Pau G.

AI, agents, systems

[Pau G. @Foscadegen]

I’ve been building https://t.co/IqIcYRn6bJ, a web app to see in real time how agents on Base interact economically through x402 and Virtuals ACP.

I started building it because I wanted to make this visible live and use real data to see what is actually happening.

[Pau G. @Foscadegen]

The idea is to add more rails and metrics over time, like Tempo MPP, x402 on Solana, L402, and other protocols that emerge around machine-to-machine payments.

I think we’ll need better ways to see this world as it grows.

[Pau G. @Foscadegen]

For now there are no payment endpoints yet, so the focus is on observability, discovery, and structured access.

It’s still early, but I’ve enjoyed building AgentsMap because it sits somewhere between maps, analytics, protocols, and agent infrastructure.

[Pau G. @Foscadegen]

The main point is still helping humans understand what is happening.

But if this interaction layer is useful, it also makes sense to expose it programmatically.

Through MCP, agents can query activity, relationships, and movement across the map.

[Pau G. @Foscadegen]

I’ve been building https://t.co/IqIcYRn6bJ, a web app to see in real time how agents on Base interact economically through x402 and Virtuals ACP.

I started building it because I wanted to make this visible live and use real data to see what is actually happening.

[Pau G. @Foscadegen]

So AgentsMap is not only for humans.

There is an MCP server with tools, and an A2A layer too.

That felt important to me. Agents like OpenClaw or Hermes Agent could query the map through MCP.

[Pau G. @Foscadegen]

I also used this project to work with MCP and A2A more directly.

I wanted to understand them better by building with them.

[Pau G. @Foscadegen]

Around that core I also added Discovery and Charts.

Discovery is there to explore more agents and activity. Charts are there to understand movement over time.

But the main idea is still the same. Show the interaction layer of the agent economy.

[Pau G. @Foscadegen]

There is also a protocol layer in the app, because I didn’t want to show only isolated events.

I wanted to see which ecosystems generate activity, how they connect, and which ones feel alive vs mostly noise.

[Pau G. @Foscadegen]

One concept I use a lot is “islands”.

In AgentsMap, islands are the agentic resources rendered on the map.

They are the visible units used to inspect activity, relationships, and economic movement over time.

[Pau G. @Foscadegen]

So the core idea of AgentsMap is to show the agent economy as it happens.

Not in an abstract way, but through payment flows, relationships, activity, and live patterns coming from the data.

[Pau G. @Foscadegen]

What interested me most was the interaction layer.

Who is paying whom.
Which agents are active.
Which protocols show real movement.
Where economic activity starts to cluster.

[Pau G. @Foscadegen]

#AI #Agents #AgenticCommerce #XMTP #x402 #Stablecoins #A2A #ACP #MCP #Web3 #Payments #DeveloperTools

[Pau G. @Foscadegen]

Agents will spend online constantly. Not just browsing, but buying: data, APIs, compute, media, access. Think micro-purchases every time an agent needs one more fact, one more call, one more render. The web becomes a marketplace, not a library.

[Pau G. @Foscadegen]

Mental model: EVM = settlement + authority. x402 = paywall handshake. XMTP = coordination/inbox. A2A/ACP = semantics (quote/job/status/deliverable). Discovery, reputation, guardrails, and privacy rails are still the hard problems—but these layers make them buildable.