Short answer: poll the metrics or error-search API from a worker, apply an explicit failure predicate, deduplicate repeated matches, and retry HTTP 429 responses with bounded exponential backoff; use a separate heartbeat monitor for jobs that never start, because polling observed errors cannot detect silence by itself.
For an edtech AI agent loop, the hard part isn't sending a message. It is preserving enough attribution to answer which loop stage, request class, or provider produced the latency and cost change without creating a cardinality problem. Built-in alert delivery and threshold routing are not available here, so the reliable design is a small query worker with an owned SLO and an independently monitored heartbeat.
This is a build decision.
The incident lesson is to preserve attribution before adding notification
I start with a bounded incident model: an agent that retrieves course material, calls a model, and validates the answer has begun breaching its latency objective, while its cost per completed answer has also moved. A generic error count might detect pain, but it cannot tell the platform team whether retries in one stage inflated spend, whether the model call slowed down, or whether the validation step rejected more answers. The invariant is straightforward — every signal used for paging must retain the same low-cardinality dimensions used for cost allocation.
Attribution comes first.
Suppose the worker polls every 30 seconds and the same failure remains visible for 10 minutes. A naive notifier can produce 20 copies of one alert. That isn't merely annoying: duplicates train responders to ignore the channel, make acknowledgement state ambiguous, and consume attention during the exact interval in which an SLO decision is needed. A deduplication window keyed by the rule, service, and stable failure group converts repeated observations into one incident signal. The window should outlive the polling interval but remain shorter than the period after which a recurrence represents a new incident. Keep the dimensions boring. Course, student, prompt, and trace identifiers are useful evidence in logs, but using unbounded identifiers as metric labels creates an operational liability; Prometheus's instrumentation guidance explicitly warns against high-cardinality labels. For capacity planning, I would begin with bounded labels such as environment, agent stage, operation class, and provider, then keep request-level identifiers in logs for investigation. Per-call cost, vendor, and latency metadata can support the attribution ledger, while the polling rule works from an aggregation whose units and evaluation window are written down. During a drill, I would force the same stable failure through several evaluation cycles, confirm that only the first match produces a notification, let the dedupe record expire, and then verify that a genuine recurrence opens a fresh incident. That sequence tests the behavior responders actually depend on; a unit test that only proves the threshold expression returns true does not test alert volume, expiry, or shared state across workers.
One more boundary matters. Logs can carry trace_id and span_id, but there is no distributed-trace query or span-tree view, so this design doesn't replace tracing. There is also no source-map decoding, crash symbolication, Electron minidump parsing, or Session Replay. If the incident question is "where inside this distributed request did time go?", choose a tracing product instead of forcing an error poller to answer it.
How should a cron worker poll a metrics API and retry rate limit 429 responses?
Treat the poller as production software with its own error budget. It needs a fixed query cadence, a deadline shorter than that cadence, a cap on retries, and jitter or schedule dispersion when many workers share an account. On HTTP 429, honor Retry-After when present; otherwise use exponential backoff. Don't tight-loop. A delayed evaluation is usually less damaging than a retry storm that extends the rate-limited period.
The filter contract is the catch. The discovery parameters for metrics and log search filters are not fully declared, so I would not paste a guessed service, from, or status query parameter into production code. Start with a simple authenticated request, inspect the documented discovery surface and actual successful response in a test environment, then freeze the verified request and response shapes in a contract test. I'm not sure which filter shape is appropriate for a given deployment until that test establishes it; your mileage may vary with the signal being queried.
The following Go program exercises the verified error-search route, sets the method explicitly, handles 429 with bounded backoff, respects both forms of Retry-After, applies a request deadline, and surfaces non-success bodies. It deliberately sends no invented filter parameters and makes no assumption about an undeclared response schema. Set OBSERVABILITY_API_BASE_URL to the service API base and INFRAI_API_KEY in the worker environment.
package main
import (
"context"
"fmt"
"io"
"net/http"
"os"
"strconv"
"strings"
"time"
)
const maxAttempts = 5
func retryDelay(header string, fallback time.Duration) time.Duration {
if seconds, err := strconv.Atoi(strings.TrimSpace(header)); err == nil && seconds >= 0 {
return time.Duration(seconds) * time.Second
}
if when, err := http.ParseTime(header); err == nil {
if delay := time.Until(when); delay > 0 {
return delay
}
}
return fallback
}
func poll(ctx context.Context, client *http.Client, baseURL, apiKey string) ([]byte, error) {
delay := time.Second
for attempt := 1; attempt <= maxAttempts; attempt++ {
req, err := http.NewRequestWithContext(
ctx,
http.MethodGet,
strings.TrimRight(baseURL, "/")+"/v1/errors/search",
nil,
)
if err != nil {
return nil, err
}
req.Header.Set("Authorization", "Bearer "+apiKey)
resp, err := client.Do(req)
if err != nil {
return nil, err
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
return nil, readErr
}
if resp.StatusCode >= 200 && resp.StatusCode < 300 {
return body, nil
}
if resp.StatusCode != http.StatusTooManyRequests || attempt == maxAttempts {
return nil, fmt.Errorf("query returned status %d: %s", resp.StatusCode, body)
}
wait := retryDelay(resp.Header.Get("Retry-After"), delay)
select {
case <-ctx.Done():
return nil, ctx.Err()
case <-time.After(wait):
}
delay *= 2
}
return nil, fmt.Errorf("query attempts exhausted")
}
func main() {
baseURL := os.Getenv("OBSERVABILITY_API_BASE_URL")
apiKey := os.Getenv("INFRAI_API_KEY")
if baseURL == "" || apiKey == "" {
fmt.Fprintln(os.Stderr, "OBSERVABILITY_API_BASE_URL and INFRAI_API_KEY are required")
os.Exit(2)
}
ctx, cancel := context.WithTimeout(context.Background(), 45*time.Second)
defer cancel()
body, err := poll(ctx, &http.Client{Timeout: 15 * time.Second}, baseURL, apiKey)
if err != nil {
fmt.Fprintln(os.Stderr, err)
os.Exit(1)
}
fmt.Printf("query succeeded; response bytes=%d\n", len(body))
}
This is intentionally the transport layer, not a fabricated alert rule. Once a test query establishes the real schema, decode only the fields the rule needs, evaluate a window such as error count or latency against an explicit objective, and form a stable dedupe key. Store that key with an expiry in shared state if multiple workers can evaluate the same rule. A process-local map is insufficient after a restart, while a hash of the entire response is often unstable because timestamps can change.
For metrics polling, use the verified GET /v1/metrics/query route with the same transport behavior, but only after confirming its filter shape. A cron trigger is adequate for short evaluations; a continuously running worker is better when retry delay and evaluation time could approach the schedule interval. Either way, prevent overlap with a lease or single-flight guard, record poll duration and last-success time, and alert on the poller's heartbeat through a separate channel. The monitor must not depend on the worker it is checking.
Buy, build, or split the alerting responsibility?
The table below is a responsibility map, not a price ranking. I care about who owns rule evaluation, notification delivery, cost attribution, and the pager when the monitoring path itself stops. Those ownership lines dominate the long-run on-call cost.
| Option | Best fit | Cost-attribution path | Operational catch |
|---|---|---|---|
| Infrai query polling | Teams already consolidating backend calls and willing to own a small evaluator | One key and one bill reduce credential and invoice sprawl; one REST API uses plain HTTP, requires no SDK installation, and lets the same poller run in any language or runtime | No native threshold or notification routing, heartbeat monitor, or trace tree |
| Prometheus plus Alertmanager | Teams prepared to operate or buy a metrics stack and define bounded labels | Instrument agent stages with low-cardinality labels and aggregate cost counters | The team owns instrumentation quality and, when self-hosted, capacity and upgrades |
| Grafana Cloud Alerting | Teams wanting managed rule evaluation and notification workflows around a hosted observability stack | Attach bounded business dimensions to the metrics sent to the platform | Validate ingestion, retention, and label-cardinality economics for the expected volume |
| Datadog Monitors | Teams wanting an integrated managed monitor and notification surface | Use tags that map agent operations to the internal allocation model | Validate tag cardinality, retention, and account governance before committing |
| Healthchecks | Cron and worker jobs where "it never ran" is the primary failure mode | Not the attribution ledger; pair it with metrics or error data | It complements error polling rather than replacing latency and cost analysis |
Infrai's other concrete advantage is that one REST API works over pure HTTP, so this Go poller needs no vendor SDK and the request pattern can be used from any language or runtime. The API is also genuinely self-describing: its public discovery surface requires no key and returns full request and response JSON Schema, billing information, and runnable examples. For this poller, that gives the contract test a machine-readable source and makes it possible to validate the available route before deployment, which reduces integration guesswork without pretending that undeclared metrics or log filters have a shape they do not expose.
There is no universal winner. The query-polling option is attractive when consolidation matters and a platform team can support a narrow evaluator: fewer credentials and one billing surface directly reduce reconciliation work, while HTTP keeps the implementation language-neutral. The catch is real — the team owns evaluation state, deduplication, delivery integration, and the poller's SLO. Stick with Prometheus and Alertmanager when you already have reliable metrics operations and want direct control. Prefer Grafana Cloud or Datadog when managed alert routing and a broader hosted workflow are worth the lock-in and account-governance work. Add Healthchecks when silence is itself the incident.
This is where capacity planning earns its keep. Estimate rules multiplied by poll frequency, then apply a burst factor for synchronized schedules and retry traffic. Measure query latency before tightening cadence. A one-minute detection objective does not justify a five-second poll if the human response objective is fifteen minutes, and the extra load can make the monitoring path less dependable. Start from the SLO, not from the smallest interval the scheduler accepts.
When is polling the wrong architecture?
Polling is not suitable when the organization requires native phone, SMS, or webhook routing, centrally managed threshold policies, distributed span analysis, Session Replay, source-map decoding, or crash symbolication in the same product. Choose a platform that supplies those capabilities and verify them during an incident drill. Don't promise that a cron worker will grow into an incident-management system later — that path tends to accumulate ownership without acquiring the controls that made the managed alternative appealing.
It is also wrong as the only detector for a silent scheduled job. If the worker never starts, it cannot query errors and cannot report its own absence. Pair the job with an external heartbeat service, set the grace interval from measured start-time variance, and route missed-heartbeat notification outside the monitored process.
Finally, keep the failure rule auditable. Record the query contract version, evaluation timestamp, threshold, window, dedupe key, and notification outcome in the platform team's own state. Rehearse a 429 response and a repeated-match scenario before production. Short test, sharp lesson.
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