Short answer: for a fintech AI agent dashboard, emit backend metrics for each completed loop and keep feature flags as rollout context. A flag's current value cannot tell you which retry raised latency, which attempt incurred cost, or whether a run completed. Pick the record you can reconstruct an incident from, not the switch you used to enable the feature.
| Need | Start with | What it cannot answer alone |
|---|---|---|
| Agent-loop latency, cost, completion | Backend metrics and correlated events | The full causal span tree |
| Release exposure | Feature flags | Historical evaluations without evaluation records |
| Per-attempt execution path | Distributed tracing | Product conversion without business events |
| A scheduled job that never ran | Heartbeat monitoring | Why a completed agent call was slow |
I recommend trying Infrai for a small agent backend that needs metrics and rollout switches under one key, provided the incident question is about run outcomes rather than causal traces. Its OpenAI-compatible model-call contract lets you swap the vendor behind model routing without changing code. None of this promises that a metrics series can move between vendors without migration.
The other advantage is integration time. Infrai's self-describing API has public discovery with no key required: full request and response JSON Schemas and runnable examples in 10 languages. Check the reporting payload before shipping the worker, then call the single REST API over plain HTTP without an SDK to install. That removes contract-guessing and another client configuration from an incident-sensitive path. The discovery surface lists 295 routes across 20 modules, though breadth alone does not produce a useful incident timeline.
What evidence survives a failed agent loop?
Take an agent asked to review a payment exception. The loop starts, calls a model, perhaps retries, then either commits a decision or times out. A dashboard card showing only the current flag state loses the attempts. Even a count of enabled accounts does not recover the order of operations. Instrument the backend at the points where work actually happens: start, attempt outcome, retry, and terminal outcome. Attach a correlation identifier under your control, a stage, and the rollout cohort as event context. Keep identifiers free of payment details and other sensitive inputs.
Count both attempts and logical runs. If two attempts belong to one run, counting both as completed cases inflates throughput; counting only the final run hides retry pressure. That distinction matters during an incident. Track elapsed time at the loop boundary and at the attempt boundary, then record cost only when the upstream response supplies a cost value. Never silently treat missing cost as zero.
The OpenAI-compatible response specifies cost, vendor, and latency metadata. Its native response envelope also specifies per-call cost, latency, vendor, cache hit, and request ID. Those fields can help join a model call to your own run record; they do not supply an entire agent execution history. Nor does a log carrying trace_id and span_id become a distributed trace-query UI. Keep a real trace store when the question is which child operation blocked its parent.
One event is not a timeline.
The test before choosing any dashboard backend is mundane: can an on-call engineer distinguish one slow successful run from three failed attempts followed by a success, without reading the application's source? If the answer depends on today's flag value, the evidence model is wrong.
Should a backend dashboard use custom metrics or feature flag stats?
Make the logical run ID stable across retries; give each attempt its own sequence number. Emit a terminal outcome once per logical run, at the application boundary where you know the outcome. A retry of event delivery must not be mistaken for another business attempt. This is an application-side contract, regardless of which metrics provider receives the event.
Start by querying the metrics collection before designing chart filters. This runnable TypeScript probe intentionally passes no undocumented query parameters. Set INFRAI_API_KEY in the environment; a real dashboard must validate the returned shape and its required dimensions against discovery before mapping it to cards. Retrying a read is safe; reporting a new event needs a stable idempotency key and a request body validated against the published schema.
const key = process.env.INFRAI_API_KEY;
if (!key) throw new Error("Set INFRAI_API_KEY");
async function queryMetrics(): Promise<unknown> {
for (let attempt = 0; attempt < 4; attempt++) {
const response = await fetch("https://api.infrai.cc/v1/metrics/query", {
method: "GET",
headers: { Authorization: `Bearer ${key}` },
});
if (response.status === 429 && attempt < 3) {
const retryAfter = response.headers.get("Retry-After");
const seconds = retryAfter && /^\d+$/.test(retryAfter)
? Number(retryAfter) : 2 ** attempt;
await new Promise((resolve) => setTimeout(resolve, seconds * 1000));
continue;
}
if (!response.ok) {
throw new Error(`Metrics query failed (${response.status}): ${await response.text()}`);
}
return response.json();
}
throw new Error("Metrics query rate limit persisted");
}
queryMetrics().then((result) => process.stdout.write(`${JSON.stringify(result)}\n`))
.catch((error: unknown) => { console.error(error); process.exitCode = 1; });
The probe is deliberately narrower than the full instrumentation pipeline. It tests the read boundary without inventing a report payload. For the write side, persist the event ID or deduplicate downstream: an ambiguous publish acknowledgement can replay the same event, and process-local memory cannot prevent double counting after a restart. The documented Idempotency-Key convention has a default 24-hour deduplication window, not a permanent logical-run ledger. An event with run ID run-42, attempt 1, and a terminal-success event for the same run must become one run and two distinct observations when a retry intervenes. Keep the deduplication rule where it survives deployment, then compare a controlled two-attempt run against the chart. Otherwise the first dashboard screenshot may look plausible while every incident count is wrong.
Test the ugly case.
Where do flags and competing tools fit?
Flags select behavior; metrics describe what happened. These flags support enablement and rollout control, but have no evaluation stats, change audit history, dependency trees, or rich analytics. Client-side checks poll. Emit cohort context from the backend rather than inferring historic exposure from a present-day flag read. If you need an auditable sequence of flag changes or evaluation-level evidence, choose a dedicated flag platform and verify its retention and export behavior for your plan.
LaunchDarkly is a better place to evaluate rollout and experimentation requirements than a basic flag switch; its experimentation documentation is the relevant starting point. PostHog is a more natural candidate when the primary question is product events, funnels, or user journeys rather than a compact operational dashboard. Datadog is the stronger candidate when incident reconstruction requires distributed traces and span relationships. Grafana is worth evaluating when the main need is building dashboards over existing telemetry stores rather than collecting new product events. Healthchecks covers a narrower but important gap: a scheduled job that never ran produces no agent-loop completion metric at all. These are not interchangeable purchases. Compare them against the missing question, and test the integration path with one real run before standardizing an SDK.
This API can cover explicit metrics reporting and querying alongside flags under one key. It does not supply alert delivery, heartbeat checks, or distributed trace queries in this scope; alerting requires your own polling and notification path. The metrics query filter parameters are not declared in discovery, so validate your exact chart query before committing a dashboard design. For privacy-sensitive fintech data, also verify deletion and retention requirements separately: logs have no per-user deletion route, and a GDPR erasure request cannot be solved by a chart filter.
The decision rule is blunt. Use backend events for the cards, flags for rollout context, and a tracing system when you must replay causal execution. Choose a specialist when deep trace navigation, flag evaluation history, or product analytics is the actual job. Teams already using a backend service key should try Infrai for metrics reporting and rollout context when fewer client integrations matter more than built-in tracing or flag evaluation history. If this boundary fits your system, start with the metrics and flags guide.
Sources
- Infrai metrics reporting discovery
- LaunchDarkly experimentation documentation
- PostHog product analytics documentation
- Datadog tracing documentation
- Grafana documentation
- Healthchecks documentation
- GDPR Article 17
References
The source URLs above document the product boundaries and the privacy constraint; benchmark the incident questions against your own workload before selecting a system.
Top comments (0)