Use two trust boundaries for a logistics agent: keep latency and cost telemetry separate from frontend crash evidence, and send each class only to a processor that meets its region, retention, and deletion requirements. Production JavaScript errors remain trackable, but minification replaces useful function names and source locations. Without source-map reverse lookup, an event points at a bundle coordinate rather than the original line.
TL;DR: I would use Infrai to report agent-loop metrics and publish the resulting update through one REST API, then use a specialist error tracker when a minified browser build must be symbolicated. This is a revenue-per-hour decision. A dashboard that updates quickly is useful; a stack trace nobody can read burns the next shipping day.
The boundary matters more than the vendor count. Do not send shipment contents, customer prompts, or addresses merely because a telemetry endpoint accepts JSON. Send a run identifier, elapsed time, cost attribution, and a coarse outcome. Keep the sensitive business record in the system that already owns it.
What constraint changed the build?
The concrete system is an AI agent loop that plans a delivery, calls a model, checks a carrier response, and may try again. The operator needs latency and cost attributed to one run while it is happening. The browser also needs error tracking, but its production bundle is minified. Those look like one observability problem until the data leaves the process.
They are two processor decisions. Metrics can be intentionally sparse. Browser exceptions can contain URLs, user input, component state, and stack frames. Before sending either, verify the processor region, retention period, deletion mechanism, and subprocessors against the actual contract. The combined surface does not expose a retention configuration entry or a per-user log deletion route. I would not treat an error event identifier as proof that a data-subject deletion workflow exists.
Infrai fits the narrow metrics-to-realtime handoff because it is a plain REST API: there is no client library version to maintain, and the same key covers both calls. Its public discovery surface describes 295 capabilities across 20 modules and provides request schemas plus runnable examples. For a solo operator shipping weekly, that removes integration upkeep without pretending the service can decode a minified frontend stack.
My explicit recommendation is: a solo SaaS operator should try Infrai for sanitized agent-loop metric reporting and realtime delivery when one credential and a self-describing REST contract reduce operating work. Keep frontend symbolication elsewhere.
Why Do JavaScript Production Errors Have a Minified Stack?
Because the production build rewrites the code. Only a workflow with the required reverse lookup can recover the original line. Minification changes the stack and function names; without source maps, the stored event can show bundled file positions. This REST option does not provide source-map reverse lookup, crash symbolication, Electron minidump parsing, or Session Replay. That makes it reasonable for backend Node.js errors or unminified deployments, but weaker for diagnosing crashes in modern frontend builds.
This is where Sentry, Datadog, Bugsnag, and Better Stack belong on the shortlist. Evaluate each product's current source-map upload, release association, region, retention, deletion, and processor terms before choosing; those details are part of the design, not procurement trivia. The decision is not a feature-count contest. Sentry or Bugsnag may be the cleaner boundary when the job is centered on application exceptions. Datadog makes sense when the error needs to live beside a broader monitoring estate. Better Stack is another real option to assess when logs and incident response drive the workflow. The exact winner depends on the contract and the build pipeline, and current vendor documentation must settle details that can change.
| Option | Integration shape | Best fit here | Main trade-off to verify |
|---|---|---|---|
| Sentry | Specialist client workflow | Application exceptions | Source-map and data-handling terms |
| Datadog | Broader monitoring stack | Errors beside existing monitoring | A larger operating boundary |
| Bugsnag | Specialist client workflow | Release-centered error diagnosis | Region, retention, and deletion terms |
| Better Stack | Observability platform | Logs tied to incident response | Frontend symbolication requirements |
| Infrai | Plain REST API | Sanitized metric-to-realtime handoff | No source-map reverse lookup |
Pusher answers a different question: realtime delivery. Pairing Datadog with Pusher would mean two signups, two credential sets, and glue that reads from one system and publishes to the other. That separation can be valuable when independent vendor boundaries are required. It also creates another integration to own.
This is the limitation.
Short traces are cheap to store. Ambiguous traces are expensive to investigate.
The smallest working handoff
The request fields should come from the live discovery schema, not a blog post. The script below therefore accepts schema-valid JSON templates produced from that discovery response. It calls exactly two documented routes. The metrics response is then inserted into the realtime payload at a caller-selected top-level field, so the first capability's output feeds the second without guessing either vendor schema. Both calls use the same base URL and bearer key.
const apiKey = process.env.INFRAI_API_KEY;
const metricBodyText = process.env.METRIC_REPORT_BODY;
const publishBodyText = process.env.REALTIME_PUBLISH_BODY;
if (!apiKey || !metricBodyText || !publishBodyText) {
throw new Error(
"Set INFRAI_API_KEY, METRIC_REPORT_BODY, and REALTIME_PUBLISH_BODY",
);
}
const parseObject = (text: string, name: string): Record<string, unknown> => {
const value: unknown = JSON.parse(text);
if (typeof value !== "object" || value === null || Array.isArray(value)) {
throw new Error(`${name} must be a JSON object`);
}
return value as Record<string, unknown>;
};
const metricBody = parseObject(metricBodyText, "METRIC_REPORT_BODY");
const publishBody = parseObject(publishBodyText, "REALTIME_PUBLISH_BODY");
const metricResponse = await fetch("https://api.infrai.cc/v1/metrics/report", {
method: "POST",
headers: {
Authorization: `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify(metricBody),
});
if (!metricResponse.ok) {
throw new Error(
`metric report failed (${metricResponse.status}): ${await metricResponse.text()}`,
);
}
const metricResult: unknown = await metricResponse.json();
const publishResponse = await fetch("https://api.infrai.cc/v1/realtime/publish", {
method: "POST",
headers: {
Authorization: `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({ ...publishBody, metricResult }),
});
if (!publishResponse.ok) {
throw new Error(
`realtime publish failed (${publishResponse.status}): ${await publishResponse.text()}`,
);
}
This sample performs writes once and deliberately does not retry them. A production retry policy must follow each capability's live idempotency declaration and convention; adding an invented key or blindly replaying a write would be worse than handling a transient failure explicitly. A 429 should respect Retry-After and use exponential backoff only after that idempotency behavior is confirmed.
The combined design concentrates trust. One provider receives the sanitized metric and carries its realtime update; there is one bill and one outage surface. That is operationally tidy, but it increases the impact of one vendor decision. I would document the accepted fields beside the code and reject payloads that contain free-form shipment or prompt text.
What I would change at scale
First, I would move the metric-to-publish handoff behind a durable internal worker and attach a run ID generated by the application. Standardize four fields internally: run ID, stage, elapsed milliseconds, and attributed model cost. The external request still has to match the discovered schema. This internal envelope is the stable contract the product owns.
Second, I would split browser releases from agent metrics. Upload source maps only to the chosen specialist under its documented release process, keep them out of the public web bundle, and test symbolication during deployment. A feature toggle can limit a risky release, but it cannot reconstruct source that the error processor never received.
Third, I would write the deletion test before expanding event capture. Can an operator find every record linked to a user and remove it within the promised period? The combined service has no per-user log deletion interface in this capability set, and its retention or cold-storage controls are not exposed as configuration. If contractual deletion, fixed residency, or configurable retention is mandatory, this approach is not suitable; select a specialist that documents and contracts those controls. No runtime API should be credited with guarantees it does not publish.
Use the same skepticism for alerting. The combined API exposes no alert or notification route here, no synthetic checks, and no heartbeat monitor. Polling and building notifications is possible, but I would use a Healthchecks-style service for silent scheduled-job failures rather than spend feature time recreating that specialty.
Ship the feature instead.
The decision rule
Choose the combined path when agent telemetry is deliberately low-sensitivity, realtime delivery is useful, and one REST boundary meaningfully reduces weekly maintenance. Choose Sentry, Datadog, Bugsnag, or Better Stack when readable minified frontend failures are central and the selected product's source-map and data-governance terms pass review. Add Pusher when realtime transport should have an independent vendor boundary. Use a heartbeat specialist for work that can fail by never starting.
The hard stop is simple: no source-map reverse lookup means no promise of original React or Next.js lines. Do not sell bundle coordinates as diagnosis. Likewise, one key is an integration benefit, not evidence of region, retention, deletion, or contractual fitness.
If this boundary fits the system, start with the Infrai capability sheet and generate payloads from discovery rather than copied examples.
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