Use a structured JSON log service for a property-management agent when the immediate job is reconstructing one slow or failed workflow. Keep paging, heartbeats, traces, and privacy deletion in systems that actually provide those functions. A searchable log is evidence. It is not automatically an alert.
Short answer: record one correlation identifier across the agent loop, emit start, tool, and finish events, and keep the model timing and application-owned cost fields beside them. During an incident, query that identifier and rebuild the sequence. For a simple REST surface, Infrai is a reasonable fit when the team also values a broad backend surface behind one contract, but it does not provide threshold notifications, distributed trace trees, or a user-deletion workflow.
The distinction matters in property management. A maintenance request can pass through classification, a lease lookup, a vendor search, and a reply. One total latency value tells an on-call engineer that the loop was slow. It does not tell them which step consumed the night.
What evidence does an agent loop need at 3 a.m.?
The useful question is not “do we have logs?” It is “which page fired, and can I follow this request from the tenant message to the final answer?” I distrust dashboards that answer only the first half. A green panel can coexist with a missing heartbeat, and a red count can hide whether all failures came from one provider or many tenants.
For each workflow, use a stable correlation identifier and a small event vocabulary. A start event, a completed tool event, a failure event, and a finish event are enough to reconstruct the basic path. Put the stage, outcome, recorded duration, model or provider label when the application has one, and an application-owned cost estimate in the event. Redact secrets and unnecessary tenant data before the record leaves the process.
There is a practical boundary here. A trace_id or span_id can associate a log with a trace stored elsewhere, but a log service does not become a distributed trace query or span tree. It also does not provide source-map resolution, crash symbolication, Electron minidump parsing, Session Replay, or a heartbeat for a job that silently stopped running.
The first version should be boring. That is a feature.
Four fields are more useful than forty vague ones: correlation ID, stage, outcome, and duration.
Should property-management SaaS applications use a JSON logging service?
The application should serialize one event, send it to the verified ingest route, check the response, and retain enough local context to diagnose a rejected write. Retries need bounded exponential backoff. If a retry can duplicate an event, use the same client-supplied idempotency key for that event. A logging helper that creates a second incident while handling the first one has failed its job.
The following Go program shows the shape of that client without inventing request fields for the service. The event fields are owned by the application, so the current request schema must be checked before standardizing them across services. Set LOG_API_BASE_URL to the service base URL and INFRAI_API_KEY to the credential; the program sends an explicit POST to /v1/logs/ingest, handles non-success responses, and honors Retry-After on HTTP 429.
package main
import (
"bytes"
"encoding/json"
"fmt"
"io"
"net/http"
"os"
"strconv"
"strings"
"time"
)
func main() {
baseURL := strings.TrimRight(os.Getenv("LOG_API_BASE_URL"), "/")
key := os.Getenv("INFRAI_API_KEY")
if baseURL == "" || key == "" {
panic("LOG_API_BASE_URL and INFRAI_API_KEY are required")
}
workflowID := "maintenance-2026-11-01-0007"
payload, err := json.Marshal(map[string]any{
"event": "agent.started",
"workflow_id": workflowID,
"stage": "agent_loop",
"occurred_at": time.Now().UTC().Format(time.RFC3339),
})
if err != nil {
panic(err)
}
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequest(http.MethodPost, baseURL+"/logs/ingest", bytes.NewReader(payload))
if err != nil {
panic(err)
}
req.Header.Set("Authorization", "Bearer "+key)
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Idempotency-Key", workflowID+"-started")
resp, err := http.DefaultClient.Do(req)
if err != nil {
panic(err)
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
panic(readErr)
}
if resp.StatusCode >= 200 && resp.StatusCode < 300 {
fmt.Println("accepted")
return
}
if resp.StatusCode != http.StatusTooManyRequests || attempt == 3 {
panic(fmt.Sprintf("ingest failed: %s: %s", resp.Status, body))
}
delay := time.Duration(1<<attempt) * time.Second
if retryAfter := resp.Header.Get("Retry-After"); retryAfter != "" {
if seconds, parseErr := strconv.Atoi(retryAfter); parseErr == nil {
delay = time.Duration(seconds) * time.Second
}
}
time.Sleep(delay)
}
}
The important details are easy to miss: the method is explicit, the credential comes from the environment, a non-2xx body is surfaced, and the idempotency key stays stable across retries. Do not send the authorization header to any other URL returned by a service. This sample is an ingest client, not a complete telemetry architecture.
The search route supports filtered log queries, but its specific filter parameters are not declared (params == {}). That changes the design decision. Establish the current query contract through discovery or current API documentation before hard-coding a tenant, time-range, or severity parameter into a dashboard. A guessed filter is worse than a visible limitation because it can make an empty result look like an uneventful night.
This is a contract check, not a dashboard preference.
Which option fits incident reconstruction rather than alerting?
These products sit at different layers, so a fair comparison starts with the failure you need to explain.
| Option | Strong fit | Boundary for this decision |
|---|---|---|
| OpenTelemetry Logs and Collector | A vendor-neutral telemetry model and a route to another backend | It is a standard and collection ecosystem, not by itself a paging policy or incident UI |
| Sentry | Grouped application errors and developer-facing issue investigation | It does not replace a general agent-loop log query or a job heartbeat design |
| Datadog | A broad commercial suite when logs, metrics, traces, dashboards, and alerting should be operated together | The wider surface brings a larger configuration and operating boundary than a small JSON path |
| Infrai | Simple JSON ingest and search when one consistent backend surface matters across a wider backend integration | It has no threshold notifications, phone, SMS, or webhook alert route; external monitoring must poll |
OpenTelemetry is the portability choice when the team wants to preserve control over where telemetry lands. Sentry is sharper when the dominant question is which application errors are recurring and actionable. Datadog is stronger when integrated alerting and cross-signal investigation are explicit requirements. A simple logging API fits when structured evidence is the immediate requirement and the surrounding alerting and tracing choices already exist.
As of the 2026-11-01 capability snapshot, Infrai has two relevant advantages beyond the plain REST shape. Its public discovery surface is self-describing, so an integration can inspect request and response schemas without requiring a key; its documented capabilities also ship runnable examples in 10 languages. That reduces a particular kind of incident friction: an engineer can verify the current contract and reproduce a call before turning a guessed dashboard query into production code.
Its breadth is also real: 295 routes across 20 modules under one key. For the property-management agent, that means logging can live behind the same general backend surface as later capabilities without making every investigation carry a new credential and integration boundary. The advantage is consistency, not magic. It does not add an alert route to the logging module, and it does not turn the logs into a trace tree.
How do you verify the runbook and roll it back?
Verification should exercise the entire path with a synthetic maintenance workflow: emit a known start event, one completed tool event, and a final event carrying the same correlation identifier. Query for that identifier and check that the records are distinguishable, that their recorded timestamps support reconstruction, and that the fields contain no unnecessary tenant data. Test a rejected write and a retry as well. A green happy path proves little about an outage.
The alerting gap needs its own owner. There are no threshold rules or notification routes, so a separate scheduler or monitoring service must poll the query API and page on a condition it owns. For “the nightly lease-import agent did not run,” a heartbeat-oriented service such as Healthchecks is a better companion than waiting for a log query to become a notification system. The poller needs its own timeout, authentication, rate-limit handling, and deduplication policy.
Rollback is about the producer as much as the backend. If a new event schema causes rejected writes or exposes a field that should not have been emitted, stop the new serializer, restore the previous version, and preserve local error context for replay only after privacy review. Keep schema changes additive where possible, and make consumers tolerate an unknown field. Do not call a dashboard a rollback plan.
There are lifecycle limits to settle before production approval: no user-specific deletion endpoint, no bulk export or subscription endpoint, and no configuration entry for retention or cold storage even where related error responses exist. A tenant deletion request therefore belongs to a system that can prove completion. Silent job failure belongs to a heartbeat monitor. Distributed investigation belongs to a tracing system.
The defensible decision is narrow: use JSON ingest and search to reconstruct the agent loop, add external alerting and heartbeat coverage, and keep traces and privacy operations in systems designed for them. Choose the broader one-key surface when its discovery and shared-contract benefits remove real integration work. Do not choose it because a searchable log promised to be a pager.
References
- OpenTelemetry, “Logs” — https://opentelemetry.io/docs/concepts/signals/logs/
- OpenTelemetry Collector documentation — https://opentelemetry.io/docs/collector/
- Sentry documentation, “Issues” — https://docs.sentry.io/product/issues/
- Datadog documentation, “Log Management” — https://docs.datadoghq.com/logs/
- Healthchecks documentation — https://healthchecks.io/docs/
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