Category guide
Spend Management for AI Agents
Budgets, recipient rules, and approval thresholds for agents that pay for work, and where each check has to run.
Published · Updated
Give an agent a budget it can't exceed, a list of who it can pay, and a record of every decision. Run the checks before the money moves, not in next month's expense report.
What spend management for AI agents means
An AI agent that can pay is a new kind of spender. It can buy a service, call a paid API, issue a refund, or send a payout on its own. Spend management is how a team decides, in advance, what that spender is allowed to do and when a person needs to sign off.
A budget written into a prompt is only a suggestion. The agent can miscount it or ignore it. Controls for agentic payments sit in the payment path itself, where the request is checked against real limits before the vendor is paid. If the request fits policy, the agent pays. If it doesn't, the payment stops or is held for a person.
Why it isn't expense management
Human expense management mostly happens after the fact. A person spends, keeps a receipt, and finance reconciles it later. That model assumes a slow, human pace and a person who can be asked to explain a charge.
Agents don't fit that model. They can spend quickly, at high frequency, and pick the vendor or amount while the workflow runs. By the time a charge shows up in a report, the money is already gone. Spend management for agents moves the decision up to the request itself. That's the last point where a limit can still stop it.
The guardrails that keep an agent inside policy
Guardrails for agentic payments are the specific rules a payment is checked against. Conto evaluates them together, and a single failure can stop or hold the payment.
Spend limits
Velocity rules
Recipient allowlists
Approval thresholds
Trust checks
Audit trail
The same checks apply whether the agent pays with a stablecoin or a protocol like x402.
Five requests, one policy
A procurement agent has a $200 daily budget, a $50 line above which a person has to approve, a $150 hard cap per payment, and three approved data vendors.
$18 to vendor A clears. $75 to vendor A is held until a person approves it. $18 to a vendor not on the list stops. $160 to vendor A stops too, and nobody is asked, because it's over the hard cap. Late in the day, with $190 already spent, a $25 request to vendor A stops as well: the daily budget has $10 left.
Each answer is stored with the request that produced it: the amount, the recipient, the rule that fired, and who approved the exception if anyone did.
Where the spend decision happens
The agent requests a payment, Conto checks it against policy, routes exceptions to a person, and records the decision. The payment then settles on whatever rail the agent's wallet uses.
Managed wallet
Conto decides, the wallet sends · Conto holds the rules and the record
Agent requests a payment
POST /payments/requestConto checks owner policy
Owner’s approver decides
Routine spend skips this step.
Buyer wallet sends funds
Once the request clears, the managed wallet signs and sends. Conto holds the decision and the record.
POST /payments/:id/executeLogged with full context
tx 0x9f2c… ✓
policy · approval · settled
Agent-controlled wallet
Agent keeps its own keys · Conto authorizes, logs on confirm
Agent asks to authorize
POST /payments/approveConto checks owner policy
Owner’s approver decides
Routine spend skips this step.
Buyer’s signer sends
Conto returns a 10-minute approval. The agent sends the transaction.
Agent reports the transaction ID
POST /payments/:id/confirmtx 0x9f2c… ✓
policy · approval · settled
Same payment context, same policy evaluation, same audit trail. Who signs and sends is where the path diverges.
Where Conto fits
Conto runs these checks as a policy engine in front of the agent's wallet. Everything above applies whether or not you use it.
Frequently asked questions
How is this different from setting a budget in the prompt?
A budget in a prompt is a suggestion the agent can ignore or miscalculate. Controls for agentic payments sit in the payment path and are enforced on every request, so a limit holds even when the agent is wrong.
What happens when an agent hits its daily cap halfway through a task?
The request that would cross the cap stops, and the agent gets a denial it can act on: finish with what it already bought, wait for the budget to reset, or ask a person to raise the limit. If a task can't be left half done, set the approval threshold below the cap so the last few payments are held for a person instead of denied.
Who should own the limits, engineering or finance?
Finance owns the numbers and the recipient list, because those are budget decisions. Engineering owns where the check runs, because a limit only holds if it sits in the payment path. Keep the policy somewhere both teams can read it and change it, with a record of who changed what.
Do limits still apply when the agent pays from its own wallet?
Yes, as long as the agent asks for a decision first and reports the result back afterward. The check is the same; the difference is that the agent's wallet signs the payment instead of a managed one. If the agent can skip the ask, the limit is a suggestion again.
Related Conto resources
Set spend limits for your first agent
Choose the agent, its budget, the recipients it can pay, and the threshold that routes a payment to a person. Watch the policy check run in the sandbox before you go live.
Next guide
How to Control AI Agent Spending