Audit Your Agent Spend

Financial intelligence for coding agents

Prove which coding agents pay off.

Connect every model call, tool action, retry, and failure to an accepted task. Compare completed-work costs, eliminate wasted spend, and defend your coding-agent ROI.

Audit Your Agent SpendFollow task ENG-2841

Get more accepted work from every coding-agent dollar.

Task outcome
Accepted
Cost provenance
8 recorded events
Acceptance evidence
Linear issue closed

One task. One proof chain.

Raw usage becomes a business result you can defend.

Task receiptENG-2841analytics-api
AcceptedLinear issue closedVerified acceptance evidence
Cost sourceEventsRecorded
Model calls3$5.18
Tool calls2$1.42
Context + retry2$1.75
Compare model costsComparable accepted work · same acceptance ruleView all models ↓
GLM-5.2$24.70
ChatGPT 5.6 Terra$26.40
Kimi K3$28.90
7 eventsEvery dollar traceableCost per accepted task$8.35
Inspect the evidence and financial states
RecordedObserved activityBilledProvider invoiceEstimatedDerived before closeForecastProjected month end

All task records, organizations, provider charges, and financial values shown on this page are illustrative data. One canonical task is followed through the opening story.

Task receipt

See exactly how the cost accumulated.

ENG-2841 is the same accepted task shown in the hero. Its receipt preserves every provider, tool, retry, and acceptance event.

TaskENG-2841analytics-api
OutcomeAcceptedLinear issue closed
Failed spend$0.96One retry
Full task cost$8.35Recorded
RetryClaude Code · Failed validation+$0.96
Model callOpenRouter · Kimi K3+$1.48
Tool callClaude Code · Tests passed+$0.54
View all seven cost events
Claude CodeContext load · analytics-api$0.84
AnthropicModel call · Claude Sonnet 5$2.14
Claude CodeTool call · Repository search$0.67
OpenAIModel call · ChatGPT 5.6 Terra$1.72
Claude CodeRetry · Failed validation$0.96
OpenRouterModel call · Kimi K3$1.48
Claude CodeTool call · Tests passed$0.54

Illustrative task record · Outcome and financial state remain separate.

Model economics

Judge models by shipped work—not token price.

Repository migrationsSame workload · same acceptance rule · full task cost
ModelCost / acceptedAcceptanceFailed spend
Z.ai · GLM-5.2Recommended for this workload$24.7083%$590
OpenAI · ChatGPT 5.6 Terra$26.4081%$760
Moonshot AI · Kimi K3Open weights$28.9085%$710
OpenAI · ChatGPT 5.6 Luna$29.1086%$670
OpenAI · ChatGPT 5.6 Sol$31.8488%$620
Anthropic · Claude Sonnet 5$36.2082%$910
Google · Gemini 3 Pro$42.0776%$1,140

Portfolio comparisonNow compare the economics of equivalent accepted work across models.

Compare two matched tasksGLM‑5.2 costs 67% less
Matched-task exampleSame task · same acceptance rule · current provider list prices
Claude Opus 5Task owner: Jared · $5.00 input / $25.00 output per 1M
Cost
$1.58
Elapsed
8m 42s
Retries
0
Inspect task events
  1. Context loadRepository and issue context$0.42
  2. Model callPlan implementation$0.45
  3. Tool callSearch and edit repository$0.28
  4. Tool callRun test suite$0.24
  5. SuccessValidate accepted result$0.19
GLM-5.2Task owner: Sarah · $1.40 input / $4.40 output per 1M
Cost
$0.52
Elapsed
10m 18s
Retries
1
Inspect task events
  1. Context loadRepository and issue context$0.12
  2. Model callPlan implementation$0.12
  3. Tool callSearch and edit repository$0.08
  4. FailureInitial validation failed$0.06
  5. RetryRevise implementation$0.09
  6. SuccessTests passed and task accepted$0.05
GLM‑5.2 costs 67% less.One retry and 1m 36s longer, but substantially better completed-task economics.

Illustrative comparison. Provider list prices checked July 30, 2026; infrastructure and platform fees excluded.

Failure diagnosis

Find the failure. Fix the cause.

AgentWolf connects the prompt, model response, tool results, retries, and acceptance evidence so teams can distinguish model failure from unclear instructions and execution problems.

Second example · rejected taskENG-2917 shows how AgentWolf distinguishes prompt ambiguity from model and tool failures.

Inspect the diagnosed failurePrompt ambiguity · $4.82 failed spend
Claude Sonnet 5
invoice-sync · ENG-2917Task rejected
  1. Engineer

    Make invoice sync retry-safe when the provider times out.

  2. Claude Sonnet 5

    I’ll inspect the retry path and its tests before changing the implementation.

  3. Readsrc/workers/invoice-sync.ts184 lines
    SearchidempotencyKey0 matches
  5. Claude Sonnet 5

    The timeout path retries the same write. I’ll add exponential backoff and rerun the focused test.

  6. pnpm test invoice-sync
    FAIL retry after provider timeout
Expected: one invoice
Received: 409 duplicate_invoice
  8. Engineer

    The retry created a duplicate invoice. Rejecting this task.

Primary causeAgent misunderstood the retry requirement

The model treated a timeout as permission to repeat a non-idempotent write.

Contributing cause
Prompt lacked acceptance criteria
Evidence
409 duplicate_invoice

Illustrative diagnosis. AgentWolf presents correlated evidence for review; teams determine the final root cause and remediation.

ROI proof

Every assumption stays visible until the result.

Coding-agent value statementIllustrative period
Accepted tasks1,286
Workspace value assumption$92 / taskConfigured by your workspace
Gross accepted-task value$118,312
Agent-stack cost− $48,290Models, tools, retries, subscriptions
Net value$70,022
Review the ROI methodology

AgentWolf multiplies accepted tasks by a workspace-defined value, subtracts recorded agent-stack cost, and divides net value by that cost. Every assumption stays visible for review.

Close the books

Put every dollar somewhere—and explain what remains.

Allocate recorded spend first. Reconcile it with provider invoices second. Forecast only after the current period makes sense.

Provider reconciliationOne exception needs review
Recorded$42,870vsBilled$43,019Unexplained variance+$1490.35%
Largest exceptionAnthropic · +$171Recorded $21,440 · billed $21,611
Spend allocation$48,290 · 100% allocated
Platformagent-runtime · Inference controls38%$18,420
Productcustomer-app · Workspace launch29%$13,940
Dataanalytics-api · Usage ledger20%$9,810
Customer Opssupport-tools · Account AC-20413%$6,120
Month-end forecast$61,480

Evidence sources

Each system contributes a different part of the financial truth.

Inspect the evidence sourcesProviders · agents · work systems
Usage + invoicesProvider evidence
OpenAIAnthropicGoogleOpenRouter
Execution contextAgent and editor evidence
CursorClaude CodeOpenCodeVS Code
Acceptance eventsWork-system evidence
GitHubJiraLinear
AgentWolf ledgerOne accepted taskOne defensible cost

Agent Cost & Efficiency Audit

Cut agent waste. Help engineers ship more.

Try AgentWolf with your existing coding-agent stack. Review task costs, failure patterns, model economics, and workflow friction to identify where you can lower spend and help engineers get more accepted work from every dollar.

Audit Your Agent Spend
  • 01Connect your coding-agent stack
  • 02Review cost and efficiency analytics
  • 03Prioritize the changes with the highest return