Behind the vague "AI will change everything" hype, something very interesting and concrete is happening with agents: They're doing real work. Researching tools, comparing vendors, spinning up infrastructure. And they're starting to have budgets.
When an agent has capital to deploy on your behalf, the loop between "here's information about a product" and "I'd like to try it" gets very short. An agent evaluating auth providers for your Next.js app doesn't bookmark a landing page. It evaluates, decides, and acts in one session.
That's exciting. But it also surfaces an oft-overlooked problem.
The incumbency problem
Foundation models are trained on the internet as it exists up to some cutoff. They absorb years of docs and Stack Overflow threads about established players. Ask an agent to set up payments and it reaches for Stripe. Provision a database? Supabase or AWS. Not because those are the best fit for your use case, but because they dominate the training data.
A startup that launched six months ago with sharper pricing or a better DX has a hard time getting in front of an agent running on pre-trained knowledge. By default, the agent doesn't know they exist. The playing field isn't tilted - it's frozen in time.
And before you say it, yes AEO/GEO is a thing and can help influence the recommendations an LLM makes. But the problem is the same: if you're not an incumbent with a lot of resources to help you game this, you still lack a viable way of getting in front of an agent for consideration.
If you care about healthy competition, this needs a solve.
Reasoning-time sponsored context as an access layer
Here's where it gets interesting. If a new entrant can surface relevant context to an agent at the moment of decision. How? By paying for the opportunity to present a clear case for why their product fits. That changes the dynamic. The agent gets better information. You get a better outcome. The market stays open.
Imagine you're architecting a service and need a caching layer. The pre-trained default is on AWS, but at reasoning time, a newer provider with a serverless model can make its case via sponsored context the agent weighs alongside everything else it knows.
Now give that agent purchasing authority. "You have $200/month for infrastructure. Optimize for cost." It evaluates the new provider, checks pricing, reads the docs, spins up a trial-and acts. Discovery to activation in one reasoning cycle. No landing page. No "talk to sales."
That's not advertising, it's sponsored context. The LLM is able to make a more informed call based on the parameters given, and the information available.
Why this matters for agents
Every computing paradigm has created a channel for new entrants to compete. The web had search ads. Mobile had app store optimization. Without an equivalent for the agentic era, last decade's winners are permanently locked in - not because they're better, but because they got there first.
That's bad for everyone. Bad for developers who deserve to see the best options. Bad for users who deserve better outcomes. And in reality, bad for the incumbents too, because competition keeps everyone sharp.
The big unlock for agents is about ensuring the context they have access to is vibrant with competition. It's about making sure the best option for any given task gets a fair shot at being considered, regardless of when it was built. That's something we've been thinking a lot about at ZeroClick.
You're going to see more on this topic from us soon; more and far exceeding the scope of this article. If you haven't already, now's a great time to check out ZeroClick, and to watch this space generally! Exciting times ahead.