During this time, I have repeatedly engaged with teams related to enterprise automation, and one increasingly clear observation is:
When AI enters the actual business systems of enterprises, the first issue that is exposed is not whether the model is smart enough, but rather how the system ensures correct execution when resources are competed for by multiple agents.
The competition for resources among enterprises used to mainly occur in:
Database Row Lock
Shared Variables
Concurrent Writing
Inventory Deduction
Cross-border Quota
Supplier Throttling
Budget Pool
Risk Control Threshold
These are resolved by architects and engineers through locks, queues, isolation levels, and distributed protocols.
But everything changes once AI is involved in execution.
AI does not share context
AI does not share state
AI based on probabilistic reasoning
AI will automatically expand the task chain
AI will trigger uncertain API calls
AI will access multiple shared resources simultaneously
AI will autonomously combine steps in the link
This leads to a very practical issue:
Enterprise resources are no longer uniformly scheduled by the system, but competed for use by multiple autonomous decision-making Agents.
If resource competition is uncontrolled, real risks will arise:
Budget is simultaneously deducted by multiple Agents
Vendor calls are preempted
Risk control is triggered in advance
Path execution is overridden
Cross-border quotas are consumed
Task chains polluting each other
Inventory and funds encountering race conditions
Such conflicts are not bugs but inevitable results of automated systems.
The more automated, the more conflicts.
The smarter, the harder to govern.
What I see in Kite is a structured design for addressing resource competition risk, rather than the common market narrative of "AI + Payment".
One, resource competition triggered by AI is not a technical issue, but a systemic risk
Resource competition in traditional systems can all be managed through:
Lock
Transaction
Queue
Isolation level
Distributed consistency
To solve this.
But in AI systems, these solutions completely fail because:
No unified memory
No unified state machine
No fixed path
No definite execution order
No strict synchronization process
No predictable resource request rhythm
AI will generate multiple tasks simultaneously, without waiting or coordinating.
Take a common scenario within an enterprise:
Two Agents responsible for the same budget pool will simultaneously believe:
"I have the conditions to execute."
Thus leading to:
Budget is repeatedly occupied
Risk control rules are triggered in advance
Cross-border quotas are used up in a short time
Vendor API has been breached
Task chains interfering with each other
What enterprises see is not AI errors, but systems falling into resource chaos.
This is why resource governance will become the most critical infrastructure issue in the era of AI automation.
Two, the role of Kite's Passport in resource governance: defining "resource accessibility boundaries"
The essence of Passport is not to "issue certificates to Agents", but to:
Define the range of resources each Agent can access.
It will specify:
Accessible budget
Accessible vendors
Executable task types
Callable link
Accessible API
Consumable quota
Can cross regions
Risk level can be undertaken
In other words, when multiple AIs want to access the same resource, Passport is the first boundary of resource governance:
Which Agents can access
How much access
Access frequency
Access order
Access permission level
It avoids loss of control over resource competition from the entry point.
Three, Modules are responsible for the "verifiable execution process" of resource governance
Resource competition occurs not only at the entry but also during execution.
The budget module is responsible for the verifiability of budget deductions
The risk control module is responsible for unified judgment of risk conditions
The path module is responsible for routing consistency
The compliance module is responsible for ensuring cross-border conditions are not bypassed
The payment module ensures that settlement actions cannot be overridden
The audit module is responsible for recording every step of resource usage
When multiple Agents simultaneously request resources, Modules will provide:
Unified judgment
Unified rules
Unified execution
Unified rejection mechanism
The most challenging aspect of resource governance is not permission control, but ensuring:
"Every resource usage behavior must be verified by the same set of rules."
This is the prerequisite for making automation controllable.
Four, on-chain structure is key to resource governance: resource competition must be verifiable
The most feared resource issue within enterprises is not occupation, but inability to verify:
Who exactly deducted the budget
Who decides this path
Why is vendor calling triggered
Why is the cross-border quota consumed?
Why is risk control triggered early
On-chain mechanisms give resource competition three key attributes:
Immutable
Replayable
Can be aligned
This allows enterprises for the first time to:
Clarify responsibilities
Rebuild execution trajectories
Define the legality of each resource action
On-chain records are not for "on-chain", but to provide the "unified source of truth" required for resource governance.
Five, stablecoins in resource governance solve not volatility, but the "quantification of resources" problem
The most feared thing in resource governance is:
Resource quantity is unstable.
If using volatile assets:
Budgets are no longer fixed numbers
Execution costs are unpredictable
Cross-border fees can cause path drift
Vendor routing will reverse due to cost changes
Risk control thresholds will be influenced by external prices
This will lead to a complete failure of resource governance.
Stablecoins make resources:
Quantifiable
Comparable
Reproducible
Auditable
Ensure that every resource competition has the same measurement basis.
Six, resource competition governance will become the most important foundational capability for future enterprise automation
The reason is simple:
Enterprise resources are limited
Unlimited number of Agents
Resource competition can occur:
Multiple Agents simultaneously competing for budgets
Multiple Agents simultaneously triggering payments
Multiple Agents simultaneously calling vendors
Multiple Agents executing cross-border simultaneously
Multiple Agents simultaneously modifying system states
Multiple Agents simultaneously generating task chains
Without a resource governance layer, the larger the scale of automation, the more chaotic the enterprise.
Kite's structure precisely addresses this problem:
Make resource competition controllable
Make resource conflicts mediable
Maintain consistency of execution paths under resource pressure
Ensure that vendor calls do not become chaotic due to competition
Ensure that budgets never encounter "preemptive errors"
Seven, I believe Kite's overlooked real positioning is: "AI Resource Governance Layer"
It is not:
Payment system
Identity system
AI tool chain
Execution framework
It is more like:
Resource Governance Layer
(Resource Governance Layer)
Its task is:
Define resource access boundaries
Verify resource consumption processes
Record resource usage trajectory
Ensure the controllability of resource competition
This is the problem that AI automation must solve to enter the real world.
Kite's structure is inherently suitable for addressing the chaos of resource competition, rather than solving a single function.
For future businesses to allow AI to handle real operations, resource governance is an unavoidable aspect, and Kite is addressing this layer.
