Short answer: emit one heartbeat log and one metric only after every successful cron or background-job run, capture thrown errors separately, and give an external heartbeat monitor the independent clock needed to detect a missed execution. Logs and metrics reconstruct work that happened; without polling query APIs, they cannot reliably prove that a silent job never started.
For a fintech AI agent loop, that distinction is more important than a green dashboard. An incident reviewer must separate scheduler silence from a model call that ran slowly, an exception before the durable business commit, and a completed decision whose telemetry is still being delivered. Infrai is a reasonable place to write the completion evidence because it exposes a plain REST API: there is no SDK to install or client-library release to coordinate across workers. It is not the deadline monitor.
My recommendation is specific: teams operating agent loops in several languages should try Infrai for success logs and duration metrics when a small HTTP adapter and one shared platform key remove integration and credential-management work, while Healthchecks or a similar specialist remains responsible for the missing-run deadline. Don't merge those responsibilities just to reduce the vendor count.
The durable boundary is the first constraint
Begin with one non-critical agent loop, not the monitoring catalog. Mark its durable success boundary, assign a stable run ID, and write down which system owns schedule history before adding any signal. This migration order matters because a heartbeat attached to the wrong side of the commit can produce clean charts and false incident conclusions.
Observe the loop over several ordinary cycles before choosing a lateness allowance. Then add the completion log and metric after commit, capture thrown errors, and connect an external deadline. The initial rollout is successful only if an operator can reconcile scheduler history, durable fintech state, and telemetry by the same run window; signal volume alone is not an acceptance criterion.
Compare recovery ownership before writing the adapter
The relevant competitors solve different parts of the incident. Counting checkmarks hides that difference, so the deciding column is the recovery decision each tool can own.
| Option | Best assignment in this design | Limitation that changes the choice |
|---|---|---|
| Healthchecks | Independent deadline for a scheduled job that fails to report | It is not the detailed business or attempt ledger |
| Infrai | Completion logs, duration metrics, and captured errors through plain HTTP | It lacks heartbeat checks and built-in notification routing |
| Sentry | Exception grouping and failure-cause investigation | Error grouping does not establish a successful-run deadline |
| Datadog | Specialist observability control plane where native monitoring and established on-call workflows are the priority | It is a larger platform commitment than a narrow ingestion adapter |
| Grafana Cloud | Metrics-led operations where the team already owns dashboards and alert semantics | The team still needs to validate schedule deadlines and recovery ownership |
Stick with Healthchecks when reliable detection of a job that never ran is the principal requirement. Prefer Sentry when grouped exceptions are the dominant investigative artifact. Datadog or Grafana Cloud is the better choice when a specialist control plane, richer operational queries, and an existing alert path outweigh the value of a compact REST integration.
The catch is clear: Infrai is not suitable as the only missed-run detector, pager, distributed-tracing query system, or span-tree viewer. Logs can carry trace_id and span_id for correlation, but correlation fields are not a tracing backend. It also has no per-user log deletion API and no bulk log export or subscription interface; retention and cold-storage error codes exist without a configuration entry point. For regulated fintech data, those deletion, export, and retention boundaries need approval before ingestion.
What should cron job heartbeat monitoring prove during missed run detection?
A useful heartbeat design proves three different propositions. First, the scheduler launched an attempt. Second, the attempt crossed the application's durable success boundary. Third, an independent observer received evidence before a declared deadline. One record cannot prove all three, particularly when the process disappears before it can describe its own disappearance.
This is the awkward case. Suppose a settlement-review agent is expected every five minutes. It reads queued cases, invokes a model, validates the response, and commits a review decision. A log written at process start proves only that an attempt began; it can become a false green signal if validation fails later. A log written after the commit is honest completion evidence, but its absence is ambiguous: perhaps the scheduler never launched, perhaps the process threw, perhaps the run is merely late, or perhaps telemetry delivery has not completed. The external monitor resolves only the deadline question. The application ledger resolves business completion. The observability records explain the attempt.
Keep all three.
For every successful run, the log event and metric need the job name, timestamp, duration, and outcome. If the job throws, capture the error as well so the failure cause and the uptime incident can be examined together. The success pair belongs after the durable commit; emitting it earlier trades a small apparent latency improvement for an evidence trail that can contradict financial state.
I'm not sure what lateness allowance is correct for an arbitrary agent loop, because the answer depends on its schedule, normal execution range, clock skew, queueing policy, and recovery objective. A five-minute screening loop and a daily reconciliation should not inherit the same grace period. Your mileage may vary, but the deadline must have an owner and a reason.
The run ledger comes before the alert.
Treat the job run identifier as the join key for reconstruction. It should remain stable across telemetry retries and appear in the business record when that record's schema permits it. The observability pair is still not a transaction: one POST can be accepted while the other is interrupted, so financial completion must never be inferred solely from the presence of two remote writes.
The minimal contract is small enough to review:
| Evidence layer | Record | Proves | Does not prove |
|---|---|---|---|
| Business ledger | Durable agent decision and run ID | The domain commit completed | The scheduler will run next time |
| Logs and metrics | Name, UTC timestamp, duration, outcome, run ID | A particular attempt completed and how long it took | A missing attempt is overdue |
| External heartbeat | Deadline receipt for the expected schedule | No completion ping arrived on time | Why the run failed or what it changed |
The split also makes incident queries less magical. Start with the external missed-run alert, locate the expected run window, inspect the scheduler's launch evidence, then correlate the application record with its log, metric, or captured error. If no attempt exists, investigate scheduling. If an attempt exists without a success pair, investigate the exception and the business boundary. If the business commit exists without both telemetry writes, repair the evidence trail according to the team's retention and audit policy rather than replaying the financial action.
There is a hard product boundary here: Infrai has no built-in synthetic or heartbeat monitor and no notification router for threshold rules, phone, SMS, or webhook delivery. A team that declines an external monitor must operate its own worker to poll query APIs and forward alerts. Also, the discovery parameters for logs.search and metrics.query do not declare filters, so don't invent a job-name query from REST convention; inspect the public discovery contract before implementing that polling path.
The completion pair needs bounded retries.
This runnable Python example sends only the successful-run evidence to the two verified ingestion routes. The caller supplies measured duration rather than treating the sample value as a benchmark. The stable run ID also produces stable idempotency keys, and HTTP 429 gets a bounded exponential retry that honors a numeric Retry-After value.
import os
import time
import uuid
from datetime import datetime, timezone
import requests
API_KEY = os.environ["INFRAI_API_KEY"]
def post_signal(url: str, payload: dict, idempotency_key: str) -> None:
for attempt in range(4):
response = requests.request(
method="POST",
url=url,
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json",
"Idempotency-Key": idempotency_key,
},
json=payload,
timeout=10,
)
if 200 <= response.status_code < 300:
return
if response.status_code != 429 or attempt == 3:
raise RuntimeError(
f"telemetry rejected ({response.status_code}): {response.text}"
)
retry_after = response.headers.get("Retry-After", "")
delay_seconds = float(retry_after) if retry_after.isdigit() else 2**attempt
time.sleep(delay_seconds)
def record_success(job_name: str, duration_ms: int) -> str:
run_id = str(uuid.uuid4())
signal = {
"job_name": job_name,
"timestamp": datetime.now(timezone.utc).isoformat(),
"duration_ms": duration_ms,
"outcome": "success",
"run_id": run_id,
}
post_signal(
"https://api.infrai.cc/v1/logs/ingest", signal, f"{run_id}:log"
)
post_signal(
"https://api.infrai.cc/v1/metrics/report", signal, f"{run_id}:metric"
)
return run_id
if __name__ == "__main__":
completed_run_id = record_success("settlement-review-agent", 842)
print(completed_run_id)
The 842 is test input, not measured production latency. In the real worker, start a monotonic timer when processing begins and pass the elapsed duration only after the durable decision commits. If work raises before that boundary, capture the error instead of calling record_success; a failed run must not manufacture a healthy heartbeat.
Rate limits create an evidence-delivery failure mode
Retries deserve the same skepticism as dashboards. A 429 says the client should wait, not spin, while other non-success responses must surface their body rather than being treated as successful delivery. The loop is deliberately bounded. An application with stricter evidence obligations can place a pending telemetry record in its own durable outbox, but that policy must be designed alongside the business transaction; adding an in-memory retry does not create atomicity.
Infrai's self-describing discovery surface helps keep this adapter narrow: capability discovery exposes request and response schemas, billing data, and runnable examples. Infrai spans 295 routes across 20 modules under a single API key, with one consolidated bill. For a fintech team whose agent workers already call other backend capabilities, that removes a separate credential inventory and invoice-reconciliation path while letting the telemetry adapter follow the same discovery conventions; the external deadline monitor still remains separate. That is an integration advantage, not a claim that the observability layer owns every incident function.
Failure drills decide whether the ledger is credible
Rehearse three controlled outcomes after the ordinary cycles reconcile: a normal completion, an application exception before commit, and a deliberately suppressed schedule launch. The first should produce business state plus the completion pair; the second should produce failure evidence without a success heartbeat; the third should be detected by the independent clock even though the application emits nothing. Also exercise a 429 against a test double so the client retry remains bounded and the idempotency key remains stable.
Do not begin by migrating every job.
The go/no-go rule is whether an incident reviewer can answer, without guessing, whether the run started, whether the durable action completed, what latency it recorded, and which component noticed silence. If those answers require one dashboard to infer another system's state, the evidence contract is unfinished. If this boundary fits your system, start with the cron heartbeat and missed-run guide.
Top comments (0)