How to Audit AI Agent Action Usage With Aident Loadout

An AI agent audit should answer five questions without exposing the agent's prompts or provider payloads: which external Action ran, which integration handled it, whether the call succeeded, where the request came from, and how many Aident credits it used. Aident Loadout exposes that boundary through the installed aident CLI.

This is different from model-token reporting. Your model provider can explain token spend, and an application trace can explain the agent's reasoning path. Loadout audit history explains the external Action calls that connected the agent to real services. If you are investigating local Claude Code token records, use the separate JSONL token-usage workflow.

The Three Ledgers an Agent Operator Needs

Do not force every cost and reliability question into one dashboard.

Agent cost views can estimate individual model-request cost from provider pricing and token counts. That remains a different meter from a paid search, media generation, enrichment, or other external Action.

OpenTelemetry's generative AI conventions define attributes for tool calls and warn that tool arguments and results can contain sensitive information. That is a useful reason to keep an operational audit compact. You need enough identity, outcome, timing, and cost context to investigate a run without turning every audit export into a copy of the provider payload.

Audit history explains what already ran. To limit spend before a call, pair it with AI agent cost guardrails and capability preflight.

1. Confirm the Account Before Reading the Audit

Install or update Aident Loadout from the canonical setup guide:

https://aident.ai/SETUP.md

Then confirm which account the CLI will inspect:

aident account auth status

Stop if the command is unauthenticated or points to an unexpected account. An audit run is only meaningful when its account boundary matches the agent activity you are investigating.

2. Start With a Bounded Summary

For a quick recent view:

aident audit summary --limit 100

The summary reports these aggregate fields:

• total, successful, and failed calls

• total Aident credits and total duration

• calls, errors, and credits grouped by integration

• calls grouped by request source and status

• the most frequently used Actions

The --limit value is the maximum number of usage rows inspected. It is not a time boundary. For a reproducible investigation, add explicit ISO timestamps:

aident audit summary \
  --dateFrom 2026-08-10T00:00:00Z \
  --dateTo 2026-08-11T00:00:00Z \
  --limit 100

Record the window and row limit with any conclusion. If the selected period contains more rows than the limit, narrow the filters or inspect additional bounded slices before treating the summary as complete.

3. Slice by Source, Status, or Integration

One aggregate failure rate can hide very different problems. Use one filter at a time to establish where the change occurred.

Separate MCP and CLI callers

aident audit summary --requestSource mcp --limit 100
aident audit summary --requestSource cli --limit 100

This answers whether a spike came from MCP-connected agents or direct CLI execution. It does not prove which prompt or person caused the call.

Isolate failed calls

aident audit summary --status error --limit 100

Compare the same time window with --status success. A failed Action can reflect invalid input, missing provider access, rate limits, an upstream error, or another execution boundary. The status count tells you where to investigate; it does not diagnose the provider by itself.

Focus on one integration

aident audit summary \
  --integrationId composio:serpapi_tools \
  --limit 100

The integration filter accepts an integration ID or capability prefix. Copy the canonical identity from Loadout discovery rather than guessing it:

aident capabilities search \
  --query "Google search" \
  --targetEnv staging

When a filter returns no rows, first verify the identity, account, time window, and caller source. Do not convert an unverified empty result into a claim that the agent made no calls.

4. Inspect Recent Rows Without Exporting Provider Data

Use recent when the aggregate tells you which slice needs inspection:

aident audit recent --limit 20

Each recent row identifies the Action and integration, request source, status, duration, credit source, credits consumed, request and session identifiers, and timestamp. It does not include the Action input or provider response payload.

That contract is useful for operational review. You can correlate a request ID with a trusted internal trace when necessary, while the routine audit remains small enough to share without publishing prompts, credentials, or customer data.

Team owners can inspect shared-wallet usage with an explicit scope:

aident audit summary --scope team --limit 100

Use team scope only for an authorized team-level review. Default account scope is safer for ordinary debugging.

5. Read Credits and Failures Carefully

The audit is evidence, not an invoice reconstruction.

• totalCredits is the Aident credit deduction recorded for the inspected rows.

• A zero-credit Action means no Aident credits were deducted for that call. It does not prove that every model, provider, or user-owned account involved was free.

• A failed call can still consume time or upstream resources, depending on where it failed.

• A high duration total can come from a few long calls or many short ones. Inspect recent rows before choosing a remedy.

• A request-source split describes the transport boundary, not the business outcome.

For model cost, use the model provider's authoritative usage source. For end-to-end latency and reasoning steps, use your application trace. For external integration operations, use Loadout audit history. Reconcile the three ledgers by timestamp and request identity where the systems expose a safe join key.

A 10-Minute Audit Checklist

1. Confirm the active Aident account.

2. Choose an explicit UTC time window.

3. Run one summary with a stated row limit.

4. Compare mcp and cli request sources.

5. Isolate error rows.

6. Filter the integration that changed.

7. Inspect only the recent rows needed for correlation.

8. Keep prompts, Action inputs, provider responses, and personal data out of the audit report.

9. State whether credits mean Aident credits, model cost, or upstream provider cost.

10. Save the query, window, limit, and conclusion so the next review is comparable.

This sequence turns "the agent used too many tools" into a testable statement: a named integration recorded a specific number of calls, errors, duration, and Aident credits in a bounded window from a known caller source.

Build an Auditable Agent Workflow

Follow https://aident.ai/SETUP.md

Then ask your agent to discover and preflight the required Actions before execution. After the run, use the audit commands above to verify the integration, source, status, duration, and Aident credit boundary.

Set up Aident Loadout and audit your first workflow

Sources

• Aident Loadout setup guide, reviewed August 11, 2026.

• OpenTelemetry generative AI semantic attributes, reviewed August 11, 2026.

• Datadog Agent Observability cost documentation, reviewed August 11, 2026.

• Installed Aident CLI audit summary and audit recent contracts, verified August 11, 2026.

Refresh this guide when the public CLI changes its audit fields or filters, when Loadout adds a new request source or cost boundary, or when the audit history retention contract changes.