Giving an AI agent access to a wallet creates an accountability problem. A transaction can be valid on-chain while still violating the user’s intended policy. The blockchain proves that a key authorized the transaction; it does not necessarily prove why the agent believed it was allowed to act.

Action receipts can connect user intent, delegated permission, policy checks and final execution.

Permission should be machine-readable

Natural-language instructions such as “spend a little if necessary” are not strong security controls. Agent permissions should define concrete limits: asset type, amount, counterparty, contract, time window and action category.

The execution layer can then enforce those limits independently of the model.

The receipt should record the authorization path

An action receipt can capture agent identity, user or organization identity, policy version, requested transaction, risk checks, approval state and resulting transaction hash.

This creates a trace from “what the agent wanted to do” to “what actually happened.”

Do not store private chain-of-thought

Auditing does not require internal reasoning transcripts. Operational facts are enough: the requested action, applicable policy, data sources used, approval outcome and final execution result.

This keeps the audit record useful without exposing sensitive prompts or proprietary model behavior.

Human approval should be explicit

High-value or unusual transactions may require a person to approve. The receipt should record who approved, what they saw and whether the final transaction matched the approved parameters.

A vague “approved” flag is not sufficient if the amount or recipient changed afterward.

Delegated permissions reduce key exposure

Agents should not need permanent access to a master private key. Short-lived delegation, smart-account policies or session keys can restrict what the agent is able to sign.

Our article on delegated wallet permissions for AI agents explains this model in more detail.

Receipts make failures easier to investigate

If an agent buys the wrong asset or sends funds to an unexpected contract, operators need to know whether the model requested it, the policy incorrectly allowed it, or the execution system transformed the request incorrectly.

A structured receipt separates these layers.

Useful policy controls

• Per-transaction spending limit.
• Daily or weekly aggregate limit.
• Allowlisted assets and contracts.
• Blocked counterparties.
• Human approval above a risk threshold.
• Short-lived authorization windows.
• Immediate revocation.

Receipts can support reputation

Over time, an agent can build a verifiable record of compliant execution without publishing every private instruction. A service can attest that the agent completed certain categories of transactions within policy.

This could become useful in multi-agent markets where one agent needs evidence that another behaves reliably.

Privacy and transparency must be balanced

Not every receipt needs to be public. Public chains may only need a hash or settlement reference, while full policy details remain in an authorized audit system.

The design should disclose enough to resolve disputes without exposing user strategy or sensitive financial behavior.

Use receipts to improve policy over time

Action receipts are not only forensic records. Aggregated receipt data can reveal which policies generate too many manual approvals, which transaction types frequently fail and where agents repeatedly approach spending limits.

Teams can use that evidence to tune permissions without weakening safety. A low-risk transaction that is approved manually hundreds of times may be a candidate for a larger automatic threshold, while a category with frequent reversals may need stricter limits. In this way, receipts become a feedback loop for safer autonomy rather than a log that is reviewed only after incidents.

Receipts can also support external review when an organization needs to prove that an autonomous workflow followed policy. A compliance team does not need access to private prompts; it needs evidence that the transaction stayed within approved limits and that required human approvals occurred. This separation makes agent accountability practical even in regulated environments.

Receipts should be immutable enough for audit but searchable enough for operations. Indexing them by agent, wallet, policy version and transaction category makes it possible to investigate patterns rather than reviewing one transaction at a time.

Autonomy requires bounded accountability

AI agents become easier to trust when every transaction can be tied to a bounded permission rather than an open-ended key. Action receipts do not prevent every error, but they make authorization observable and enforceable.

That becomes increasingly important as agents move from recommending transactions to executing them.