Use unhandled exception API

[gyuheon0h]

What does this PR do?
Uses ddog_crasht_report_unhandled_exception to report unhandled exceptions instead of building a crash report piece by piece manually.

We no longer test crash report internals for unhandled exceptions; the implementation detail should be abstracted away to libdatadog; we need to just test that we trigger this on at_exit

Motivation:
Libdatadog has a new API to report unhandled exceptions, here: feat(crashtracking): unhandled exception reporting FFI

We should use it , since it abstracts away the building and reporting logic for a crash report, instead of handling building and exporting crash reports on the library side

Change log entry
None

Additional Notes:

How to test the change?
Run a ruby program instrumented with this and crash it and check the agent logs.

Run rpsec test to ensure at_exit hook calls the report func

[gyuheon0h]

• Use unhandled exception API #5411 👈 (View in Graphite)
• master

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[github-actions]

Thank you for updating Change log entry section 👏

^{Visited at: 2026-03-02 21:38:20 UTC}

[datadog-datadog-prod-us1]

✅ Tests

🎉 All green!

❄️ No new flaky tests detected
🧪 All tests passed

🎯 Code Coverage (details)
• Patch Coverage: 100.00%
• Overall Coverage: 95.10% (-0.01%)

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: e4b4b24 | Docs | Datadog PR Page | Was this helpful? React with 👍/👎 or give us feedback!}

[pr-commenter]

Benchmarks

Benchmark execution time: 2026-03-03 14:29:28

Comparing candidate commit e4b4b24 in PR branch gyuheon0h/new-unhandled-exc-epi with baseline commit 158e037 in branch master.

Found 1 performance improvements and 1 performance regressions! Performance is the same for 42 metrics, 2 unstable metrics.

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 ----------------------------------'

scenario:profiling - hold / resume

• 🟥 throughput [-107182.851op/s; -103905.161op/s] or [-6.243%; -6.052%]

scenario:profiling - sample timeline=false

• 🟩 throughput [+0.429op/s; +0.488op/s] or [+6.398%; +7.283%]

[ivoanjo]

👍 Left two comments but overall it's looking great.