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This design lets a hosted application answer, with evidence, which agent, project, team, customer feature or environment consumed tokens, why a loop continued, what it cost under the applicable price version, and whether it should have been stopped.
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What a hosted app health dashboard should control
An agent loop is a sequence of model turns, tool calls, retries and possibly delegated subagent runs. A dashboard that records only the final response cannot explain spend or diagnose a runaway process. Build six controls into the API and runtime:
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- Stable attribution dimensions on every event.
- Complete usage capture, including cached and reasoning tokens when available.
- Trace and span correlation across your application, providers, tools and subagents.
- Loop health metrics that expose speed, failures, duration and quality.
- Budgets and anomaly controls that can pause or terminate execution.
- Provider-neutral export and reconciliation for audit and billing accuracy.
Implementing all six gives you both financial attribution and operational control. Omitting any one creates a blind spot: dimensions without usage cannot price a run, usage without traces cannot explain it, and dashboards without runtime limits cannot stop it.
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1. Put stable attribution dimensions on every event
Make attribution fields mandatory in the event schema, not optional dashboard tags. The same identifiers must travel from the hosted request through model providers, tools, retries and subagents.
| Field | What it identifies | Why it matters for cost |
|---|---|---|
run_id |
One end-to-end agent execution | Aggregates all turns, tools, retries and child runs into one billable unit. |
agent_id |
The agent or workflow definition | Shows which version or role consumes tokens. |
parent_run_id |
The caller of a subagent run | Prevents delegated work from disappearing from the parent’s cost. |
project, environment |
Application and deployment context | Separates production, staging and experiments. |
team |
Internal owner | Supports chargeback, budgets and alerts by team. |
customer_or_feature |
External tenant or product capability | Reveals unit economics and customer-level outliers. |
provider, model |
Model service and model identifier | Applies the correct token categories and rate card. |
price_version |
The pricing table revision used | Makes historical cost calculations reproducible after prices change. |
Generate run_id at the runtime boundary and preserve it for the entire loop. Generate a new child identifier for each subagent while setting its parent_run_id. Do not overwrite the parent’s project, team or customer context when a child is created; inherit those values unless a deliberate ownership transfer is recorded.
Use an immutable event envelope
Store the attribution envelope alongside each model, tool, retry and subagent event. A minimal record can look like this:
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2. Capture the complete usage record
Persist every usage field the provider returns. At minimum, retain input, output and total tokens. Also store cached-input and reasoning-token fields when present; providers do not expose identical categories on every model.
Separate raw usage from calculated cost
Write the provider’s raw response to durable storage before transforming it. Then normalize known fields into a common schema while retaining the original payload for audit. Include currency, the calculation timestamp, provider, model and price_version in every cost record.
| Normalized value | Recommended treatment |
|---|---|
| Input tokens | Charge using the input rate in the selected price version. |
| Output tokens | Charge using the output rate; keep separate from input for analysis. |
| Cached-input tokens | Record separately because cached rates can differ from uncached input. |
| Reasoning tokens | Preserve when exposed; do not infer them from total tokens. |
| Total tokens | Use as a consistency check against component fields. |
A practical calculation is the sum of each token category multiplied by the rate in the matching price-table revision. Never hard-code a single rate in application code. Providers change models, prices and accounting rules, and the same model family can have different rates by region or token category.
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Handle delayed or best-effort accounting
Usage reporting can be best-effort and may change as accounting arrives. Mark records as provisional when the provider has not finalized usage, then update them using an idempotent event key. Keep both the original estimate and the final value with timestamps so a finance user can explain a variance instead of seeing a silently rewritten number.
When a response contains no usage, record an explicit usage_status such as missing or pending. Do not substitute zero; zero implies that the call was free and will understate cost.
3. Correlate traces and spans across the whole loop
Attribution fields answer who and what; tracing answers where time and tokens went. Propagate one trace context through the hosted application, provider request, tool execution, retry and subagent boundaries.
Recommended span hierarchy
- Root span: the incoming hosted-app operation, carrying the
run_id. - Model span: prompt, response metadata, usage fields, latency and provider request ID.
- Tool span: tool name, arguments hash, result status and duration. Avoid storing secrets in arguments.
- Retry span: attempt number, trigger, backoff and outcome.
- Subagent span: child run ID and parent run ID, linked to the child’s own model and tool spans.
OpenAI’s agent observability model exposes turns, tool calls, subagents and recorded token usage. Its tracing guidance supports inspecting session traces and exporting them as OTLP JSON through a public API. Anthropic documents OpenTelemetry spans from application and API activity. These patterns allow a hosted dashboard to use a common trace view even when model providers differ.
Control cardinality and privacy
Use low-cardinality dimensions such as team, environment and model for dashboard grouping. Put high-cardinality identifiers such as customer IDs and run IDs in searchable attributes rather than metric labels. Redact prompts, tool arguments and customer content unless retention is explicitly required; store hashes or encrypted payload references when you need correlation without exposing sensitive data.
4. Measure loop health, not just total spend
A cost number without execution context cannot distinguish a valuable long task from a stuck loop. Track these metrics per run and aggregate them by agent, team, feature, customer and environment.
- Calls per run: model, tool and retry counts, with separate subagent totals.
- Loop duration: wall-clock time from root start to completion, timeout or termination.
- Latency percentiles: p50, p95 and p99 for model calls, tools and complete runs.
- Tool failures: count and failure rate by tool and error class.
- Provider errors: authentication, rate-limit, timeout and server-error categories.
- Retry count: attempts per operation and cumulative backoff time.
- Token velocity: tokens generated per second, calculated over the generation interval.
- Quality or feedback: evaluator scores, user corrections, acceptance and escalation signals.
Token velocity is useful for detecting a loop that is producing large responses rapidly, while calls per run identifies repeated planning or tool cycles. A high duration with low token velocity may indicate a blocked tool; high velocity with rising error rates may indicate an uncontrolled retry storm.
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Define completion and failure states
Every root run should end in one of a small set of states: completed, failed, timed out, cancelled, budget-paused or budget-terminated. Require an end reason and timestamp. This prevents “active” runs from remaining in dashboards after a worker has died and makes daily spend totals reconcile with runtime records.
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5. Enforce budgets and stop runaway loops at runtime
A dashboard alert that arrives after completion is not a control. Enforce limits in the loop supervisor or API gateway, where another model or tool call can still be prevented.
Use layered limits
| Limit | Example policy | Action when exceeded |
|---|---|---|
| Per-call | Maximum output tokens or tool timeout | Constrain the call or return a controlled failure. |
| Per-run | Token, cost, call-count and wall-clock ceilings | Pause for review or terminate the run. |
| Per-team | Daily or monthly budget | Throttle new runs and notify the owner. |
| Per-customer or feature | Tenant quota or unit-cost threshold | Degrade gracefully, queue, or require approval. |
Compute an incremental estimate before each model call: prior committed cost plus the maximum expected cost of the next operation. If the estimate crosses the run budget, do not start the call. Keep a small emergency reserve for finalization or error handling, and make the reserve visible in the policy.
Detect anomalies by rate as well as amount
Alert on sudden changes in calls per minute, token velocity, retries, error rate and cost per completed task. Datadog’s Agent Observability documentation says it automatically estimates cost for each LLM request from public provider pricing and token counts annotated on LLM and embedding spans, and supports monitors grouped by team, customer tier, feature or environment. Such grouping is useful for finding a single tenant or deployment that is causing a fleet-wide increase.
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Estimated cost is not the provider invoice. Label it as estimated until reconciliation, and include the price version and timestamp used for the estimate. The dashboard should show both the alert threshold and the policy action, such as pause, terminate, downgrade or require human approval.
Make termination safe
Cancellation must propagate to queued model calls, tool workers and child agents. Record a termination span, release leases, stop retries and mark the root run with the exact limit that fired. If a provider cannot cancel an in-flight request, prevent subsequent calls and account for any late usage when it arrives.
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6. Export normalized data and reconcile billing
Keep your dashboard convenient, but do not make it the only copy of financial evidence. Export normalized traces, spans, usage and cost records to a warehouse or OpenTelemetry-compatible backend. Retain the raw provider response and the normalized version together.
Reconcile on a regular cadence
- Aggregate estimated cost by provider, model, price version, team and billing period.
- Retrieve provider usage or cost data for the same period. Anthropic’s Usage/Cost API requires organization-level credentials.
- Match records using provider request IDs, time windows and model identifiers.
- Classify differences as delayed accounting, missing usage, price-table drift, rounding or unallocated calls.
- Publish a reconciliation status and owner for every unresolved difference.
OpenAI documents dashboard usage views and public trace export, while Anthropic documents usage and cost retrieval plus OpenTelemetry export. A provider-neutral schema lets finance compare these sources without forcing every team to learn a different event format.
Retention and access controls
Set retention by data class: raw prompts may need a shorter period than aggregated cost records. Restrict customer-level views to authorized staff, separate operational debugging from finance access, and log every export. Residency requirements can affect where traces and raw responses are stored, so select a warehouse and telemetry region that matches your contracts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing an implementation pattern
Evaluate an implementation against the same operational questions rather than choosing by dashboard appearance.
| Approach | Strength | Questions to verify |
|---|---|---|
| Provider-native agent tooling | Deep visibility into that provider’s turns, tool calls, subagents and usage fields. | Can it ingest other providers, enforce your own budgets and export complete traces? |
| Multi-SDK tracing platform | Common instrumentation and evaluation views across application code. | Which dimensions, retention periods and warehouse exports are available? |
| Provider-normalized observability platform | Estimated cost and monitors grouped by business dimensions; Datadog documents coverage of more than 800 models as of its documentation page accessed October 20, 2026. | How are prices updated, and can you override or version the rate table? |
| OpenTelemetry-first stack | Portable spans and control over storage and retention. | Do your collectors preserve GenAI usage fields, provider IDs and late-arriving updates? |
Compare attribution granularity, provider coverage, trace export format, enforcement location, latency overhead, retention and residency, dashboard dimensions, billing reconciliation and access controls. A tool can be excellent at tracing yet unsuitable for hard budget enforcement if it runs outside the request path.
Implementation sequence for a hosted application
- Define the event contract. Make the attribution envelope and usage status required fields. Reject events that lack a run ID, provider, model or price version.
- Instrument the root runtime. Create the trace and run ID before the first model call; propagate context through asynchronous jobs.
- Wrap every provider and tool client. Emit start, completion, error and retry events, including provider request IDs and durations.
- Add an idempotent usage writer. Accept late updates and preserve raw responses; never count a retry as an overwrite.
- Build the cost calculator. Use versioned rates, explicit currency and calculation timestamps. Flag unknown models instead of assigning a guessed price.
- Expose health views. Provide run drill-down, team and feature rollups, latency percentiles, token velocity, failures and feedback.
- Place the budget gate before execution. Check projected cost and call count before each model or tool operation.
- Test failure branches. Simulate provider timeouts, missing usage, duplicate events, child-agent cancellation and price changes.
- Reconcile and review. Compare estimates with provider billing, investigate unallocated spend and revise alert thresholds from observed workload.
Common failure modes and fixes
Subagent spend is missing
The parent recorded only its own model calls. Add a child run with parent_run_id, inherit ownership dimensions and aggregate child usage into the parent view while retaining a separate child detail.
Costs change after the fact
Usage arrived late or the rate table was edited. Keep provisional and final usage states, immutable price versions and calculation timestamps.
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Retries look like productive work
Retries were not separate events. Record attempt number, trigger and backoff, then chart retry tokens independently from successful-call tokens.
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Enforcement exists only in analytics. Move the budget check into the runtime supervisor or gateway and propagate cancellation to tools and child agents.
Quality falls as cost falls
Only token totals were optimized. Add completion quality, user feedback and evaluator scores to the same run and compare cost per accepted outcome.
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Check delayed accounting, cached-input treatment, rounding, model aliases, currency and calls made outside the instrumented path. Keep an explicit unallocated category until each difference is explained.
What success looks like
An operator should be able to open any run and see its complete parent-child trace, every model and tool attempt, token categories, the price version used, estimated and reconciled cost, latency, failures, retries, quality signals and the budget decision. A team lead should be able to roll that data up by project, environment, customer or feature without losing the underlying evidence. Finally, the runtime should be able to pause or terminate a loop before another expensive operation begins.
There is no authoritative cross-platform average agent-loop cost or universal savings percentage for these controls. The practical goal is stronger: every unit of spend is attributable, every estimate is qualified, and every runaway pattern has a defined detection and recovery path.
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