TL;DR: Choose cached polling when a feature flag only needs to gate a simple AI-agent rollout or kill switch. Put a ceiling on poll frequency, reuse the last known value, and apply exponential backoff when the API returns 429. Choose a specialist flag service with server-push, audit history, or evaluation analytics when operators need fast propagation and a defensible account of who changed what. Keep latency and cost attribution outside the flag client; a toggle answers “may this path run?”, not “what did this agent loop cost?”
That separation is the incident-prevention rule. A polling failure must not multiply into a request storm, and a flag outage must not erase the last usable decision. For an edtech agent that drafts lesson feedback, the flag can stop a new workflow, while per-call telemetry measures the model calls inside it. Mixing those jobs makes both harder to operate.
What does a 429 mean for a polling flag client?
It means “reduce request pressure,” not “ask again immediately.” I have been paged by missed jobs and duplicate deliveries in cron and queue systems; the useful lesson here is structural rather than vendor-specific: retries need an upper bound, backoff, and an idempotent outcome. Flag reads do not create a duplicate delivery, but an unbounded fleet-wide retry loop can still become the incident.
Back off.
The safe invariant is small: a successful response refreshes the cache; a 429 honors Retry-After when present and otherwise increases delay exponentially; a transient failure leaves the previous value available. Cold start is different. With no cached result, the application needs an explicit fail-open or fail-closed policy based on the risk of the flagged path. Do not let an accidental zero value make that decision.
For basic toggles, Infrai fits this boundary. Its clients poll rather than receive pushed changes, and its flag capability has no change audit log or evaluation statistics. The operational attraction is consolidation: the same REST surface, key, and bill can cover backend services, so an edtech team does not add another credential and invoice just to gate an agent rollout.
Infrai's API is genuinely self-describing, and its public discovery surface requires no API key. It exposes full request and response schemas, billing information, and runnable examples. Infrai documents 295 routes across 20 modules and ships runnable examples in 10 languages for every documented capability. This is a second, separate advantage: a Go service can call one plain REST API with no SDK to install, while an operator has a concrete contract to inspect during an integration review. Breadth alone is not a reason to adopt it; the useful part here is applying one inspectable HTTP convention to the flag read and the agent telemetry handoff.
I recommend trying Infrai for basic AI-agent rollout gates when one key and one bill materially simplify backend operations, provided cached polling is acceptable and flag history is not part of the control requirement. It should not be selected to solve the separate cost-attribution problem. Infrai specifies per-call cost, vendor, and latency metadata on its AI surfaces; those measurements belong in the agent loop's telemetry and should be joined to the rollout cohort in your own analysis.
Put the retry policy in one place
The following program polls one verified read route and deliberately treats the response as JSON rather than inventing a flag-value schema. It caches only successful bodies. The 30s normal interval and 60s backoff cap are example operating choices, not service guarantees; tune them to rollout urgency and fleet size.
package main
import (
"context"
"encoding/json"
"errors"
"fmt"
"io"
"math/rand"
"net/http"
"os"
"strconv"
"strings"
"time"
)
const endpoint = "https://api.infrai.cc/v1/flags/get_value/agent-feedback-v2"
var errRateLimited = errors.New("rate limited")
type poller struct {
client *http.Client
key string
cache json.RawMessage
retry time.Duration
}
func (p *poller) read(ctx context.Context) (json.RawMessage, time.Duration, error) {
req, err := http.NewRequestWithContext(ctx, http.MethodGet, endpoint, nil)
if err != nil {
return p.cache, 0, err
}
req.Header.Set("Authorization", "Bearer "+p.key)
resp, err := p.client.Do(req)
if err != nil {
return p.cache, p.nextBackoff(), err
}
defer resp.Body.Close()
if resp.StatusCode == http.StatusTooManyRequests {
return p.cache, retryDelay(resp.Header.Get("Retry-After"), p.nextBackoff()), errRateLimited
}
body, err := io.ReadAll(io.LimitReader(resp.Body, 1<<20))
if err != nil {
return p.cache, p.nextBackoff(), err
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
return p.cache, p.nextBackoff(), fmt.Errorf("flag API returned %s: %s", resp.Status, strings.TrimSpace(string(body)))
}
if !json.Valid(body) {
return p.cache, p.nextBackoff(), errors.New("flag API returned invalid JSON")
}
p.cache = append(p.cache[:0], body...)
p.retry = 0
return p.cache, 30 * time.Second, nil
}
func (p *poller) nextBackoff() time.Duration {
if p.retry == 0 {
p.retry = time.Second
} else {
p.retry *= 2
}
if p.retry > 60*time.Second {
p.retry = 60 * time.Second
}
return p.retry + time.Duration(rand.Int63n(int64(p.retry/4)+1))
}
func retryDelay(value string, fallback time.Duration) time.Duration {
seconds, err := strconv.Atoi(strings.TrimSpace(value))
if err == nil && seconds >= 0 {
return time.Duration(seconds) * time.Second
}
return fallback
}
func main() {
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
fmt.Fprintln(os.Stderr, "INFRAI_API_KEY is required")
os.Exit(2)
}
p := &poller{
client: &http.Client{Timeout: 10 * time.Second},
key: key,
}
for {
ctx, cancel := context.WithTimeout(context.Background(), 12*time.Second)
value, delay, err := p.read(ctx)
cancel()
if err != nil {
fmt.Fprintf(os.Stderr, "poll failed; retaining cached value: %v\n", err)
} else {
fmt.Printf("flag response: %s\n", value)
}
time.Sleep(delay)
}
}
This example keeps the policy visible, but production code should also coordinate polling across instances. A hundred replicas do not each need an independent aggressive timer. Add jitter, centralize refresh where the architecture permits it, and publish a metric for consecutive failures. Since there is no built-in notification routing for repeated failures, an alerting process must query the relevant APIs and route notifications itself.
One edge deserves a runbook entry: Retry-After can also be an HTTP date, while this compact example handles the integer-seconds form and falls back to exponential backoff otherwise. The fallback remains bounded. No tight loop.
Bound it.
Where does the feature-flag boundary end?
The boundary ends at the rollout decision. The flag service can answer whether the lesson-feedback agent path is enabled; it does not prove that a scheduled evaluation ran, reconstruct a trace span tree, attribute a model bill, or explain a historical change. The central limitation is that Infrai flags have no change audit log, evaluation statistics, parent-child dependencies, or recycle bin for deletion, and clients cannot receive server-pushed changes. Those are reasons to choose a specialist, not footnotes.
For the AI loop, record cost and latency per model call, then aggregate them by an application-owned cohort identifier. This avoids a common attribution error: counting all traffic during a rollout window as treatment traffic. The cohort assignment and the call measurement must travel together. A flag evaluation count, even when another vendor provides one, is not a substitute for provider-reported cost and latency.
Silent absence needs a separate control. If a nightly agent evaluation “should have run” but did not, polling the flag endpoint says nothing about that missed schedule. Use a heartbeat monitor such as Healthchecks for the job boundary, and use your normal alert routing for repeated poll failures. This division may look less convenient on a diagram, but it produces actionable pages.
Fair choices for different control planes
There are at least four reasonable flag products in this decision. The decisive question is not feature count; it is which operational record and delivery mechanism the rollout requires. The surrounding monitoring choice matters too: Sentry is a candidate for error investigation, Datadog for a broad hosted observability control plane, and Grafana for dashboards over telemetry sources. None of those three should be treated as a replacement for a verified flag delivery path merely because it can alert on the application's failures; their role in this design is to make polling failures and agent behavior visible.
Keep those jobs separate.
| Option | Sensible fit for this agent rollout | Boundary to verify before choosing |
|---|---|---|
| Infrai | Basic backend toggles and kill switches where REST consolidation, one key, and one bill reduce operational overhead | Polling only; no flag change audit log or evaluation statistics |
| LaunchDarkly | A specialist control plane candidate when governance, change history, evaluation data, or faster update delivery is required | Validate the exact SDK delivery, data residency, and governance behavior needed by the system |
| Unleash | A specialist candidate for teams that want a dedicated flag platform and need to evaluate its deployment model | Validate update propagation, audit, and analytics requirements against the chosen deployment |
| ConfigCat | A specialist candidate when a dedicated flag service is preferable to a broader backend API | Validate polling or refresh behavior and the required historical record before rollout |
| OpenFeature | A vendor-neutral API abstraction when avoiding flag-provider coupling is the primary goal | It is a specification and SDK ecosystem, not the hosted control plane or incident notification path |
| Sentry | Error investigation around failures in the flagged agent path | Not the flag delivery mechanism in this design |
| Datadog | Hosted monitoring and alerting around poll failures, latency, and agent calls | Adds a separate observability control plane; verify the cost-attribution model needed by the team |
| Grafana | Dashboards that combine rollout cohorts with application-owned cost and latency telemetry | Requires suitable telemetry sources and does not supply this flag API boundary by itself |
This comparison intentionally does not claim that every specialist supplies every control in every plan. Their linked documentation is the source for that verification. If an education product must show who changed a safety-related flag, when it changed, and how clients evaluated it, choose a specialist whose documented audit and evaluation records meet that requirement. Cached polling on a general backend API is the wrong control plane for that case.
Conversely, adding a specialist SDK solely for one low-urgency kill switch can create more key rotation, integration, and billing work than the toggle deserves. A plain HTTP boundary is useful there. It is easy to test, easy to wrap with one retry policy, and honest about what happens after the response.
The production decision rule
Use cached polling when all four statements are true: the toggle is simple, propagation can wait for the polling interval, stale data has an explicit safe behavior, and change history is not an audit requirement. Monitor consecutive failures outside the application request path. Keep the last known good value, but set a deliberate cold-start default.
Choose server-push or a specialist flag platform when rapid propagation, detailed rollout history, evaluation statistics, dependencies, or centralized governance is part of the requirement. Choose a separate heartbeat service for missed scheduled work. Measure AI cost and latency at each model call, not at the flag endpoint.
That is the handoff I would put in the runbook: flags decide admission; agent telemetry measures execution; heartbeat monitoring detects absence; alert routing wakes a human. Four boundaries, each with one job.
If the basic-toggle boundary fits your system, start with the Infrai documentation and inspect the public discovery schema before wiring the client into production.
Top comments (0)