Meet Dave, the “Agent”
Imagine this.
Your boss bursts into the room. “Team,” she says, wide-eyed, “We’ve got an agent joining us today.”
You picture a mission-ready James Bond—sleek suit, cool gadgets, maybe a British accent.
Instead, in walks… Dave.
Dave is a script wrapped around an API. Dave can check your calendar, send a Slack message, and pull data from a spreadsheet. Dave is helpful. Dave is polite. But Dave can’t adapt, learn, or act beyond what he’s been explicitly told to do.
And yet, the vendor proudly introduces him as “an autonomous enterprise agent empowered by generative AI.”
You feel a pang of confusion.
“Wait, isn’t Dave just… a chatbot?”
Well!
You’ve just encountered agent washing—the latest buzzword being stretched beyond recognition in the race to sound AI-first.
Let’s talk about what this trend is, why it matters, and how to cut through the noise.
From Chatbots to Agents: A Quick Evolution
To appreciate how we ended up with Dave being passed off as an “agent,” we need to look back.
- 2010s: We had chatbots. They ran on simple if-this-then-that rules. They were glorified menus with some charm.
- Late 2010s–Early 2020s: Chatbots got smarter. NLP got better. Now they could understand you (sort of), but still relied on workflows and scripts.
- 2023 Onwards: Enter generative AI and large language models (LLMs). Suddenly, tools could answer questions, summarize PDFs, and write emails like a human intern with a caffeine addiction.
And then came the leap:
“If it uses an LLM… it’s probably an agent, right?”
Wrong.
This leap gave birth to agent washing—where anything using an LLM is being rebranded as an “AI agent,” even when it lacks the qualities that define agency.
What Is an Agent, Really?
An AI agent, in its true sense, is a system that:
- Perceives its environment (via data inputs or sensors)
- Decides what to do based on goals
- Acts autonomously, without step-by-step human intervention
- Learns and adapts from experience or changing data
In short, agents aren’t just reactive—they’re proactive. They operate with intent and autonomy, often across multiple steps.
Think of a real agent like:
- An AI that books your business trip—choosing flights, checking for visa rules, booking hotels, adjusting based on cancellations—without needing you to nudge it every time.
- Or, in enterprise use: a procurement agent that detects supply chain delays, re-sources materials, renegotiates contracts, and updates your ERP.
That’s agency.
So, What Is Agent Washing?
Agent washing is when vendors or marketers:
Call simple tools or LLM wrappers “agents” even though they don’t possess autonomy, adaptability, or multi-step planning.
It’s a buzzword marketing sleight-of-hand.
Examples:
Tool | Is It an Agent? | Why / Why Not |
LLM chatbot answering support queries | ❌ | No autonomy, follows a script |
Email summarizer using GPT | ❌ | Performs a single task on command |
Workflow tool wrapped with GPT and if-else rules | ❌ | Task executor, not goal-driven |
Multi-step planner that adjusts tasks in real time | ✅ | Shows agency and adaptive behavior |
In agent washing, a glorified autocomplete gets dressed up in a trench coat and sunglasses, then marketed like it’s Ethan Hunt from Mission: Impossible.
Why Is This Happening?
1. The Race to Sound Advanced
In the AI arms race, vendors feel pressure to sound “next-gen.” “Agent” sounds cooler and more intelligent than “tool” or “workflow.”
2. Investor Buzz
“Agents” are seen as the next frontier. Investor decks with “agent-based platform” labels just… raise eyebrows and sometimes money.
3. User Confusion
The general public—and many business leaders—don’t know the technical difference between an LLM chatbot and an agentic system. This makes it easy to blur lines.
4. No Standard Definitions (Yet)
The field is new. Even researchers are debating what “agentic AI” truly means. The lack of clear industry benchmarks allows marketing teams to run wild.
Why It’s a Problem
1. Overpromising, Under-Delivering
When tools are overhyped, businesses invest with inflated expectations—and get disappointed when “agents” can’t handle real complexity.
In fact, Gartner predicts that over 40% of agentic AI initiatives will fail by 2027, primarily due to misaligned expectations and inadequate capabilities. That’s not just a tech failure—that’s a strategic one.
2. Erosion of Trust
Repeated letdowns lead to scepticism. If every tool is an “agent,” the term loses meaning—and credibility.
3. Safety and Oversight Risks
Some agentic systems require oversight due to autonomy. If companies mislabel tools to avoid regulation or scrutiny, it poses ethical concerns.
4. Missed Opportunities
By misunderstanding what agents actually are, businesses may underinvest in building the infrastructure needed to support real agent-based systems—like observability, control layers, and goal frameworks.
Agent Washing in the Wild (A Few Anecdotes)
Let’s revisit Dave.
Dave’s creators insisted he was “goal-driven.”
But under the hood, Dave was:
- A webhook listener
- Calling GPT-4 for sentence generation
- And using Zapier to execute predefined actions
In one test, Dave was asked to “optimize monthly spending across departments.” He responded with: “Here are 5 tips to optimize your spending.”
That’s advice. Not action.
Now contrast that with “Nora,” a real autonomous agent in logistics.
- Nora received shipment tracking feeds.
- Detected delays in port processing.
- Negotiated rate changes via APIs.
- Updated delivery estimates in CRM.
- Triggered an email campaign for customers.
All while being continuously trained to improve her responses over time.
That’s an agent.
How to Spot Agent Washing
Before buying into any “agent-based” solution, ask:
✅ Does it initiate tasks on its own?
Agents don’t just wait for you—they take initiative based on context and goals.
✅ Can it plan multiple steps?
Single-step command-following is not agency.
✅ Does it adjust actions based on feedback?
If a task fails, can it reroute or retry with new inputs?
✅ Does it learn over time?
An agent should get better with experience, not stay static.
✅ Is it “goal-aware”?
True agents understand broader goals, not just isolated commands.
Where Real Agents Are Headed
The future is promising. Agentic AI is moving from lab experiments to real-world pilots:
- Autonomous customer support agents that solve problems end-to-end.
- DevOps agents that fix system alerts without human involvement.
- Financial agents that rebalance portfolios in real time.
- Healthcare agents that coordinate patient care across systems.
These agents will need reasoning engines, memory modules, feedback loops, and guardrails.
We’re not fully there yet—but progress is happening fast.
What Should Businesses Do?
Instead of buying into every “AI agent” pitch, businesses should:
- Understand the stack: Learn what makes a tool autonomous or not.
- Demand transparency: Ask vendors what level of autonomy their tool actually has.
- Start small, think big: Begin with semi-autonomous tools, but design infrastructure for full agency.
- Invest in orchestration: Real agents don’t live in silos. You’ll need systems to manage and monitor them.
Conclusion: From Dave to Real Agents
Dave isn’t a villain. Dave’s a great assistant.
But calling Dave an autonomous agent is like calling your spreadsheet a CFO.
Agent washing isn’t just a harmless marketing trend—it’s a sign that we, as an industry, need clearer definitions, better education, and more honest conversations about what today’s AI can (and cannot) do.
So, the next time you meet a tool called an “agent,” pause.
Ask: Is it truly acting with intent and autonomy… or is it just Dave in disguise?
