AI-powered shopping agent browsing an online retail storefront, representing the hidden role AI agents can play between retailers and customers.
Paul Sims

Paul Sims

Retail Technology Strategy Consultant
AI

August 5, 2026

Advocate, spy or actor? The key AI agent types every retailer needs to know

“Agent” has quietly become the most overused word in retail technology. It’s thrown around in every meeting and every vendor deck, and rarely defined. But the differences between one kind of agent and another are not academic. They decide whose side the AI is actually on when it’s standing between you and your customer.

Three customer archetypes to start with

The advocate is an agent in the commercial sense – like a travel agent acting for you. It’s permissioned and declared: it works on your explicit authority and shares your values. The spy is an agent in the intelligence sense: a covert operator whose real allegiance is hidden, and whose interests can quietly conflict with the customer’s. The actor is an agent in the fullest sense of the word: an agent capable of independent judgement based on an inferred model of your values, rather than explicit instructions. It’s the most autonomous, and for now, the most theoretical.

The double agent (and why it’s the dangerous one)

Most agents a retailer builds for itself are advocates: clear allegiance, bounded scope. The commercially ambiguous ones are built by the platforms that sit between retailers and customers, such as Google’s shopping agents, ChatGPT in discovery mode, Amazon’s Alexa for Shopping. They claim to represent the customer, but the platforms behind them have investors who expect returns and advertisers who pay for placement – paying the platform, not the retailer whose products the agent is selecting between.

That’s the double agent. The customer believes they have an advocate, but they really have a spy. The architecture looks legitimate, and it really is acting on the customer’s behalf – it’s just also acting on somebody else’s. When Amazon’s Rufus shops on a customer’s behalf through Scheduled Actions, the customer believes it’s working for them. But it’s operated by Amazon, whose interests aren’t identical to theirs. The conflict isn’t a side effect; it’s part of the design.

Infographic showing three AI agent archetypes: (1) The Advocate – a permissioned, transparent agent that acts on a user’s explicit instructions, illustrated by a grocery shopping assistant; (2) The Spy – a covert agent whose true interests are hidden, illustrated by a brand agent disguised as a neutral comparison tool; and (3) The Actor – an autonomous agent that makes decisions on a user’s behalf based on inferred values and preferences, illustrated by an AI ordering wine for a dinner party. Each panel includes an icon, key characteristics, an example, and a supporting illustration.
From the book: The Agent Taxonomy – who does your agent serve? (The Joshua Problem, Ch.1)

The question to ask yourself

All agent types come down to one test you can apply to any agent touching your business: whose interests does it serve, and who authorised it? An advocate deserves your data; a spy doesn’t. For platform agents specifically, the book puts it like this:

“Before granting any platform agent access to our systems and our customers, can we answer the three questions posed earlier in the book: what data does it access, how does it rank our products, and who is accountable when something goes wrong? If we cannot answer all three, we are extending trust we have not verified.”

In short

“Agent” isn’t one thing: it’s at least three (and possibly four), and they don’t all answer to the same master. The retailers who treat that distinction as a contractual question, not a technical one, are the ones who’ll avoid handing their customers, and their data, to an agent quietly working for someone else.

This is one thread of a much bigger argument.

The Joshua Problem: Why Intent is Everything in Agentic Commerce is free for retail leaders. Read Chapter 1 now – then download the full book.

Sources

  1. “Agenticity” – origin of the term (Wiktionary)
  2. “Agentiality” – Richard Whitt (SSRN)
  3. “Agenticness” – OpenAI, agentic AI research

About the author

Paul is a Retail Technology Strategy Consultant, who recently joined Equal Experts after spending the last 20+ years working for retailers across the globe, including positions as CTO at Halfords and Chief Architect at New Look, Primark, Marks & Spencer, and Argos in Shanghai. Connect with Paul on LinkedIn.

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