PacioliDouble-entry bookkeeping for AI agents

The receipt for what your agent did

Did your AI actually
do what it said?

Agents book, buy, and subscribe on your behalf — then report it went fine. Pacioli keeps the books. Every action is entered twice — what the agent claimed beside what the evidence shows— and prints a receipt when they don’t reconcile.

Spend trackers show you what was charged. Pacioli proves whether your agent told you the truth.

Pick a scenario, or edit any field

Deterministic rules first · LLM judge on the residual

The engine runs deterministic rules in your browser — instant, no account, no key. Overspend, unauthorized recurrence, and scope creep are exact. Fuzzy claim-vs-evidence mismatches (“cheapest”, wrong item or date) are abstained and routed to the LLM judge, which marks every finding LLM-assisted.

Pacioli™
Agent Action Receipt
No. 0042Jun 14
chatgpt-agent
UA 1542 SFO–AUS nonstop
“Book the cheapest nonstop to Austin on the 14th, under $300.”
Claimed
Actual
book one flight
Scope
UA 1542 SFO–AUS nonstop, seat selection, trip insuranceunrequested add-on "trip insurance" billed beyond the authorized scope
within $300.00
Money
$378.00over budget by $78.00
Out of balanceΔ +$78.00 · 2 discrepancies
Likely cause
Undisclosed add-ons or fees
PCL · 0042 · UNITEDAIRL · MMXXVI

The Weekly Close

A week of agent activity, reconciled.

Individual receipts are the entries; the Weekly Close is the digest — every action your agents took, how much moved, and the handful that need your eyes. Here it is over the demo scenarios, computed by the same engine. Sign off to close the books.

— Weekly Close —
Z-Report
5 agent actions · 5 scenarios
5
Actions
2
Balanced
3
Need your eyes
$935.99
Moved
40%
Reconciled this week
⚑ 3 entries don't reconcile
UA 1542 SFO–AUS nonstopover budget by $78.00
+$78.00
Stackly Pro — Monthlyunauthorized recurring/subscription charge
+$14.99
Uplift V2 Standing Deskmoney spent when purchase was not authorized
+$329.00
reconcile
Sign off to close the books
Z · WK23

Deterministic-first

Rules before the model

Overspend, unauthorized recurrence, and scope creep are exact, auditable rules — run right in your browser. The LLM judge only touches the fuzzy residual, and every judgment is badged.

Measured, not claimed

A per-class eval, reproducible

The engine is scored per class against a labeled dataset — precision and recall, honest weak spots included. Run it yourself in one command.

See the methods →

Not hypothetical

Real agent failures

Documented, source-cited cases — Air Canada, Replit, a $1 Tahoe — rendered as receipts. The harm is real; the books didn't balance.

Open the ledger →