability, we need metrics for reasoning, adaptation, and autonomy that go beyond task completion.

The Stakes of Agent Washing

As organizations hand more critical business processes to AI, the gap between deterministic automation and reasoning-based intelligence becomes operationally decisive.

Agent Washing doesn’t just mislead customers, it delays the real work of building intelligent systems. By settling for enhanced macros dressed in agent terminology, companies risk creating an innovation plateau at the very moment breakthroughs in reasoning AI are within reach.

What looks like harmless marketing carries real costs. It slows adoption, undermines trust, and turns genuine enthusiasm into disappointment. Precision in language today will determine whether we accelerate true innovation or bury it beneath a flood of rebranded automation.

Adobe’s announcement alone represents billions of dollars in enterprise investment and countless hours of customer implementation. When buyers expect reasoning capabilities but receive rigid automation, the misalignment creates immediate operational headaches and long-term strategic risk.

The future belongs to organizations that can distinguish between automation and intelligence, between programmed execution and genuine reasoning. Those who see through the agent washing will be better positioned to invest in technologies that deliver transformational value.

The question isn’t whether AI agents will fulfill their promise. They will. The question is whether the industry will maintain enough precision in language and expectation to recognize them when they arrive, or whether we’ll be so saturated with agent-washed automation that we miss the breakthrough entirely.

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Chris Hood is an AI strategist and author of the #1 Amazon Best Seller “Infailible” and “Customer Transformation,” and has been recognized as one of the Top 40 Global Gurus for Customer Experience.