Add custom CI Visibility spans for new-e2e test jobs

[chouetz]

Summary

• Add datadog-ci span instrumentation to e2e pipeline jobs so each setup step and test phase appears as a separate span in CI Visibility flamegraphs
• Create shared bash helpers (.setup_datadog_ci_sections) for before_script spans and Python helpers (ci_visibility_section context manager) for invoke task spans
• Auto-send accumulated Python spans via CIVisibilitySection.send_all() hook in custom_task.py

What Changed

• New tasks/libs/common/ci_visibility.py: Python CI Visibility helpers — CIVisibilitySection dataclass with batch send, ci_visibility_section() context manager (no-op outside CI)
• New .gitlab/.pre/common/ci_visibility_sections.yml: Bash helpers (datadog-ci-start-section / datadog-ci-end-section) + DATADOG_API_KEY fetch
• Modified .gitlab/test/e2e/e2e.yml: Wrapped 8 before_script steps with span functions (dep retrieval, AWS creds, SSH keys, Pulumi login, cloud creds, CI Visibility links)
• Modified tasks/custom_task/custom_task.py: Added CIVisibilitySection.send_all() in finally block after each invoke task
• Modified tasks/new_e2e_tests.py: Wrapped 6 phases in run() with ci_visibility_section (dynamic test calc, test execution attempts, teardown, merge results, process results, logs post-processing)

Span Tagging Convention

• agent-custom-span:true — filter for agent custom spans
• agent-category:e2e-setup — before_script spans
• agent-category:e2e — script (invoke task) spans

Test plan

• Push to feature branch, trigger e2e pipeline
• Verify spans in CI Visibility with filter ci_level:custom @agent-custom-span:true
• Confirm flamegraph shows before_script spans (tar extraction, credential setup) and script spans (test execution attempts)
• Verify e2e tests still pass — span sending uses || true / warn=True so failures are non-blocking

🤖 Generated with Claude Code

[agent-platform-auto-pr]

Gitlab CI Configuration Changes

⚠️ Diff too large to display on Github.

Changes Summary

Removed	Modified	Added	Renamed
0	151	1	0

ℹ️ Diff available in the job log.

[agent-platform-auto-pr]

Files inventory check summary

File checks results against ancestor a6298b3e:

Results for datadog-agent_7.79.0~devel.git.9.3e53e15.pipeline.103731930-1_amd64.deb:

No change detected

[agent-platform-auto-pr]

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor 3fb6170
📊 Static Quality Gates Dashboard
🔗 SQG Job

31 successful checks with minimal change (< 2 KiB)

	Quality gate	Current Size
✅	agent_deb_amd64	750.699 MiB
✅	agent_deb_amd64_fips	707.631 MiB
✅	agent_heroku_amd64	313.003 MiB
✅	agent_msi	604.238 MiB
✅	agent_rpm_amd64	750.682 MiB
✅	agent_rpm_amd64_fips	707.614 MiB
✅	agent_rpm_arm64	729.016 MiB
✅	agent_rpm_arm64_fips	689.008 MiB
✅	agent_suse_amd64	750.682 MiB
✅	agent_suse_amd64_fips	707.614 MiB
✅	agent_suse_arm64	729.016 MiB
✅	agent_suse_arm64_fips	689.008 MiB
✅	docker_agent_amd64	811.017 MiB
✅	docker_agent_arm64	814.164 MiB
✅	docker_agent_jmx_amd64	1001.933 MiB
✅	docker_agent_jmx_arm64	993.858 MiB
✅	docker_cluster_agent_amd64	205.172 MiB
✅	docker_cluster_agent_arm64	219.549 MiB
✅	docker_cws_instrumentation_amd64	7.142 MiB
✅	docker_cws_instrumentation_arm64	6.689 MiB
✅	docker_dogstatsd_amd64	39.215 MiB
✅	docker_dogstatsd_arm64	37.445 MiB
✅	dogstatsd_deb_amd64	29.855 MiB
✅	dogstatsd_deb_arm64	28.007 MiB
✅	dogstatsd_rpm_amd64	29.855 MiB
✅	dogstatsd_suse_amd64	29.855 MiB
✅	iot_agent_deb_amd64	43.218 MiB
✅	iot_agent_deb_arm64	40.273 MiB
✅	iot_agent_deb_armhf	41.017 MiB
✅	iot_agent_rpm_amd64	43.219 MiB
✅	iot_agent_suse_amd64	43.219 MiB

On-wire sizes (compressed)

	Quality gate	Change	Size (prev → curr → max)
✅	agent_deb_amd64	-46.05 KiB (0.03% reduction)	174.738 → 174.693 → 177.700
✅	agent_deb_amd64_fips	+9.67 KiB (0.01% increase)	165.240 → 165.250 → 172.230
✅	agent_heroku_amd64	-3.33 KiB (0.00% reduction)	74.944 → 74.941 → 79.970
✅	agent_msi	-44.0 KiB (0.03% reduction)	138.246 → 138.203 → 146.220
✅	agent_rpm_amd64	+26.34 KiB (0.01% increase)	177.584 → 177.609 → 180.780
✅	agent_rpm_amd64_fips	-53.47 KiB (0.03% reduction)	167.318 → 167.266 → 173.370
✅	agent_rpm_arm64	-22.0 KiB (0.01% reduction)	159.582 → 159.560 → 161.610
✅	agent_rpm_arm64_fips	neutral	151.269 MiB → 155.910
✅	agent_suse_amd64	+26.34 KiB (0.01% increase)	177.584 → 177.609 → 180.780
✅	agent_suse_amd64_fips	-53.47 KiB (0.03% reduction)	167.318 → 167.266 → 173.370
✅	agent_suse_arm64	-22.0 KiB (0.01% reduction)	159.582 → 159.560 → 161.610
✅	agent_suse_arm64_fips	neutral	151.269 MiB → 155.910
✅	docker_agent_amd64	-2.83 KiB (0.00% reduction)	267.883 → 267.880 → 271.240
✅	docker_agent_arm64	neutral	255.084 MiB → 259.800
✅	docker_agent_jmx_amd64	neutral	336.526 MiB → 339.870
✅	docker_agent_jmx_arm64	neutral	319.725 MiB → 324.390
✅	docker_cluster_agent_amd64	neutral	71.886 MiB → 72.920
✅	docker_cluster_agent_arm64	neutral	67.458 MiB → 68.220
✅	docker_cws_instrumentation_amd64	neutral	2.999 MiB → 3.330
✅	docker_cws_instrumentation_arm64	neutral	2.729 MiB → 3.090
✅	docker_dogstatsd_amd64	neutral	15.161 MiB → 15.820
✅	docker_dogstatsd_arm64	neutral	14.480 MiB → 14.830
✅	dogstatsd_deb_amd64	neutral	7.888 MiB → 8.790
✅	dogstatsd_deb_arm64	neutral	6.774 MiB → 7.710
✅	dogstatsd_rpm_amd64	neutral	7.898 MiB → 8.800
✅	dogstatsd_suse_amd64	neutral	7.898 MiB → 8.800
✅	iot_agent_deb_amd64	neutral	11.387 MiB → 12.040
✅	iot_agent_deb_arm64	neutral	9.693 MiB → 10.450
✅	iot_agent_deb_armhf	neutral	9.928 MiB → 10.620
✅	iot_agent_rpm_amd64	neutral	11.401 MiB → 12.060
✅	iot_agent_suse_amd64	neutral	11.401 MiB → 12.060

[cit-pr-commenter-54b7da]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 8c1e679d-8507-4d42-a94e-790fcbee6336

Baseline: 3fb6170
Comparison: 3e53e15
Diff

Optimization Goals: ✅ No significant changes detected

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	docker_containers_cpu	% cpu utilization	+4.43	[+1.30, +7.56]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	docker_containers_cpu	% cpu utilization	+4.43	[+1.30, +7.56]	1	Logs
➖	quality_gate_logs	% cpu utilization	+1.34	[-0.24, +2.92]	1	Logs bounds checks dashboard
➖	quality_gate_idle_all_features	memory utilization	+0.36	[+0.32, +0.40]	1	Logs bounds checks dashboard
➖	ddot_metrics_sum_cumulative	memory utilization	+0.31	[+0.16, +0.45]	1	Logs
➖	ddot_metrics	memory utilization	+0.22	[+0.05, +0.40]	1	Logs
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	+0.22	[+0.16, +0.28]	1	Logs
➖	file_tree	memory utilization	+0.21	[+0.16, +0.26]	1	Logs
➖	quality_gate_idle	memory utilization	+0.18	[+0.13, +0.23]	1	Logs bounds checks dashboard
➖	docker_containers_memory	memory utilization	+0.12	[+0.05, +0.19]	1	Logs
➖	ddot_metrics_sum_cumulativetodelta_exporter	memory utilization	+0.07	[-0.16, +0.29]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.06	[-0.38, +0.50]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.03	[-0.06, +0.12]	1	Logs
➖	uds_dogstatsd_to_api_v3	ingress throughput	+0.00	[-0.19, +0.19]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.20, +0.19]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.09, +0.09]	1	Logs
➖	ddot_metrics_sum_delta	memory utilization	-0.02	[-0.20, +0.15]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.06	[-0.52, +0.40]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.06	[-0.45, +0.33]	1	Logs
➖	quality_gate_metrics_logs	memory utilization	-0.27	[-0.50, -0.03]	1	Logs bounds checks dashboard
➖	ddot_logs	memory utilization	-0.27	[-0.33, -0.21]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.72	[-0.86, -0.58]	1	Logs
➖	otlp_ingest_metrics	memory utilization	-0.73	[-0.89, -0.57]	1	Logs
➖	otlp_ingest_logs	memory utilization	-1.44	[-1.54, -1.35]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	observed_value	links
✅	docker_containers_cpu	simple_check_run	10/10	651 ≥ 26
✅	docker_containers_memory	memory_usage	10/10	275.03MiB ≤ 370MiB
✅	docker_containers_memory	simple_check_run	10/10	679 ≥ 26
✅	file_to_blackhole_0ms_latency	memory_usage	10/10	0.19GiB ≤ 1.20GiB
✅	file_to_blackhole_0ms_latency	missed_bytes	10/10	0B = 0B
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10	0.23GiB ≤ 1.20GiB
✅	file_to_blackhole_1000ms_latency	missed_bytes	10/10	0B = 0B
✅	file_to_blackhole_100ms_latency	memory_usage	10/10	0.19GiB ≤ 1.20GiB
✅	file_to_blackhole_100ms_latency	missed_bytes	10/10	0B = 0B
✅	file_to_blackhole_500ms_latency	memory_usage	10/10	0.21GiB ≤ 1.20GiB
✅	file_to_blackhole_500ms_latency	missed_bytes	10/10	0B = 0B
✅	quality_gate_idle	intake_connections	10/10	3 = 3	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	174.62MiB ≤ 175MiB	bounds checks dashboard
✅	quality_gate_idle_all_features	intake_connections	10/10	2 ≤ 3	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	491.49MiB ≤ 550MiB	bounds checks dashboard
✅	quality_gate_logs	intake_connections	10/10	3 ≤ 6	bounds checks dashboard
✅	quality_gate_logs	memory_usage	10/10	202.17MiB ≤ 220MiB	bounds checks dashboard
✅	quality_gate_logs	missed_bytes	10/10	0B = 0B	bounds checks dashboard
✅	quality_gate_metrics_logs	cpu_usage	10/10	356.07 ≤ 2000	bounds checks dashboard
✅	quality_gate_metrics_logs	intake_connections	10/10	4 ≤ 6	bounds checks dashboard
✅	quality_gate_metrics_logs	memory_usage	10/10	400.36MiB ≤ 475MiB	bounds checks dashboard
✅	quality_gate_metrics_logs	missed_bytes	10/10	0B = 0B	bounds checks dashboard

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".

CI Pass/Fail Decision

✅ Passed. All Quality Gates passed.

• quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check missed_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check missed_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.

[dd-octo-sts]

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