The industry is treating KYA and KYB as competing frameworks for agentic identity. They're not — they solve different problems at different layers. Here's how they fit together.
The rise of AI agents in financial and commercial workflows has generated a new framework: Know Your Agent, or KYA. The premise is intuitive. If KYC asks "who is this person?" and KYB asks "what is this business?", then KYA asks "what is this AI agent, and who authorized it to act?"
KYA addresses a real and urgent problem. But as adoption grows, the industry is beginning to conflate KYA with KYB, or to treat KYA as a replacement for business identity verification in agentic workflows. This conflation creates blind spots.
KYA and KYB solve different trust problems at different layers of the agentic commerce stack. Understanding the difference, and how the two frameworks complement each other, is necessary for anyone building or evaluating trust infrastructure for agentic B2B interactions.
Know Your Agent frameworks focus on verifying the AI agent itself as an actor in a transaction or workflow. The core questions KYA asks are:
These questions arise from a specific problem: in traditional commerce, the parties to a transaction are identified humans or known organizations. In agentic commerce, one or both parties may be an AI system operating autonomously. Existing identity frameworks assume human actors and break down when the "customer" is a software agent acting on someone's behalf.
KYA frameworks respond to this gap by extending identity verification to the agent layer. Proposed mechanisms include digital agent credentials (tamper-proof attestations of who built the agent, who authorized it, and what it's permitted to do) and dynamic identity verification (real-time confirmation that an agent is operating within scope). Agent audit trails, which maintain accountability through automated action chains, are a third pillar.
KYA is most acutely needed in contexts where AI agents are consumer-facing or payment-initiating:
In these contexts, the merchant, service provider, or financial institution needs to know: who is this agent, who authorized it, and is it operating within sanctioned boundaries? These are the questions KYA addresses.
KYB addresses a distinct set of questions. Where KYA asks about the agent itself, KYB in agentic contexts asks about the business entity the agent is representing, interacting with, or making decisions about.
In automated B2B workflows, this manifests in several ways:
Agent-driven onboarding. An AI agent processes a business's application for a financial product, merchant account, or commercial relationship. KYB is the process of verifying that the applying business is real, legitimate, and safe to transact with. The fact that an agent is processing the application doesn't change the KYB requirement; it changes how that requirement is fulfilled.
Agent-to-agent commerce. When an AI agent acting on behalf of Company A initiates a transaction with a service operated by Company B, the trust question is not only "is this agent authorized?" (KYA) but "is Company B a legitimate, non-sanctioned entity?" (KYB). Both questions are necessary.
AI-assisted due diligence. An AI agent conducting background research on a business counterparty, supplier, or acquisition target needs reliable business identity data. The quality of the agent's conclusions depends on the quality of that data. This is a KYB infrastructure problem, not a KYA problem.
Continuous risk monitoring. Agentic workflows increasingly handle ongoing monitoring of business relationships: checking for ownership changes, sanctions exposures, adverse media, or license suspensions. This is KYB (specifically, perpetual KYB) operated by agents.
In all of these cases, KYB is not about verifying the agent. It's about verifying the businesses the agent interacts with, represents, or makes decisions about. These are structurally different trust problems.
The messenger analogy is worth holding. KYA verifies that the messenger is authorized and acting within scope. KYB verifies that the parties the messenger is connecting are legitimate. Both matter; neither substitutes for the other.
KYA and KYB are distinct frameworks, but they intersect in several important places.
Every AI agent has a principal, an organization or individual responsible for its deployment. In B2B contexts, this is typically a business. KYA frameworks that verify the agent's authorization chain ultimately trace back to a business entity. That entity is subject to KYB.
A merchant acquirer evaluating whether to accept transactions from an AI payment agent needs to answer both: Is this agent authorized by a legitimate principal? (KYA) Is the principal business legitimate, not sanctioned, and in good standing? (KYB).
Stated differently: KYA verification of an agent may require KYB verification of the business that deployed it.
The most common intersection is simply AI agents performing KYB verification. An automated onboarding workflow where an AI agent collects documents, queries business registries, traverses ownership chains, and makes or recommends a verification decision is a KYB workflow conducted by an agent.
For this workflow to be trustworthy, two things must be true simultaneously: the agent must be operating within its authorized scope (KYA) and the business data it retrieves and reasons over must be reliable (KYB data infrastructure). A poorly authorized agent can subvert the workflow. A poorly grounded data layer means the agent's conclusions are unreliable regardless of its authorization.
In multi-agent architectures (where one AI orchestrates others), both frameworks apply at multiple levels. The orchestrating agent's scope and authorization are KYA questions. The businesses that orchestrated agents interact with are KYB questions. The agents receiving instructions from the orchestrator have their own KYA considerations.
Trust in multi-agent B2B workflows requires both frameworks operating together, not one substituting for the other.
As the industry develops standards and tooling for agentic identity, several conflations are worth naming.
KYA as a replacement for KYB in agent-driven onboarding. Some framings suggest that once an AI agent is KYA-verified, its outputs (including business verification decisions) can be trusted without examining the underlying data quality. This is not correct. KYA establishes that the agent is authorized and uncompromised; it says nothing about whether the business data the agent retrieved was accurate, fresh, or correctly attributed to the right entity.
KYB as sufficient for agentic commerce. The inverse error is assuming traditional KYB workflows are adequate when the applicant is using an AI agent. This misses the novel questions KYA raises. If an AI agent can submit falsified documents, claim authorizations it doesn't have, or operate outside its principal's sanctioned scope, KYB verification of the business doesn't catch those failures.
Treating KYA as only relevant for consumer contexts. Most KYA content focuses on consumer-facing scenarios (shopping agents, personal finance agents). But KYA questions are equally relevant in B2B contexts: a procurement agent submitting purchase orders, a contract execution agent signing agreements, an accounts payable agent initiating wire transfers. B2B platforms need to address all of these.
For organizations building or evaluating trust infrastructure for agentic B2B interactions, a complete framework requires both layers:
At the agent layer (KYA):
At the business identity layer (KYB):
A transaction where an authorized agent (KYA-verified) interacts with a legitimately identified business counterparty (KYB-verified) is the target state. Failing either layer creates distinct risk:
Neither layer protects against the other's failure mode.