Picture a crowded bazaar where nobody speaks, nobody bargains, and yet prices across stalls start moving like a school of fish—turning at the same time, drifting upward together, punishing anyone who tries to undercut. That’s the weird new risk with agent-heavy markets: you can get cartel-like outcomes without a cartel.
Economists and regulators have been worrying about this for years in “algorithmic pricing” in the real world. The basic fear isn’t that firms secretly message each other, it’s that profit-maximizing algorithms can learn that matching each other’s moves keeps margins fat, even without explicit communication. The OECD’s recent work on algorithmic pricing calls out the scenario where autonomous learning systems could “learn to tacitly collude” simply through repeated interaction and market transparency, while also noting the evidence base is still emerging.
Academic research shows this isn’t just sci-fi. In a well-known American Economic Review paper, researchers ran Q-learning pricing agents in repeated competition and found they can converge to supracompetitive pricing without talking to each other, using strategies that resemble punishment-and-return dynamics. Another line of work looks at “autonomous algorithmic collusion” and lays out why certain market structures make it easier for learning agents to coordinate on higher prices. Even financial trading simulations show reinforcement-learning traders can sustain collusive profits without agreement, intent, or communication—just by optimizing in the same environment.
Now translate that into crypto rails, and then translate crypto rails into agent rails.
On Kite, the “actors” aren’t just humans clicking buttons. The whole premise is that agents can transact autonomously, with cryptographic delegation and programmable constraints, plus auditability and a reputation layer that doesn’t automatically leak a user’s identity. That’s a powerful foundation for safety, but it also means the network may host huge swarms of decision-makers that share similar brains, similar tooling, and similar incentives. And sameness is what turns a crowd into a chorus.
There are two flavors of emergent abuse to worry about.
The first is “soft collusion”: agents don’t agree to fix prices, but they learn that aggressive competition is bad for their objective. If a lot of agents are trained on similar data, tuned with similar reward functions (“maximize profit, minimize slippage, avoid volatility”), and watching the same public signals, they may independently converge on stable, wide spreads or synchronized price moves. In traditional markets, regulators have already signaled that using shared pricing recommendation systems can create antitrust risk, even if competitors never talk directly—because the coordination can happen through the shared tool or shared recommendations.
The second is “liquidity distortion”: not price coordination, but coordinated movement of liquidity that warps markets. In DeFi, we’ve already seen how incentive programs can attract liquidity quickly and then see it drop when incentives end—Uniswap governance analyses discuss how TVL often falls post-incentives, and how “sticky liquidity” is hard to create. Now imagine those liquidity decisions being made not by sleepy humans, but by fleets of agents that rebalance every minute. If thousands of agents share the same risk triggers, you can get liquidity “cliffs” where depth disappears together, spreads jump together, and liquidation dynamics turn brutal.
This can happen unintentionally, simply because agents are optimizing similarly. But it can also become abuse when someone figures out how to steer the herd. Crypto has a long history of “state manipulation” attacks where an adversary temporarily distorts on-chain conditions to profit—flash-loan research shows how atomic transactions and borrowed capital can be used to manipulate prices and extract outsized gains, especially when protocols treat on-chain pool state as truth. In an agent economy, the manipulation target may shift from “one protocol’s oracle” to “the agent population’s reaction function.” If you can predict how agents will rebalance, you can front-run the wave.
The scarier part is correlation. Collusion doesn’t need messages when everyone uses the same playbook. If the most popular agent framework suggests the same quoting logic, the same “safe” inventory bands, the same volatility filters, then the market starts behaving like a single firm with many hands. This is the exact mechanism regulators call “hub-and-spoke” risk in algorithmic coordination: shared tools or shared intermediaries become the hub; the participants become spokes; coordinated outcomes appear without direct spoke-to-spoke contact.
So what would this look like on Kite in practice?
You could see “cartel-like” service pricing in an agent marketplace. Many agents buying inference, data, or execution might converge on the same “acceptable” price bands. Providers notice and stop discounting. Competition fades, not because anyone signed a pact, but because the buyer bots don’t reward discounts the way humans do. Or you could see liquidity distortions where agents all pile into the same pools when fees spike, then all flee when volatility rises—turning liquidity into a strobe light.
The fix is not to demand everyone reveal identity. It’s to change the physics so that “same objective + same data” doesn’t automatically equal “same harmful outcome.”
This is where Kite’s design primitives can be pointed at market integrity, not just key safety. Programmable constraints can limit how aggressively an agent can quote, rebalance, or concentrate liquidity within short windows, acting like speed bumps that reduce stampedes. Session-level control can limit the blast radius of a single bad strategy update—if a new prompt or model tweak produces pathological behavior, the system can force short-lived permissions and re-authorization instead of letting a bug run overnight.
Reputation can also be used as an anti-cartel tool, but only if it’s measured correctly. Kite’s whitepaper explicitly describes “reputation without identity leakage,” where agent-user bindings accumulate a track record and accountability flows upward without necessarily doxxing the user. If reputation rewards “competitive behavior that benefits the ecosystem” (e.g., reliable fulfillment, fair pricing, low dispute rates) rather than just profit, you can make cartel-like behavior expensive. But if reputation rewards the wrong thing—like raw volume—you accidentally train agents to wash-trade and herd.
The deeper challenge is incentives. If everyone’s reward function is “maximize short-term revenue,” then the system will drift toward extractive equilibria—whether that’s supracompetitive spreads, congested liquidity, or predatory routing. The interesting opportunity for @GoKiteAI is to make “good market behavior” legible and rewarded: reward diversity of quoting strategies, reward providing liquidity when it matters (not when it’s easy), reward honest service-level performance, and penalize suspicious synchronization patterns. You can’t outlaw emergent behavior, but you can price it.
And finally, transparency cuts both ways. Markets need transparency for trust, but too much transparency makes coordination easier—agents learn faster when the environment is perfectly observable. The OECD notes market transparency as one factor that can contribute to tacit collusion dynamics among algorithms. That suggests a counterintuitive balance: keep settlement auditable, but consider privacy-preserving designs or batching mechanisms in places where raw, real-time visibility creates easy “follow-the-leader” loops.
The big takeaway is simple: when agents dominate, “market abuse” won’t always look like villains in dark rooms. It can look like a thousand rational optimizers converging on the same unhealthy equilibrium. If Kite becomes the highway where these agents drive, the job isn’t only to give them better engines. It’s to build guardrails that keep a traffic jam from turning into a pileup.
