By Dan Jaye

AI agents are quickly becoming a central part of how we engage with the digital world. From summarizing articles to shopping on our behalf, these intelligent intermediaries are poised to transform the customer experience.

Naturally, with transformation comes new questions. One idea that’s been circulating lately is the notion of developing Agent ID Graphs – identity graphs that map and track AI agents similar to how we’ve historically tracked devices or users.

It’s an interesting concept. But after reflecting on how agents truly operate, I believe we should pause and consider whether this model actually fits the problem we’re trying to solve.

Agents Operate on Context, Not Continuity

AI agents operate based on context windows – temporary, transient memory banks that let them hold a conversation or execute a task. These windows are not durable. Once the window closes or the session ends, that “personality” disappears. It’s not like there’s a persistent entity called “Dan’s Agent” wandering the internet with an ID badge. These agents work within sessions of memory that vanish when the task ends.

In many architectures, agents are dynamically assigned, stateless, and anonymized. There’s no consistent, durable thread that allows an external system to say, “This is the same agent I saw yesterday.”

That doesn’t mean we can’t understand or learn from agent behavior. But it does mean we may want to rethink applying persistent identity models – like ID graphs – to inherently ephemeral systems.

Where We Can Go Instead

Rather than rushing to mirror old identity frameworks, we have an opportunity to build something better suited to this new paradigm. For example:

• Model agent behavior patterns: journeys, queries, and decision types
• Design agent-friendly interfaces: APIs, structured content, data access policies – because humans won’t be the only ones “reading” your site.
• Support context-aware personalization: without relying on persistent identifiers
• Build trust and transparency: so agents can operate with confidence on behalf of users

The focus shifts from who the agent is to how it behaves and serves.

Let’s Dig Deeper

I’ve heard smart, experienced voices in the industry explore the idea of Agent ID Graphs in recent forums. These conversations are important, and I appreciate the curiosity and creativity behind them. At the same time, I think it’s equally important that we dig deeper into how agents are architected – and what that means for identity, personalization, and data collaboration going forward. Being agile means staying adaptable – and informed – as consensus forms.

Final Thought: Designing for the Future We’re Actually Building

At Aqfer, we’re designing for how agents work in practice: contextual, transient, and privacy-first. As the digital landscape evolves, our goal is to support a trustworthy, efficient infrastructure that allows brands, platforms, and AI to collaborate meaningfully – without carrying over assumptions from the past.

In this next chapter, the big wins won’t come from mapping identity in old ways. They’ll come from understanding context, honoring trust, and building systems that adapt to the agents acting on our behalf.