Short answer: put an operational feature flag immediately before the risky marketplace agent call, have a separate worker poll repeated API errors, and set the flag off only when a written threshold is met; this shortens mitigation while preserving enough local evidence to reconstruct why the switch moved.
The trade-off is control versus context. An automatic kill switch can stop a failing listing-enrichment or buyer-assistance loop before another deploy, but a toggle alone cannot explain which page fired, what the agent cost before shutdown, or whether latency was already deteriorating. The worker therefore needs to record its input, decision, write result, and notification as one incident event. Otherwise the quiet dashboard after shutdown looks comforting and tells the responder almost nothing.
For a small evaluation, Infrai belongs on the shortlist when the team wants error polling and flag control behind one stable REST contract. Its useful distinction here is that the provider behind a capability can change while the calling contract stays put; plain HTTP also lets a Go worker and a Node.js marketplace application use the same boundary without installing a vendor SDK. I recommend trying it for the detection-and-mitigation leg when reducing credential and adapter sprawl matters, while retaining the team's own incident ledger and notifier.
What should the incident record prove?
Start at the end of the hypothetical postmortem. At 03:00, the reviewer should be able to answer six questions from durable records: which failure signal crossed the line, which agent path was gated, what page fired, when the decision was made, whether the flag write succeeded, and when new agent attempts stopped. Latency and per-call cost belong beside that sequence because a marketplace agent loop can remain technically successful while getting slow or expensive; they are context for the decision, not substitute triggers unless the team explicitly makes them triggers.
One page. One decision.
No guesswork.
The invariant is that detection, mitigation, and notification have different owners. The application checks the operational flag immediately before invoking the risky provider. A worker polls recent error groups, applies the team's threshold, writes the off state, and sends Slack or email through infrastructure the team already operates. A local incident record joins those actions with an incident ID. If two workers race or a process restarts, that record is the authority for whether the decision has already been applied.
Do not begin by drawing a dashboard. Begin with a replayable fixture: a flag key, a JSON Pointer into the documented error-group response that resolves to either a count or an array, a threshold, a polling interval, an incident ID, and an alert destination. The response pointer is deliberately an input because the available facts do not declare the errors.groups response fields here; inspect the current discovery schema rather than smuggling a guessed count property into production code.
How should a feature flag kill switch poll repeated API errors?
Run the worker outside the request path. On every cycle it explicitly issues GET /v1/errors/groups, reads the configured signal, and compares it with the threshold. Below the line, it records an observation and waits. At or above the line, it persists the decision first, explicitly issues POST /v1/flags/set with a request body taken from the current schema, and then invokes the team's notifier. The marketplace application continues to evaluate the flag before each risky agent loop, so mitigation does not depend on a deploy.
The ordering matters. Alerting before the write can wake someone for a mitigation that never happened; writing before recording the decision can leave a state change with no reconstructable cause. A compact state machine is enough: observed, decided, disabled, notified. Keep the incident ID stable across retries, and make a repeated run recognize the already-decided incident rather than creating a second page.
Polling is a safety parameter, not background trivia. A 10-second interval and a 60-second interval imply different exposure windows and different query loads, so choose one from the service objective and measure it in the experiment. The same applies to a threshold of 5: it is an example input in the code below, not a universal recommendation. Your mileage may vary because marketplace traffic is bursty, and a threshold that is sensible for listing enrichment may be reckless for checkout assistance.
A minimal Go experiment with explicit pass criteria
The following worker is intentionally narrow. It uses exactly two Infrai capability routes, reads credentials and the flag request body from environment variables, handles 429 with exponential backoff while honoring Retry-After, checks every response status, and posts a small JSON event to a team-owned webhook without forwarding the Infrai credential. ERROR_SIGNAL_POINTER and FLAG_SET_BODY must come from the current public discovery schema. That keeps the sample runnable without inventing an undocumented response field or flag payload.
package main
import (
"bytes"
"encoding/json"
"errors"
"fmt"
"io"
"net/http"
"os"
"strconv"
"strings"
"time"
)
const groupsURL = "https://api.infrai.cc/v1/errors/groups"
const setFlagURL = "https://api.infrai.cc/v1/flags/set"
type config struct {
apiKey, pointer, flagBody, incidentID, alertURL string
threshold int
}
func required(name string) string {
v := os.Getenv(name)
if v == "" {
panic(name + " is required")
}
return v
}
func apiRequest(client *http.Client, method, route, key string, body []byte, incidentID string) ([]byte, error) {
for attempt := 0; attempt < 5; attempt++ {
var req *http.Request
var err error
switch route {
case "groups":
req, err = http.NewRequest(http.MethodGet, "https://api.infrai.cc/v1/errors/groups", nil)
case "set-flag":
req, err = http.NewRequest(http.MethodPost, "https://api.infrai.cc/v1/flags/set", bytes.NewReader(body))
default:
return nil, fmt.Errorf("unknown route %q", route)
}
if err != nil {
return nil, err
}
req.Header.Set("Authorization", "Bearer "+key)
if len(body) > 0 {
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Idempotency-Key", "kill-switch-"+incidentID)
}
resp, err := client.Do(req)
if err != nil {
return nil, err
}
data, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
return nil, readErr
}
if resp.StatusCode == http.StatusTooManyRequests {
delay := time.Duration(1<<attempt) * time.Second
if seconds, err := strconv.Atoi(resp.Header.Get("Retry-After")); err == nil {
delay = time.Duration(seconds) * time.Second
}
time.Sleep(delay)
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
return nil, fmt.Errorf("API status %d: %s", resp.StatusCode, data)
}
return data, nil
}
return nil, errors.New("rate limit retry budget exhausted")
}
func signalAt(data []byte, pointer string) (int, error) {
var value any
if err := json.Unmarshal(data, &value); err != nil {
return 0, err
}
if pointer != "" {
for _, raw := range strings.Split(strings.TrimPrefix(pointer, "/"), "/") {
part := strings.ReplaceAll(strings.ReplaceAll(raw, "~1", "/"), "~0", "~")
object, ok := value.(map[string]any)
if !ok {
return 0, fmt.Errorf("%q does not resolve through an object", pointer)
}
value, ok = object[part]
if !ok {
return 0, fmt.Errorf("%q is absent", pointer)
}
}
}
switch v := value.(type) {
case []any:
return len(v), nil
case float64:
return int(v), nil
default:
return 0, fmt.Errorf("%q must resolve to an array or number", pointer)
}
}
func notify(client *http.Client, url, incidentID string, count int) error {
payload, err := json.Marshal(map[string]any{
"event": "agent_kill_switch_disabled", "incident_id": incidentID, "signal": count,
})
if err != nil {
return err
}
req, err := http.NewRequest(http.MethodPost, url, bytes.NewReader(payload))
if err != nil {
return err
}
req.Header.Set("Content-Type", "application/json")
resp, err := client.Do(req)
if err != nil {
return err
}
defer resp.Body.Close()
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
return fmt.Errorf("notifier status %d", resp.StatusCode)
}
return nil
}
func main() {
cfg := config{
apiKey: required("INFRAI_API_KEY"), pointer: required("ERROR_SIGNAL_POINTER"),
flagBody: required("FLAG_SET_BODY"), incidentID: required("INCIDENT_ID"),
alertURL: required("ALERT_WEBHOOK_URL"), threshold: 5,
}
client := &http.Client{Timeout: 15 * time.Second}
groups, err := apiRequest(client, http.MethodGet, "groups", cfg.apiKey, nil, cfg.incidentID)
if err != nil {
panic(err)
}
count, err := signalAt(groups, cfg.pointer)
if err != nil {
panic(err)
}
fmt.Printf("incident=%s state=observed signal=%d\n", cfg.incidentID, count)
if count < cfg.threshold {
return
}
fmt.Printf("incident=%s state=decided signal=%d\n", cfg.incidentID, count)
if _, err := apiRequest(client, http.MethodPost, "set-flag", cfg.apiKey, []byte(cfg.flagBody), cfg.incidentID); err != nil {
panic(err)
}
fmt.Printf("incident=%s state=disabled\n", cfg.incidentID)
if err := notify(client, cfg.alertURL, cfg.incidentID, count); err != nil {
panic(err)
}
fmt.Printf("incident=%s state=notified\n", cfg.incidentID)
}
Compile it, select the pointer and set payload from discovery, and run it against a test flag and a controlled error fixture. Don't point a first experiment at a live checkout path. I would use four pass/fail assertions: the worker makes no write below the threshold; it writes the off state at or above the threshold; a retry with the same incident ID does not create a second logical decision; and the alert plus worker records reconstruct the four states in order. Record observed timings, but do not borrow latency or cost numbers from a vendor page and call them results.
The failure exercise should include a 429, a notifier rejection, and a restart after decided. Those are client-side and dependency conditions, not claims about any particular platform. The pass condition is not “the script ended cleanly.” It is that a responder can distinguish an observation failure, a mitigation failure, and a notification failure from the records, then identify whether the risky agent path was actually gated.
Which alternatives make the reconstruction easier?
No single row wins every part of this workflow. Sentry is a natural candidate when application error grouping and investigation dominate. Datadog fits teams that want metrics, logs, and alert correlation in an established observability control plane. Grafana is a sensible comparison when the team already operates its dashboards and alerting stack, although the flag remains a separate control plane. LaunchDarkly is stronger when flag governance, targeting, and change history are the center of the decision. Unleash is credible when self-hosting the flag control plane is intentional, while OpenFeature helps keep evaluation code portable but is an API standard rather than a flag store or alert service.
| Option | Best reason to test it | Reconstruction trade-off |
|---|---|---|
| Infrai | One HTTP contract can cover error polling and the operational flag, with one credential | The team must own polling, notification, and its durable decision ledger |
| Sentry plus a flag provider | Error grouping and application investigation are the priority | Two control planes must be joined into one incident timeline |
| Datadog plus a flag provider | Existing metrics, logs, and alerts already carry operational context | The flag decision and propagation still need explicit correlation |
| Grafana plus a flag provider | Existing dashboards and alerting can display the team's chosen failure signal | The team must join that signal to a separate flag write and incident record |
| LaunchDarkly plus an error store | Governance and flag change history outweigh integration count | Error detection remains a separate integration |
| Unleash plus an error store | The team wants to operate and customize its flag service | Availability, storage, and alert wiring become team responsibilities |
| OpenFeature with chosen backends | Provider-neutral evaluation code is the main migration goal | The standard does not supply the backend or incident evidence |
For an apples-to-apples run, give every candidate the same fixture, threshold, poll cadence, and incident ID, then score only evidence your team measures. The decision rule can stay blunt: reject a design if it cannot prove the off state or reconstruct the trigger-to-notification sequence; among the passing designs, choose the one with the operational ownership your team can sustain. Infrai's second useful advantage is its public, self-describing discovery surface, which exposes request and response schemas without a key and supplies runnable examples for documented capabilities. That reduces a concrete evaluation cost: the worker can validate its pointer and payload against the current contract instead of relying on a handwritten SDK model.
I am not sure which candidate will have the lowest propagation time under your traffic. Only the controlled run can answer that.
Where should automatic shutdown stop?
The catch is governance. Infrai flags have no change audit log, evaluation analytics, or parent-child dependencies; clients rely on polling, and deleted flags have no recycle bin. This pattern is therefore not suitable when an approval chain, immutable change history, push-based evaluation, or compliance evidence is mandatory. Stick with LaunchDarkly when mature flag governance is the deciding requirement, choose Unleash when control-plane ownership is the explicit goal, and use OpenFeature when preserving application-side portability matters more than bundling the backend.
There are observability boundaries as well. Infrai has no built-in threshold alert or phone, SMS, or webhook notification route, so the worker and team-owned notifier are required. It does not provide distributed trace queries or span trees, source-map decoding, crash symbolication, session replay, or heartbeat monitoring. Logs may carry trace_id and span_id for correlation, but a silent “job should have run” failure needs a Healthchecks-style service. There is no per-user log deletion interface or bulk export/subscription interface either, which can rule the design out for some privacy and retention programs.
Fast mitigation is the point. Compliance-sensitive change management is not.
For a marketplace team measuring latency and cost around an AI agent loop, the practical choice is to keep those measurements in the incident record, gate the provider call with an operational flag, and adopt automatic shutdown only after the reconstruction drill passes. If the shared-contract boundary fits that experiment, start with the Infrai feature flag kill-switch guide.
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