AI agent observability that can prove it.
Observability tells you what your agent did. It won't tell an auditor, a customer, or an incident review that it happened that way — because traces and logs are mutable and you kept them. a2a cloud emits an Ed25519-signed receipt per run: the same inputs, outputs, tool calls, latency, and cost your telemetry carries, but signed. Telemetry you graph and evidence you can prove, from one record.
signed telemetry · per-run cost · replayable
Observability tells you what happened. It can't prove it.
Traces, metrics, and logs are built to help you debug — and they're excellent at it. But they're mutable, operator-controlled, and aggregate. When an agent takes a consequential action and someone later asks whether it really ran that way, under whose authority, and at what cost, a dashboard is the record kept by the party being asked. It answers 'what happened' and goes quiet on 'prove it' — the exact question accountability turns on.
Receipts: telemetry that doubles as evidence.
A receipt carries the same fields your traces do — inputs, outputs, tool calls, latency, cost — plus the caller and the scoped grant, all Ed25519-signed and hash-chained. One record serves the engineer graphing latency and the auditor verifying a decision.
Telemetry that is also evidence
A receipt records inputs, outputs, tool calls, latency, and cost per run — the same fields your traces carry. The difference: it's Ed25519-signed, so the same record you graph is also the record you can prove.
Per-run cost attribution
Cost and elapsed time are captured per tool call and attributed to the caller and grant. You get spend down to the individual action — and it's tied to who authorized it, not just which service emitted it.
Caller and authority in the trace
Standard observability tells you a span ran. A receipt tells you who called, under which scoped grant, with what audience and TTL. Accountability data lives in the telemetry, not in a separate access log.
Tamper-evident by construction
Dashboards read from logs an operator can edit. Receipts hash-chain, so a deleted or altered run breaks the chain. Your observability record can't be quietly rewritten after an incident.
Replay, not just inspect
Observability lets you look at a past run. A receipt lets you re-execute it deterministically — same inputs, same grants, same outputs. Root-cause analysis you can re-run instead of reconstruct.
One record, two audiences
Engineers get latency, cost, and failure telemetry; compliance gets signed, retained, per-decision evidence — from the same receipt. No second pipeline to reconcile the two views.
Traditional telemetry vs. signed receipts.
Frequently asked.
What's the difference between AI agent observability and accountability?
Observability tells you what happened — traces, metrics, and logs you inspect to debug behavior. Accountability lets you prove what happened to someone who wasn't there and may not trust you. On a2a cloud a signed receipt does both: it carries the same inputs, outputs, tool calls, latency, and cost your telemetry would, but it's Ed25519-signed and hash-chained, so the record you graph is also the record you can prove.
Can signed receipts replace my observability stack?
They complement it. Receipts give you per-run telemetry — inputs, outputs, tool calls, latency, and per-tool-call cost — that doubles as tamper-evident evidence, so you often don't need a separate audit pipeline. You may still run APM or tracing for infrastructure-level signals. What receipts add is the accountability layer most observability tools lack: a signed, retained, per-decision record.
How does per-run cost attribution work?
Each receipt captures cost and elapsed time per tool call and attributes them to the caller and the scoped grant that authorized the run. That means spend is broken down to the individual action and tied to who invoked it — useful for chargeback, budget alerts, and spotting a runaway agent, with the attribution grounded in signed evidence rather than aggregate service metrics.
Why aren't traces and logs enough for AI agents?
Traces and logs are mutable and operator-controlled, so they answer 'what happened' but not 'can you prove it.' For an autonomous agent taking consequential actions, an incident review, customer, or regulator wants a record the audited party can't have edited. Receipts add signing, hash-chaining, retention, and the authorizing grant on top of the same telemetry — turning observability into something you can stand behind.
Does receipt-based observability support replay?
Yes. Because a receipt captures the inputs and the grants a run executed under, you can deterministically replay it — same inputs, same grants, same outputs — instead of reconstructing state by hand from spans. That makes root-cause analysis a re-run rather than a reconstruction, and lets a verifier re-derive the result independently.
Related guides.
All guides live in the guides index.
Graph it. Then prove it.
a2a cloud deploys any agent — LangGraph, OpenAI Agents SDK, CrewAI, or custom — with a managed Postgres database, an MCP server, an API, a frontend, and an Ed25519-signed receipt for every run. Per-tool-call cost attribution, the authorizing grant in the record, deterministic replay. Observability and accountability from the same signed source.