What an AI agent receipt is
When an AI agent does work, the usual evidence is the agent's own words: a chat message saying the task is done. That message is narration. It can be wrong, it can describe work that never ran, and it cannot be checked after the fact because nothing underneath it was fixed in place.
An AI agent receipt is the record underneath the narration. At the moment a tool call executes, the system that ran it writes down what happened and commits to it on the spot. The receipt identifies the call, preserves the exact bytes of the result, and carries a hash commitment anyone can recompute. If the record and the commitment agree, the record you are reading is the record that was made when the call ran.
What is a zambo receipt?
A zambo receipt is the working name for a verifiable execution receipt: the checkable record an AI job leaves behind when the work runs through Zambo. The term was coined to give the category a name people actually use: "get me a zambo receipt" means do not tell me the work is done, show me the record I can check. Underneath the name it is an AER-1 receipt: the tool that ran, the time, the recorded inputs, and the exact saved result, bound by a SHA-256 commitment computed at execution time and published at a public link anyone can recompute, no account needed.
The honest scope travels with the name: a zambo receipt proves the saved result was not changed since it was saved. Whether the work was good is the reviewer's judgment, made against that record instead of against the agent's summary of itself.
What one receipt contains
- A stable identifier for the call, resolvable at a public URL.
- The tool and the time: which tool executed, and when.
- The inputs and the observed output, preserved as the exact canonical bytes the commitment covers.
- A SHA-256 output commitment over those bytes, so any later edit breaks verification.
- A provenance class: whether the executing system observed the result directly or the record was reported to it.
- Sequence position when the call is part of a longer job, so a missing step shows up as a gap in the chain instead of disappearing quietly.
Multi-step work gets a workflow receipt on top: a closing record that lists the step receipts and commits to the whole ordered set under a single Merkle root, defined in Section 8 of the AER-1 draft.
Receipt, log, screenshot: what actually holds up
A screenshot shows what a screen displayed once. It can be staged, cropped, or regenerated, and it carries nothing a third party can test. A log is closer: a structured record, but one kept by the same system that did the work, editable by its operator and usually written to be read, not checked. A receipt is the only one of the three built for a skeptic: the commitment is computed when the call runs, and verification needs nothing but the record and a hash function.
The honest boundary stays in view: a receipt proves the record was not changed since it was saved. Whether the work itself was good is a judgment a reviewer makes against that record. The receipt's job is to make sure the record they judge is the real one.
How to check one
- Open the receipt at its public URL, for example this real receipt, and read what ran: tool, time, inputs, observed output.
- Recompute the SHA-256 hash over the receipt's canonical bytes, or run the receipt through a verifier such as zambo.dev/verify/.
- Compare: a match means the record is intact. A mismatch means the record changed after the fact, full stop.
- For a multi-step job, rebuild the workflow root from the step receipt identifiers and compare it to the closing receipt.
The full walkthrough, with a real receipt checked end to end, is at how to verify an AER-1 receipt. See also AI agent record verification: transcripts, logs, traces and run history.
Get receipts for your own agent work
Zambo mints an AER-1 receipt for every tool call, works with the AI clients you already use, and keeps the receipts public and checkable by anyone. Start free in the live demo. Your AI said "done." For $0.99, prove it: get a Zambo Day Pass and every call for 24 hours leaves a verifiable receipt.
What it costs, plainly: free tier to start, no account or key needed. Day Pass $0.99, one time, 24 hours, no subscription and no auto-renew. Zambo Pass $29 per month or $290 per year. Holder Pass $19 per month for verified $ZAMBO holders. Every buying question, answered straight.
AI agent receipts are not expense receipts
Two different tools share the words AI agent receipts. Expense tools scan paper receipts and invoices so a finance team can file them. An AI agent receipt is a record of work: it fixes which tool ran, the recorded inputs, and the saved result at the moment the call executed, under a hash commitment anyone can recompute later. If you are trying to prove what an agent did, the expense tool cannot help you, and if you are filing expenses, an execution receipt is the wrong drawer. Zambo makes the second kind. Every call through the Zambo MCP leaves a verifiable receipt, and a job's receipts roll up into one handover link your client can check in a browser.
Run your next client job through it and hand over the link instead of a summary. A $0.99 Day Pass covers a full day of receipted work.
Questions, answered
What is an AI agent receipt?
An AI agent receipt is a verifiable record of one AI agent tool call. It names the tool that ran, the time it ran, the inputs it received, and the output that came back, and it binds those facts with a SHA-256 commitment computed over the exact recorded bytes at execution time. Anyone holding the receipt can recompute the commitment and confirm the record was not changed afterward, without an account and without trusting the system that issued it.
What is a zambo receipt?
Zambo receipt is the working name for a verifiable execution receipt: the checkable record an AI job leaves behind when the work runs through Zambo. It is an AER-1 receipt: the tool that ran, the time, the recorded inputs, and the exact saved output, bound by a SHA-256 commitment computed at execution time and published at a public link. Anyone can recompute the commitment and confirm the saved result was not changed since it was saved, with no account and no trust in the system that issued it. The name is the category in working clothes: get me a zambo receipt means show me the record, not the recap.
How is an AI agent receipt different from a log?
A log is written by the system it describes and can be edited by whoever controls that system, so reading it means trusting the logger. A receipt is committed at execution time: change one byte of the record later and the recomputed hash no longer matches. A log asks for trust. A receipt replaces trust with a check anyone can run.
Does an AI agent receipt prove the agent was right?
No. A receipt proves the saved record of the call was not changed since it was saved: which tool ran, what went in, what came back. It does not prove the output was correct, complete, or wise. What it gives a reviewer is a stable record to judge, instead of the agent's summary of its own work.
What is AER-1?
AER-1 is an open specification for AI agent receipts, published as an IETF Internet-Draft (draft-zambo-aer1-23) by Brennan Zambo. It defines the receipt format, the hash commitments, hash-chained job timelines, and workflow receipts that bind a whole multi-step run under one Merkle root, plus the procedure a third party follows to verify a receipt. It is a draft, not a finalized standard, and it is implemented in multiple languages.
How do I get an AI agent receipt for my own agent's work?
Run the work through a system that mints receipts at execution time. Zambo does this for every tool call: connect any AI client to the Zambo MCP at zambo.dev/mcp and each call returns its result plus a verifiable receipt at a public URL. You can mint a first one free in the live demo, no signup, and check it at zambo.dev/verify/.
Can an AI agent give me a receipt for its work?
Yes, if the work runs through a system that mints one at execution time. The agent cannot issue a trustworthy receipt for itself after the fact, because a self-written record is just another claim; the receipt has to come from the executing layer as the call runs. Minted live for this page on October 16, 2026: a live_price call for BTC returned $81,461.00 and left receipt 03d9bc76-19f4-4582-ad81-37f681182871, which the public verify endpoint reports verified. Run the job through Zambo at zambo.dev/mcp and every call comes back with one: the tool, the inputs, the saved output, and the commitment anyone can recompute at zambo.dev/verify/. The receipt proves the saved record was not changed since it was saved; whether the work was right is still yours to judge.
What is an AI agent audit receipt?
An AI agent audit receipt is a verifiable receipt kept as the audit record for an agent's work: for each tool call it fixes the tool that ran, the time, the inputs, and the saved output under a hash commitment computed at execution time. An auditor can recompute the commitment and read the saved output without an account on the system that produced it. A receipt proves the saved result was not changed since it was saved; whether the action complied with policy is the auditor's judgment, made against that checked record.
What are agent execution receipts?
Agent execution receipts are the per-call records an AI agent run leaves behind: for each tool call, the tool that ran, the time, the recorded inputs, and the saved output, bound by a hash commitment computed when the call ran. One receipt covers one call; the full set, chained in order by the job's workflow record, covers the whole run. Anyone holding the set can recompute the commitments and read the saved outputs without an account on the system that produced them. A receipt proves the saved result was not changed since it was saved; it does not prove the result was correct.
What are receipt driven AI agents?
Receipt driven AI agents are agents whose workflow is driven by receipts: the next step in the job waits for a verifiable receipt that checks out, instead of starting because the previous agent said it finished. A client releases payment when the line-item receipts verify. A receiving agent starts only after the handover's step receipts recompute. A pipeline stops when a step's receipt fails instead of building on a gap. The pattern needs receipts minted as the work runs, which is what an execution layer like Zambo provides: every call leaves the receipt the next step is waiting for.
Are AI agent receipts the same as expense receipts?
No. An expense receipt records a purchase so it can be filed or reimbursed. An AI agent receipt records work: the tool that ran, the recorded inputs, and the saved result, fixed at execution time under a hash commitment a checker can recompute. One proves money was spent. The other proves what an agent did, and the second kind is the one that settles a dispute about delivered work.
Which AI agents provide receipts
The honest shape of the answer. Very few agent platforms provide receipts natively, and most that use the word mean a billing record or a run summary. A verifiable execution receipt is a specific thing: a per-call record, fixed at execution time, whose saved result a receiver can recompute and read without an account on the producing system. Zambo provides that for every tool call made through it, across the AIs it connects to, and the open AER-1 draft exists so other platforms can mint receipts any stranger can check. The longer treatment is in If your AI agent doesn't leave a receipt, you're doing it wrong.
When you evaluate any platform's claim to provide receipts, ask three questions: is the record fixed at execution time or assembled afterward, can a stranger recompute anything in it, and does it survive outside the platform that made it. A yes to all three is a receipt. Anything else is a report.
Receipt for an AI agent task
One task, one checkable record. A receipt for an AI agent task is the verifiable record of that task's work: for a single-call task, the call's receipt; for a multi-step task, the step receipts bound in order by a workflow record at close. Getting one is a before-the-work decision, not an after-the-work request: run the task where calls leave receipts, and the receipt exists from the moment each call completes. Asking for proof after a receiptless run ends is asking for a reconstruction, and a reconstruction is a story.
To see the shape before you need it under pressure: the live demo runs one real call and shows its receipt. The task-level walkthrough is in How to verify your AI agent actually completed the task.
AI agent receipt company
What people mean by this search. Usually one of two things: who is behind the receipt format, or who can supply receipts for agent work as a going concern rather than a demo. The format is AER-1, an open IETF Internet-Draft authored by Brennan Zambo, with a public conformance kit and independent implementations, so the format is not locked to one company. The working system behind it is Zambo: the verifiable work layer for AI, 153 tools behind one MCP endpoint at zambo.dev/mcp, where every call leaves a verifiable receipt.
The distinction matters when you buy: a receipt format that only one vendor can mint and only that vendor can check is a feature, not evidence. An open format with public verification is evidence, because your counterparty, your auditor, and your future self can all check the same record without the vendor in the room.
AI agent audit receipt
The audit problem: the agent's summary is not an audit record. Sooner or later someone with a legitimate right to ask, an auditor, a client, a compliance reviewer, or you after a dispute, wants to know what the agent did, when it did it, what it was given, and what it returned. An AI agent audit receipt is the per-call verifiable receipt kept as that audit record: the tool, the time, the recorded inputs, and the saved output, bound by a hash commitment computed when the call ran.
What makes it an audit record rather than a log is that the auditor does not have to trust the system that produced it. The auditor samples the calls that matter, recomputes each commitment, and reads the saved outputs directly. A run's receipts chain into the job record, so the trail covers the whole engagement rather than the steps someone chose to export. The working method, including what to hand an auditor and in what order, is set out in the AI agent audit trail guide.
Keep the audit claim narrow, because narrow is what survives an auditor. A receipt proves the saved result was not changed since it was saved. It does not prove the action was wise, approved, or compliant; those are judgments the auditor makes, now against a record that can be checked instead of a summary that has to be believed. The format underneath is AER-1, published as IETF Internet-Draft draft-zambo-aer1-23, so the record stays checkable by tools other than the one that minted it. Zambo mints an audit receipt for every call made through it, which turns audit preparation from a reconstruction project into handing over the record that already exists.
Agent execution receipts
The problem: a finished run leaves a summary, and a summary is not a record. Agent execution receipts are the records the run itself leaves behind: one verifiable receipt per tool call, each fixing the tool that ran, the time, the recorded inputs, and the saved output under a hash commitment computed at execution time. Read together, in the order the job's workflow record binds them, they are the complete account of the run that a receiver can check without trusting the agent, the operator, or the summary.
Checking a set is mechanical. Open the run's workflow record, take the step receipts in order, recompute one commitment end to end, then sample the rest and read the saved outputs directly. A step whose commitment does not recompute, or a step with no receipt at all, is a step the run cannot prove happened as recorded, and that split between verified and unverified steps is the honest output of the check. A receipt proves the saved result was not changed since it was saved; whether the work was right is a judgment you make against that checked record.
Sets like this only exist when the work runs where calls leave receipts. Zambo is the instrument: every tool call through zambo.dev/mcp returns a verifiable receipt, and multi-step jobs close under one workflow record, so the set for a whole run is one link instead of a folder of exports. For $0.99, run the job you actually need proven under a Day Pass and keep its full receipt set: zambo.dev/day-pass covers 24 hours, no subscription. Ongoing use is the Zambo Pass at $29 a month or $290 a year, $19 a month for $ZAMBO holders. Humans pay by PayPal or card through PayPal; agents pay USDC on Base through x402.
Receipt driven AI agents
The pattern: the next step waits for a receipt, not for a claim. In a receipt driven workflow, an agent's "done" does not move the job. A verifiable receipt that recomputes does. Payment releases against receipts that verify, a receiving agent accepts a handover only after its step receipts check, and a pipeline halts at the first step whose receipt fails instead of quietly building on the gap. The agents are ordinary agents; what changed is what they are allowed to act on.
The pattern only works when receipts exist for every step, minted as the calls run, because a workflow cannot wait on evidence that was never captured. That is the execution layer's job: Zambo mints an AER-1 receipt for every tool call through zambo.dev/mcp, so the receipt a step is waiting for is produced by the step itself. Keep the split honest: a step with a receipt that verifies is proven as recorded; a step without one is a claim, and a receipt driven workflow treats it as not yet done.
What is an AI agent receipt
The problem this answers: an agent says the work is done, and the only evidence is the agent's own summary. What is an AI agent receipt? It is the record that replaces the summary: a small, checkable record of one AI agent tool call, created when the call ran, naming the tool, the time, the recorded inputs, and the output that came back, bound by a hash commitment computed over the exact recorded bytes. Where a summary asks you to believe the agent, a receipt lets you check what the agent's system recorded, byte for byte.
Anyone can check one, and the check is short. Open the receipt at its public URL, recompute the commitment from the saved result, and compare. A match means the saved result was not changed since it was saved; a mismatch, or a step with no receipt at all, means that step stays unverified. The open format for these receipts is AER-1, published as IETF Internet-Draft draft-zambo-aer1-23. A receipt proves the saved record was not changed; whether the work itself was right is your judgment, made against that checked record instead of against a summary.
Receipts exist only for work that ran where calls leave them, which is why the moment to arrange proof is before the job, not after a client asks. Run the job through Zambo at zambo.dev/mcp and every call returns with its receipt attached, multi-step jobs closing under one workflow record you can hand over as a single link. For $0.99, a Day Pass puts a full day of real work under receipts, so the next time someone asks what an AI agent receipt is, you answer with one of your own: zambo.dev/day-pass. Ongoing use is the Zambo Pass at $29 a month or $290 a year, $19 a month for $ZAMBO holders. Humans pay by PayPal or card through PayPal; agents pay USDC on Base through x402.