People often talk about AI agents as if they move around on their own with perfect judgment. They don’t. They follow keys and rules, and if those keys leak or the rules are loose, things break. Kite saw this early while building out its agent network, and the team leaned hard into identity as the core line of defense. Not a fancy feature. Just something that had to work if the network was going to grow past a tiny lab demo.
Kite uses layers for identity instead of one big master key that controls everything. It sounds simple at first, though the impact becomes clearer once you picture thousands of agents acting for people, apps, or companies at the same time. Errors and exploits don’t wait around for clean designs, and Kite built its identity system to deal with that messy side of automation.
The first layer, user identity, sits quietly in the background. This is the key that belongs to a real person. It does not roam the network. It stays locked in secure hardware or a safe part of the device. The point is to keep the “root” out of sight. Many systems claim to do something similar, yet they still let apps poke at the core key when signing actions. Kite avoids that. The user key only delegates. That’s it.
People decide what their agents can do right at this layer. Spending caps. Allowed actions. Access windows. Things you want to set once, then forget until something changes. Nothing wild about it, though the difference is how tightly those rules stick. They are cryptographic parts of the agent’s identity, not settings tucked into an app menu that can reset after an update.
Then comes the agent identity layer. Each agent gets its own key pair. They are derived from the user but never expose the user’s private key. This matters. If an agent key leaks, the thief does not become you. They only become that one agent, and even then, only until revoked. Many attacks stop right here because the agent has a defined, narrow scope. It can’t reach into unrelated assets or sign actions outside its job.
Agent identities act a bit like digital work badges. A badge only lets you into certain rooms. Even if someone steals the badge, they can’t stroll into the control room unless that room was part of the rules. This design cuts off common exploit paths where attackers move sideways through a system once they breach a single point.
Another thing that makes this layer strong is how other agents and services can verify it on-chain. They don’t guess. They check. Spoofing becomes harder because an agent pretending to be someone else can’t fake the underlying cryptographic proof. No “almost looks right” trickery.
The third layer, session identity, feels small but ends up carrying a lot of weight. Every time an agent runs a specific task, a temporary key is created. It lasts only for that session. Once the job ends, that key is gone. Even if a bad actor manages to intercept it, they only get an expired pass. Not something they can reuse tomorrow or even five minutes later.
This shrinking of the attack window matters more than people expect. Many exploits rely on long-lived authorization. The shorter the lifespan, the smaller the damage andsomeone might steal a key but find it useless by the time they try to act.
These three layers combine in a way that feels almost like building walls inside walls, though not in a heavy-handed way. More like recognizing that AI agents are fast and unpredictable, and humans cannot watch every move. So the system must give users control even when they’re not paying attention. If an agent misbehaves or gets hijacked, the user can pull its access instantly with one revocation at the root. No hunting for which part went wrong.
Kite also ties everything into verifiable credentials. An agent can prove that it meets certain requirements before interacting with another agent or service. This avoids the guessing game where apps hope a caller is legitimate. They check the credential, the identity, the permissions, then decide. The chain of trust stays intact because all these proofs link back to the root identity through signed steps.
What makes this setup helpful in practice is that the network records actions in a traceable way. The chain of events, from the user identity all the way down to the session key used for a single call, is visible on-chain. If something odd happens, people can follow the trail and see whether the action was allowed, spoofed, or triggered by a compromised agent. It cuts out the fog that often surrounds automated systems where logs sit scattered across closed servers.
Millions of calls have already moved through Kite’s testnet in early 2025. That volume reveals which designs hold up and which ones crack under real use. The identity system held up well because the layers enforce limits by design, not by optional checks. And since agents often act far faster than humans, the system cannot rely on user confirmation for every move. The layers provide confidence that even rapid actions stay within safe bounds.
Kite also made sure that recovery isn’t an afterthought. If something goes wrong, a user can revoke an agent or a whole branch of delegated keys. That revocation travels through the network and takes effect right away. Many platforms treat recovery like a support ticket. Kite treats it as part of the identity architecture.
Another interesting side effect is reputation. Because identities are consistent and traceable, agents build reputations across services. Good behavior sticks. Bad behavior sticks too. This helps services judge unknown agents without relying only on fresh credentials that reveal nothing about past actions.
Taken together, these identity layers do more than block exploits. They give users practical control without forcing them to micromanage everything agents do. The system keeps things tight even when the user is offline, asleep, or busy. And when something feels off, the user still holds the master switch. Not the app. Not the agent. Not the network.
Kite’s identity model isn’t about fancy language or lofty claims. It’s about making sure autonomous agents don’t act beyond their bounds, no matter how fast or often they run. That’s the real reason the layered approach works: it matches how automation behaves in the wild, not how people wish it behaved on paper.
