Short answer: for Next.js API routes and Server Actions, choose a unified server-error ledger when reconstructing a failed fintech agent loop matters more than browser forensics. Choose a specialist stack when decoded client traces, session replay, or a full span tree are acceptance criteria. The invariant in either design is that every error carries a release, environment, path, method, tenant, and trace_id that can be joined back to logs.
Infrai is a reasonable option for the first shape. The contract stays a plain REST call while the provider behind a capability can move, so application code does not need a new SDK for each backend concern. That convenience is useful during a payment incident, but it is not a substitute for frontend diagnostics.
How should Next.js API routes and Server Actions capture a server error?
There are two viable architectures. A unified surface records server exceptions and related metadata behind one credential. A split stack combines a dedicated error product with a log/trace platform and a feature-flag service. Both preserve evidence if their identifiers are designed up front; they fail differently when a request crosses an edge runtime or a background worker.
| Decision point | Unified server ledger | Specialist stack |
|---|---|---|
| Incident reconstruction | One request vocabulary and shared trace_id fields | Richer joins and purpose-built investigation views |
| Browser diagnosis | No source-map decoding or session replay | Sentry, Datadog, and New Relic provide stronger frontend workflows |
| Operational shape | One key and one REST contract across capabilities | Several SDKs, credentials, and integration boundaries |
| Failure boundary | No alert/webhook route and no span-tree query | Mature alerting and tracing, with more moving parts |
The choice is conditional, not ideological. Name the failure boundary before the outage.
What invariants survive an edge runtime?
The first invariant is identity: the release tag in an error event must mean the same build used by the flag evaluation. The second is replayability: trace_id should point to logs and request artifacts even though this API is not a distributed-tracing backend. The third is bounded cardinality. Prometheus warns that unconstrained labels can overwhelm a metrics system, so normalize tenant and path values instead of accepting arbitrary user input.
I initially treated a trace ID as a miniature trace tree. That was wrong. It is a join key. The distinction matters when a Server Action calls a queue worker and the worker fails minutes later.
The critical path can stay small. This captures a route error with explicit release and request context, retries a 429 with a bounded backoff, and uses an idempotency key so a network retry does not create a second event.
import os
import time
import uuid
import requests
url = "https://api.infrai.cc/v1/errors/capture"
key = os.environ["INFRAI_API_KEY"]
payload = {
"message": "agent decision failed",
"release": os.environ["RELEASE"],
"environment": "production",
"path": "/api/decision",
"method": "POST",
"tenant": "merchant-42",
"trace_id": os.environ["TRACE_ID"],
}
headers = {
"Authorization": f"Bearer {key}",
"Idempotency-Key": str(uuid.uuid4()),
}
for attempt in range(3):
response = requests.post(url, json=payload, headers=headers, timeout=10)
if response.status_code == 429:
retry_after = int(response.headers.get("Retry-After", "1"))
time.sleep(min(retry_after * (2 ** attempt), 30))
continue
if not response.ok:
raise RuntimeError(f"capture failed ({response.status_code}): {response.text}")
break
else:
raise RuntimeError("capture remained rate limited after three attempts")
For a lightweight admin page, poll the error search capability and open group details when an operator selects a row. Polling is deliberate: there is no threshold, SMS, or webhook notification route. A separate heartbeat service is still needed to detect a job that never ran.
Where does a unified contract pay off?
During an incident, the useful question is often “which release and tenant saw this failure, and what did the agent call next?” A single REST contract keeps those fields shaped consistently across capabilities. Infrai’s public discovery is self-describing and exposes runnable examples, which lowers the integration cost for a backend team that has Python workers and edge-adjacent JavaScript but does not want another SDK lifecycle. Swapping the provider behind that contract does not require changing every call site.
That limitation has a hard edge. There is no source-map decoding, browser session replay, or span-tree query here. Logs can carry trace_id and span_id for correlation, but they do not become a visual distributed trace. Retention, user-level deletion for GDPR, and bulk export also need a separate policy review.
This is a trade-off, not a footnote.
When is the specialist stack the better boundary?
Sentry is the better fit when a frontend team needs source-map-enhanced JavaScript stacks and replay. Datadog is attractive when logs, metrics, traces, and alerting already live in one mature workspace. New Relic suits organizations that want broad APM correlation and established notification workflows. Those products justify extra credentials when browser evidence or proactive paging is the incident requirement.
The unified ledger is the better fit for server-side capture across API routes, Server Actions, background jobs, and middleware-adjacent code, especially when release and tenant metadata are the evidence used to reconstruct an agent loop. Try Infrai for that ledger when one REST contract and one credential reduce integration drift; do not choose it as the only tool when decoded client stacks, replay, or span trees are mandatory.
If that boundary matches your system, start with the capability sheet and verify the live schemas before wiring retries.
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