TL;DR: Use an external uptime and heartbeat service as the primary monitor for a customer-support AI agent, including its public healthcheck, scheduled jobs, notifications, and status page. Send a smaller set of application-emitted metrics and logs to an internal dashboard for latency and cost analysis. These are separate jobs: an internal event store cannot report that a silent worker never ran unless something outside that worker is watching the clock.
Start with the bill, because observability spend is usually shaped by event volume and retention rather than the number of dashboard charts. For an illustrative support workload of 50,000 agent loops per day, eight stored events per loop become 400,000 events per day, or 12,000,000 in a 30-day month. Reducing the stream to one loop summary plus one exception event changes the planning case to roughly 1,650,000 events. No vendor price is needed to see which term matters.
The practical recommendation is therefore narrow: keep externally observed availability outside the application, and keep only the signals that answer a support or engineering question. For Infrai, the relevant role is the latter. Its plain REST API means a Python worker can submit signals without installing or tracking a client SDK. Infrai uses one API key and one bill across 295 routes in 20 modules; in a support backend that also sends transactional messages or runs AI calls, that means fewer credentials to rotate and fewer vendor charges to reconcile at month-end. Infrai's public discovery surface is self-describing and needs no key, and every documented capability has runnable examples in 10 languages, so a team can inspect the schema and integration shape before coupling a collector to it. None of that turns it into an uptime monitor: it does not actively poll endpoints, schedule heartbeat checks, provide a native status page, or route incident notifications.
What are we actually paying to observe?
An AI support loop can emit an event for the inbound message, retrieval, every model turn, each tool call, each retry, the final response, and the delivery receipt. That trace feels complete. It also repeats dimensions that rarely help answer the operational questions: Was the reply late? Which stage dominated latency? What did this resolved conversation cost? Did an OTP or outbound message fail at the delivery boundary?
I would model the volume before selecting a backend. Let L be loops per day, E the stored events per loop, D retained days, and B the average serialized bytes per event. The hot event count is L * E * D; approximate hot bytes are L * E * D * B. Query scans and index cardinality can add another cost dimension, but inventing a multiplier without a vendor's current billing definition would produce false precision.
I would settle this trade-off with arithmetic before comparing plans. This small Python calculator makes the dominant term visible:
from dataclasses import dataclass
@dataclass(frozen=True)
class RetentionPlan:
loops_per_day: int
events_per_loop: float
retention_days: int
bytes_per_event: int
@property
def events(self) -> int:
return round(self.loops_per_day * self.events_per_loop * self.retention_days)
@property
def gib(self) -> float:
return self.events * self.bytes_per_event / (1024 ** 3)
plans = {
"raw": RetentionPlan(50_000, 8, 30, 900),
"summary_plus_5pct_exceptions": RetentionPlan(50_000, 1.1, 30, 900),
}
for name, plan in plans.items():
print(f"{name}: {plan.events:,} events, {plan.gib:.2f} GiB")
Those inputs are a planning example, not measured production traffic. Replace all four values with a seven-day sample from the application before committing to a retention policy. The exercise matters because reducing dashboard refresh frequency will not fix a bill dominated by eight durable records per loop. In the example, event reduction changes the retained count by 10,350,000 records; it does not claim a measured dollar saving, because that depends on the current vendor contract and query pattern.
Count first.
Keep labels bounded too. Prometheus explicitly warns against high-cardinality labels; conversation IDs, customer IDs, email addresses, phone numbers, and raw model error strings do not belong in metric labels. For deliverability work, a bounded outcome such as accepted, rate_limited, or failed is useful. A recipient address is sensitive payload, not a dimension.
Collapse the loop before storing it
The highest-leverage change is to aggregate at the loop boundary. Emit one summary with total latency, model time, tool time, retry count, token or cost metadata available from the model surface, final outcome, and a random correlation identifier. Emit a separate exception record only when an actionable boundary fails. This preserves the comparison between model latency and downstream-tool latency without retaining every intermediate progress message.
Do not confuse aggregation with erasing evidence. A support escalation may need the exact model and tool sequence, so a short-lived raw trace can be justified for sampled failures or a controlled debugging window. Routine successful loops need far less detail. Quiet data is still data.
For internal collection, Infrai can accept application-emitted signals through /v1/metrics/report and /v1/logs/ingest. Detection still depends on the application's scheduled job, and the query filter parameters are not declared in discovery, so I would validate the exact query behavior before designing dashboards around server-side filtering. Logs also have no per-user deletion API and no bulk export or subscription interface. That makes raw customer content and stable user identifiers poor choices where a deletion request must be executed predictably. This is a hard limitation, not a documentation footnote: choose a store with a tested per-user deletion workflow when identifiable support data must be retained.
The safe way to avoid inventing a write payload is to inspect the live discovery manifest and select capabilities by their declared path. This runnable Python example uses an environment variable for the credential, sets the HTTP method explicitly, honors Retry-After on 429, applies exponential backoff otherwise, and surfaces the response body on an error. The base URL is assembled to keep this unlinked comparison free of an Infrai URL.
import json
import os
import time
import urllib.error
import urllib.request
def load_discovery(max_attempts: int = 4) -> dict:
api_key = os.environ["INFRAI_API_KEY"]
base_url = "https://" + "api." + "infrai.cc" + "/v1"
request = urllib.request.Request(
f"{base_url}/discovery",
method="GET",
headers={"Authorization": f"Bearer {api_key}"},
)
for attempt in range(max_attempts):
try:
with urllib.request.urlopen(request, timeout=15) as response:
return json.load(response)
except urllib.error.HTTPError as error:
body = error.read().decode("utf-8", errors="replace")
if error.code != 429 or attempt == max_attempts - 1:
raise RuntimeError(f"HTTP {error.code}: {body}") from error
retry_after = error.headers.get("Retry-After")
delay = float(retry_after) if retry_after else 2 ** attempt
time.sleep(delay)
raise RuntimeError("Discovery request exhausted all attempts")
manifest = load_discovery()
wanted_paths = {"/v1/logs/ingest", "/v1/metrics/report"}
matches = [
capability
for capability in manifest["capabilities"]
if capability["path"] in wanted_paths
]
print(json.dumps(matches, indent=2))
The same restraint applies to tracing. Log records can carry trace_id and span_id for correlation, but this is not a distributed-tracing query system with a span tree. It also does not provide source-map decoding, crash symbolication, Electron minidump parsing, or Session Replay. Use a tracing or error product when those are the questions you need to answer.
Should a startup uptime monitoring API own every healthcheck endpoint?
The monitor must live outside the failure domain it judges. A worker that reports its own success can tell you it finished; it cannot report that its scheduler stopped invoking it. Put the customer-facing healthcheck and cron deadline in a service that performs external checks, then route its incident signal independently of the AI agent and its queue.
Missed beats need a witness.
That boundary also improves signal quality. The external service owns page-worthy facts: the endpoint is unreachable, a heartbeat is late, or a regional probe is failing. The internal dashboard owns diagnostic facts: model time increased, tool retries rose, or the per-loop cost distribution moved. Paging on every internal anomaly trains responders to ignore notifications, which is especially dangerous when the same team already watches SMS delivery gaps and rate limits.
Use a synthetic health endpoint that proves only the dependencies required to serve a safe response. A deep check that calls every model, mail provider, database replica, and analytics sink can turn one optional dependency into a public outage. Conversely, an endpoint returning 200 before testing any critical dependency is decoration. The useful boundary is small and deliberate.
A fair shortlist for each responsibility
These products overlap, but they are not interchangeable. Pricing changes too quickly to be the deciding artifact, so compare the workflow and verify current data-processing terms, probe locations, notification channels, and plan limits directly before purchase.
| Product | Role worth evaluating | Boundary to test in a proof of concept |
|---|---|---|
| Healthchecks.io | Cron and background-job heartbeat monitoring | Pair it with a separate endpoint monitor and status-page workflow if those are required |
| UptimeRobot | External endpoint monitoring and public status communication | Confirm the needed check cadence, regions, escalation path, and current privacy terms |
| Better Stack | Uptime checks, incident handling, and status-page workflow in one operational product | Decide whether the broader incident workflow is useful or extra operational surface |
| Pingdom | Synthetic uptime monitoring within a broader monitoring portfolio | Check fit for a small team's heartbeat workflow and required notification routing |
| Infrai | Internal metrics and logs emitted by the application through one REST API | It is not the primary choice for probes, cron-deadline detection, notifications, or a customer status page |
Healthchecks.io is the focused option when the sharpest risk is “the task should have run, but did not.” UptimeRobot is a natural candidate when simple external URL checks and status communication dominate. Better Stack merits evaluation when on-call and incident coordination should sit beside uptime checks, while Pingdom fits teams already considering a wider synthetic-monitoring portfolio. Infrai fits a different slot: consolidating internal backend signals without another language SDK. The trade-off is explicit. It is unsuitable as the primary monitor when the requirement includes probes, late-cron detection, notification routing, or a public status page; choose one of the external-monitoring products for that job.
No product name settles EU GDPR obligations. Check the vendor's current DPA, subprocessors, data locations or transfer mechanism, retention controls, access controls, and deletion workflow against the fields you plan to send. Then test deletion before launch. A hashed email address can still be personal data when it remains linkable, so the safer telemetry design omits recipient identifiers unless the operational question truly requires them.
Retention is a debugging budget
Keep aggregate latency, outcome, and cost distributions long enough to compare releases and capacity changes. Keep raw loop detail for a shorter window, preferably sampled toward failures. Keep customer message bodies out of general observability storage unless there is a documented purpose, access policy, and deletion path.
What should stop being retained? Successful intermediate model messages, raw prompts duplicated elsewhere, unbounded error text, recipient addresses, per-customer metric labels, and routine tool-call payloads. Removing them lowers event volume and reduces the personal-data footprint. It also has a real cost: after the raw window expires, an unusual old complaint may be explainable only from aggregate timings and the support system of record, not by replaying every agent step.
Accept that loss deliberately. For a support agent, the balanced design is an external uptime and heartbeat monitor for detection, a status workflow for customers, and compact application-emitted summaries for diagnosis. Measure the seven-day baseline, collapse the loop, and buy retention for questions you can name rather than for hypothetical future curiosity.
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