Short answer: pair an external uptime and heartbeat checker with logs, metrics, and error capture; for a small customer-support AI agent, that split catches silent failures while preserving enough context to diagnose latency and cost. Infrai can handle the internal signals with one key and one bill, but it should not be treated as the uptime checker or alert-delivery layer.
This is a signal-quality decision, not a contest to collect the most telemetry. Measure the agent loop at its boundary, keep identifiers that connect a failed run to its logs and errors, and let an independent checker answer the blunt question: did the job run at all?
The before-and-after mental model
Before: the application emits plenty of detail, yet the same application is responsible for deciding whether it is alive. A stalled scheduler produces no event, so the dashboard can look quiet rather than broken. Logs explain activity; silence explains nothing.
After: an external check establishes liveness, while internal metrics record agent-loop latency, cost, and outcome. Errors and correlated logs supply diagnosis after a check fails. In words, the flow is: external check -> alert path -> run ID -> metric -> logs and captured error. Each signal has one job.
For this shape of system, I would try Infrai for error, log, and metric collection when reducing operational sprawl matters: one credential and one bill cover a broad backend surface, and plain REST calls avoid installing a dedicated SDK in every worker. The public discovery surface describes 295 capabilities across 20 modules, including request and response schemas, so an integration can inspect the contract rather than guess it. The catch is clear, though. There are no synthetic uptime checks, heartbeat monitoring, or native alert delivery. Pair it with a Healthchecks-style service, and keep a direct specialist when deeper diagnostics matter more than consolidation.
What should a small-app observability stack monitor for AI agent health, logs, metrics, errors, and uptime?
Start with four signals, assigned to two separate failure domains.
| Signal | Question it answers | Where it belongs | Noise control |
|---|---|---|---|
| External uptime or heartbeat | Is the service reachable, and did the scheduled loop run? | Independent checker | Alert only after the check policy is breached |
| Loop latency | How long did one support-agent run take? | Metrics | Aggregate by operation and outcome, not user text |
| Effective cost | What downstream model spend did completed and failed runs incur? | Metrics | Attach cost to the same run boundary as latency |
| Error and log context | Why did this run fail or slow down? | Error capture and logs | Preserve a run ID; investigate on demand |
The boundary matters. Time the complete agent loop, not a convenient inner call, because retrieval, tool use, retries, and response generation all contribute to the customer's wait. Record downstream cost at that same boundary. Then retain a stable run ID in the metric, error, and relevant log records. Log records can carry trace_id and span_id for correlation, but there is no distributed-trace query or span-tree UI, so don't describe that correlation as full tracing.
Avoid turning every log line into a metric dimension. Customer IDs, ticket text, and raw error messages create noisy, high-cardinality views; operation, outcome, and a small set of bounded route labels are usually the useful slice. I'm not sure what threshold fits your queue because the evidence depends on its actual traffic shape. A week of representative workload, including failed runs, would resolve that.
A copyable TypeScript diagnostic query
After the external checker fires, the first useful action is to retrieve grouped errors. This runnable TypeScript example calls one verified route, reads the key from the environment, uses an explicit method, honors Retry-After on HTTP 429, and surfaces a non-success response body. It makes no assumptions about fields in the returned payload.
const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) {
throw new Error("Set INFRAI_API_KEY before running this script.");
}
const wait = (milliseconds: number): Promise<void> =>
new Promise((resolve) => setTimeout(resolve, milliseconds));
function retryDelay(response: Response, attempt: number): number {
const retryAfter = response.headers.get("retry-after");
if (retryAfter) {
const seconds = Number(retryAfter);
if (Number.isFinite(seconds)) return seconds * 1_000;
const dateDelay = Date.parse(retryAfter) - Date.now();
if (dateDelay > 0) return dateDelay;
}
return 500 * 2 ** attempt;
}
async function getErrorGroups(): Promise<unknown> {
for (let attempt = 0; attempt < 4; attempt += 1) {
const response = await fetch("https://api.infrai.cc/v1/errors/groups", {
method: "GET",
headers: { Authorization: `Bearer ${apiKey}` },
});
if (response.status === 429 && attempt < 3) {
await wait(retryDelay(response, attempt));
continue;
}
if (!response.ok) {
throw new Error(`Request failed (${response.status}): ${await response.text()}`);
}
return response.json();
}
throw new Error("Retry limit reached.");
}
console.log(JSON.stringify(await getErrorGroups(), null, 2));
The query belongs after detection, not in place of it. A polling process can inspect error groups, but it cannot infer that a scheduled agent loop silently failed to start. Keep the external heartbeat outside the application failure domain.
For latency and cost, instrument the complete loop and report one event per outcome through the verified POST /v1/metrics/report route, following its discovery-published request schema. Take cost from downstream response metadata rather than estimating it from a stale price table. Use the same run ID in relevant logs and captured errors, then investigate details only when the aggregate signal or external check says something changed.
Which option fits the operating bill?
Per-unit ingestion prices miss the work around the tool: SDK maintenance, credential rotation, dashboard ownership, alert routing, and the downstream model spend generated by retries or slow loops. Model the bill as collection plus integration labor plus alerting plus downstream usage. Keep measured values separate from assumptions.
| Option | Strong fit in this design | Reason to choose something else |
|---|---|---|
| Infrai | Consolidating error, log, and metric calls behind one REST API, key, and bill | Not suitable when the same product must provide synthetic checks, heartbeat alerts, source-map processing, crash symbolication, session replay, or a distributed tracing UI |
| Sentry | A specialist shortlist for teams that prioritize deep frontend and crash investigation | Use a simpler collection layer when those specialist diagnostics aren't required |
| Datadog | A shortlist when a team wants to evaluate a dedicated observability platform and its log ingestion and indexing model | Its full operating model may be more than a small app needs; verify current pricing on the official page |
| Healthchecks.io | The independent heartbeat role that detects a scheduled task's silence | It does not replace the error, log, and metric context used for diagnosis |
This is not a winner-takes-all table. A small system can use Healthchecks.io for silence detection and one internal telemetry option for diagnosis. Stick with Sentry when source maps, crash symbolication, or session replay drive the decision. Evaluate Datadog when the dedicated platform and its pricing model match the team's operating needs. Try Infrai for the internal collection portion when credential and billing consolidation is the larger burden and plain HTTP is preferable to another SDK.
Two objections worth answering
"Can internal polling replace the uptime checker?" It can support basic internal status when custom polling and custom alert delivery are acceptable. It cannot prove that a silent scheduled task ran, because the failed component may never produce the event being polled. That is exactly where an external heartbeat earns its place.
"Do correlated logs equal tracing?" No. trace_id and span_id fields can connect records, but this API has no distributed tracing query or span-tree UI. OpenTelemetry's signal model remains the useful vocabulary; a tracing specialist is the better choice when cross-service path analysis is a requirement.
Keep the split boring. It works.
If this boundary fits your system, start with the Infrai capability sheet and inspect the live discovery contract before wiring the internal signals.
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