feat(openfeature): add agentless configuration polling

[pavlokhrebto]

What does this PR do?

Adds the Agentless configuration delivery mechanism itself, stacked on #5218 (source
resolution/endpoint): a JSON:API envelope parser for the Universal Flags Configuration payload
(openfeature/jsonapi.go), and the poll loop that fetches it (openfeature/agentless_source.go) —
per-poll retries with backoff, ETag/304 handling with last-known-good semantics, gzip decoding, and
per-category log-once for persistent failures. Includes an httptest.Server-based fake backend
mirroring the system-tests mock (utils/mocked_backend/ffe.py) covering the full behavior matrix:
ETag not advanced on parse failure, retry-within-poll, non-retryable failures, no-overlap between
polls, and no log line ever containing the endpoint URL.

Still not wired up to the provider — nothing issues a request until Phase 3 lands.

Stack

1. feat(openfeature): resolve agentless feature-flag configuration source and endpoint #5218 — settings, source precedence, endpoint
2. feat(openfeature): add agentless configuration polling #5222 — JSON:API parser, poll loop (this PR)
3. feat(openfeature): wire provider activation and lifecycle for agentless delivery #5223 — provider wiring, activation, lifecycle, RC gating
4. feat(openfeature): add init outcomes and provider lifecycle events #5226 — init outcomes, provider events

Motivation

Continuation of Agentless Feature Flags delivery (FFL-2695); see #5218 for the overall context.

Reviewer's Checklist

• Changed code has unit tests for its functionality at or near 100% coverage.
• System-Tests covering this feature have been added and enabled with the va.b.c-dev version tag: see Guard eager OpenFeature init and add detailed /ffe/evaluate for Go agentless configuration contract system-tests#7580 (referenced from feat(openfeature): resolve agentless feature-flag configuration source and endpoint #5218).
• There is a benchmark for any new code, or changes to existing code.
• If this interacts with the agent in a new way, a system test has been added.
• New code is free of linting errors. You can check this by running make lint locally.
• New code doesn't break existing tests. You can check this by running make test locally.
• Add an appropriate team label so this PR gets put in the right place for the release notes.
• All generated files are up to date. You can check this by running make generate locally.
• Non-trivial go.mod changes, e.g. adding new modules, are reviewed by @DataDog/dd-trace-go-guild. Make sure all nested modules are up to date by running make fix-modules locally.

Unsure? Have a question? Request a review!

[datadog-datadog-prod-us1-2]

✅ All CI checks and tests passed.

🎉 All green!

🧪 All tests passed
❄️ No new flaky tests detected

🎯 Code Coverage (details)
• Patch Coverage: 88.29%
• Overall Coverage: 69.98% (+13.30%)

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 78c4c8d | Docs | View more details | Give us feedback!}

[chatgpt-codex-connector]

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[pr-commenter]

Benchmarks

Benchmark execution time: 2026-09-08 08:27:29

Comparing candidate commit 78c4c8d in PR branch pavlo.khrebto/FFL-2695/agentless-poller with baseline commit 0dcbf18 in branch main.

📊 Benchmarking dashboard

Found 0 performance improvements and 0 performance regressions! Performance is the same for 335 metrics, 0 unstable metrics, 1 flaky benchmarks without significant changes.

Explanation

This is an A/B test comparing a candidate commit's performance against that of a baseline commit. Performance changes are noted in the tables below as:

• 🟩 = significantly better candidate vs. baseline
• 🟥 = significantly worse candidate vs. baseline

We compute a confidence interval (CI) over the relative difference of means between metrics from the candidate and baseline commits, considering the baseline as the reference.

If the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD), the change is considered significant.

Feel free to reach out to #apm-benchmarking-platform on Slack if you have any questions.

More details about the CI and significant changes

You can imagine this CI as a range of values that is likely to contain the true difference of means between the candidate and baseline commits.

CIs of the difference of means are often centered around 0%, because often changes are not that big:

---------------------------------(------|---^--------)-------------------------------->
                              -0.6%    0%  0.3%     +1.2%
                                 |          |        |
         lower bound of the CI --'          |        |
sample mean (center of the CI) -------------'        |
         upper bound of the CI ----------------------'

As described above, a change is considered significant if the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD).

For instance, for an execution time metric, this confidence interval indicates a significantly worse performance:

----------------------------------------|---------|---(---------^---------)---------->
                                       0%        1%  1.3%      2.2%      3.1%
                                                  |   |         |         |
       significant impact threshold --------------'   |         |         |
                      lower bound of CI --------------'         |         |
       sample mean (center of the CI) --------------------------'         |
                      upper bound of CI ----------------------------------'

Known flaky benchmarks

These benchmarks are marked as flaky and will not trigger a failure. Modify FLAKY_BENCHMARKS_REGEX to control which benchmarks are marked as flaky.

Known flaky benchmarks without significant changes:

• scenario:BenchmarkOTLPTraceWriterFlush

[chatgpt-codex-connector]

💡 Codex Review

Here are some automated review suggestions for this pull request.

Reviewed commit: 610c0e7d52

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[darccio]

LGTM