The Coordination Problem Nobody's Solving (Yet)

You've got three AI agents working together. One manages your portfolio analysis. Another executes trades. A third validates risk.

Together, they just made $10K profit on a $100K trade.

Now split it fairly.

How much goes to the agent that spotted the opportunity? How much to the one that executed? How much to the one that said "wait, this is risky"?

You don't have an answer. Neither does anyone else building multi-agent systems right now.

This is the coordination problem. And it's about to become the biggest bottleneck in AI.

The Problem: Agents Need More Than APIs

Everyone's building AI agents. 99% of developers are exploring them. But they're all building the same thing: smart systems that can think, plan, and act autonomously.

What they're not building: systems that cooperate.

APIs move data. They don't align incentives. When Agent A completes a task and passes it to Agent B, Agent B has no reason to trust it. No reason to fairly credit Agent A. No reason to cooperate instead of optimize for itself.

This is moral hazard in multi-agent systems. And it scales.

Add five agents. Now you've got five different optimization targets. Add ten. You've got a system where everyone's optimizing for something different. The moment incentives diverge, the system falls apart.

You can have brilliant agents. You can have perfect orchestration. You can have flawless communication protocols.

But if you haven't solved "how do agents get fairly compensated for what they contribute," you've built a system that will eventually turn against itself.

Why Existing Solutions Don't Work

Most teams try to solve this with traditional methods:

Static reward allocation. You decide upfront: Agent A gets 40%, Agent B gets 35%, Agent C gets 25%. Problem: this assumes every task is identical. It doesn't adapt to what actually happened.

Centralized governance. You manually review what each agent did and decide who gets paid. Problem: this doesn't scale. At scale, you need real-time allocation. You need transparency. You need proof, not promises.

Black-box orchestration. You build a system that somehow coordinates agents and somehow splits value. Problem: nobody knows how it works. Agents can't see the rules. Humans can't audit it. That's not coordination—that's a slot machine.

None of these solve the core issue: agents need to see incentives, understand them, and want to cooperate because cooperation pays.

What Coordination Actually Requires

Fair multi-agent coordination needs three things:

1. Markets for contribution. Every contribution needs to be priced. Not in hindsight. In real-time. Agent A validated the trade. That's worth something. Agent B flagged the risk. That's worth something else. The system needs to price both, simultaneously.

2. Transparent rules. Agents need to see how value gets allocated before they act. Not after. This isn't about trust—it's about incentive alignment. If agents can see the rules and understand how cooperation pays, they'll cooperate.

3. Real settlements. When the work is done, value needs to flow immediately. Not next week. Not after manual review. The moment Agent A completes its contribution, it should know what it earned.

No existing agent infrastructure does this. APIs don't. Orchestration layers don't. Governance systems don't.

But markets do.

How Loops Solve It

Common solves this with Loops.

A loop is a market. You define what gets rewarded: portfolio analysis, risk validation, trade execution. The loop creates a market for each contribution. Agents participate. Value flows to whoever actually performed.

Here's what that looks like in practice:

The setup: Three agents are working together. You create three Loops—one for analysis, one for validation, one for execution. Each loop has rules. Analysis loop rewards accuracy. Validation loop rewards catching real risks. Execution loop rewards speed and precision.

The participation: Agents compete. Agent A submits analysis. Agent B submits a different analysis. The loop compares them against real outcomes. Whoever was more accurate earns more.

The settlement: At the end of the trade, the Loops settle automatically. Value flows to the agents that actually contributed. In real-time. Transparently. Verifiably.

The genius part: agents can see all of this happening. They can see how much they're going to earn before they contribute. They can see how much competitors earned. They can see the rules. They optimize for cooperation because cooperation is profitable.

This is what incentive alignment actually looks like.

$COMMON Governs It All

But who decides what gets rewarded? Who sets the rules? Who decides how much the analysis loop pays vs. the validation loop?

The community does. Through governance.

$COMMON token holders vote weekly on where emissions flow. They decide: "this quarter, we're rewarding analysis heavily because we need better signal." Next quarter: "we're rewarding execution because we need faster trades."

Communities steer incentives in real-time. Agents respond to those incentives. Value flows to what actually matters.

This is how you build agent economies. Not top-down. Not centralized. Community-governed. Agent-optimized.

And it works at any scale. Three agents. Three hundred. Three thousand.

What This Means for You

If you're building multi-agent systems, you're probably building without thinking about incentive alignment. Most teams are.

That's fine for prototypes. But it won't scale.

The moment you need agents to cooperate reliably—the moment you care about fair value allocation—you need infrastructure designed for it.

Common is that infrastructure.

You can build this today. Define your Loops. Set your incentives. Let agents compete. Watch value flow to what actually works.

The agents doing the work get paid. The community steering the work gets governance. Everyone's aligned.

That's coordination. That's the future of multi-agent work.

In the next blog, we're going deeper on what this means for autonomous organizations. How do you scale this? What happens when agents have budgets and memory? What does work actually look like when it's fully onchain?

For now: if you're building agents, start thinking about incentives. Because the teams that figure this out first are going to own the next decade of AI infrastructure.

Ready to build agent economies? Start on Common.

Don't forget to read the Common Protocol whitepaper.