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Add LLMObs.submitFeedback for end-user feedback submission - #12129

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Add LLMObs.submitFeedback for end-user feedback submission#12129
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Ports the end-user feedback SDK from dd-trace-py (#19347) and dd-trace-js
(#9654) to Java.

Feedback is submitted through a builder rather than overloads: with 4 target
kinds, 5 value types and 3 optional fields, an overload-based API would need
~25 signatures. The builder also makes the runtime type/value coherence checks
that Python and Node.js need statically impossible, since each value type has
its own setter.

Feedback is a v2 eval-metric concept -- event_kind, submitter and the
non-score value types only exist there -- while Java evaluations post to v1.
Since one batch maps to one HTTP request, the two cannot share a worker, so
EvalProcessingWorker is generalised into LLMObsIntakeWorker<T> parameterised
by API path, thread and batch serializer. The eval path keeps every parameter
it had (queue capacity, flush interval, flush threshold, retry policy, URL and
header construction) and its payload is byte-for-byte identical; only the log
message wording differs, now that it is templated per payload kind.
LLMObsEvalTest pins the v1 payload against regression.

submitFeedback goes through its own LLMObsFeedbackProcessor interface rather
than a new method on LLMObsEvalProcessor, to avoid breaking binary
compatibility for external implementors of a published interface.
@datadog-official

datadog-official Bot commented Aug 3, 2026

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🎯 Code Coverage (details)
Patch Coverage: 75.88%
Overall Coverage: 58.02% (+0.12%)

This comment will be updated automatically if new data arrives.
🔗 Commit SHA: fcd1138 | Docs | Datadog PR Page | Give us feedback!

@dd-octo-sts

dd-octo-sts Bot commented Aug 3, 2026

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🟢 Java Benchmark SLOs — All performance SLOs passed

Suite Status
Startup 🟢 pass

SLO thresholds are defined here based on automatically generated metrics. A warning is raised when results are within 5% of the threshold.

PR vs. master results
Scenario Candidate master Δ (95% CI of mean)
startup:insecure-bank:iast:Agent 13.97 s 13.93 s [-0.6%; +1.0%] (no difference)
startup:insecure-bank:tracing:Agent 12.93 s 12.99 s [-1.1%; +0.3%] (no difference)
startup:petclinic:appsec:Agent 16.84 s 16.65 s [+0.2%; +2.2%] (maybe worse)
startup:petclinic:iast:Agent 16.87 s 16.96 s [-1.5%; +0.5%] (no difference)
startup:petclinic:profiling:Agent 16.23 s 16.34 s [-6.9%; +5.6%] (unstable)
startup:petclinic:sca:Agent 16.84 s 16.46 s [+1.0%; +3.7%] (maybe worse)
startup:petclinic:tracing:Agent 16.16 s 15.83 s [-2.2%; +6.4%] (no difference)

Commit: fcd1138f · CI Pipeline · Benchmarking Platform UI


Load and DaCapo benchmarks can be triggered manually in the GitLab pipeline. Results will appear in the Benchmarking Platform UI after completion.

@ddog-thibault-nadin ddog-thibault-nadin added tag: ai generated Largely based on code generated by an AI or LLM comp: mlobs ML Observability (LLMObs) type: feature Enhancements and improvements labels Aug 3, 2026
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