Agent payment discussions often conflate “tokenization” with “safety.”
Tokenization helps. But the credential model you choose determines your blast radius and your ability to audit.
In practice, teams end up choosing among three models:
- ▸Dedicated virtual cards (one per agent/workflow)
- ▸Tokenized cards (network tokens, often still tied to a primary funding source)
- ▸Shared / delegated tokens (agent borrows your main credentials with scoped parameters)
Dedicated virtual cards (isolation-first)
What it is: each agent/workflow gets its own card and spend bucket.
Strengths
- ▸fixed blast radius
- ▸clean audit trails (“everything on this card came from this agent”)
- ▸easier reconciliation
Risks
- ▸operational overhead if not automated
Best for: production agents with real money at stake.
Tokenized cards (security-first, not necessarily isolation-first)
What it is: a token represents a card or funding source without exposing the PAN directly.
Strengths
- ▸reduces raw credential exposure
- ▸integrates with existing rails
Risks
- ▸can still draw from a primary credit line
- ▸isolation depends on how the token is scoped and enforced
Tokenization is good hygiene. It’s not a blast radius guarantee.
Shared/delegated tokens (speed-first)
What it is: the agent requests scoped tokens to spend from an existing account.
Strengths
- ▸fast to integrate
- ▸flexible
Risks
- ▸blast radius can expand if policies fail
- ▸compromised agents can request broader scopes
- ▸drift is harder to detect when spend mixes
See: Why AI agents should never share credentials.
The decision rule
If you’re deploying agents in production:
- ▸Use dedicated credentials for isolation
- ▸Use tokenization as hygiene
- ▸Avoid shared tokens unless you can prove constraints are enforced outside the agent’s control
Bottom line
The credential model is the product.
Choose the model that stays safe when the agent is wrong, then layer policy and verification on top.
Looking for agent spending controls? Start with MCP + skills, then choose a plan that fits your workload.