Route agent crash detection through agent telemetry component

[iglendd]

What does this PR do?

• Extend Agent Telemetry not only sends registered and governed internal metrics on a configured cadence/schedule but also send registered and governed internal events (with registered event now Agent Crash as agentbsod).
• Replace how Agent Crash payloads (agentbsod events) are routed. Currently it has been using the APM Tracer general mechanism as a semi-structured log message. With this PR change, information will be sent as a structural payload via the newer Agent Telemetry mechanism.

Motivation

• Provide better Agent Telemetry governance and compliance for Agent Crash telemetry
• Using common Agent Telemetry mechanism
• Eliminate reliance on APM Tracer code

Describe how you validated your changes

Added following unit tests

• TestAgentTelemetryParseDefaultConfiguration
• TestAgentTelemetryEventConfiguration
• TestAgentTelemetrySendRegisteredEvent
• TestAgentTelemetrySendNonRegisteredEvent

Possible Drawbacks / Trade-offs

Additional Notes

[cit-pr-commenter]

Go Package Import Differences

Baseline: 0092d92
Comparison: f98bcf7

binary	os	arch	change
agent	windows	amd64	+0, -1 -github.com/DataDog/datadog-agent/pkg/internaltelemetry

[agent-platform-auto-pr]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv aws.create-vm --pipeline-id=52064516 --os-family=ubuntu

Note: This applies to commit f98bcf7

[agent-platform-auto-pr]

Uncompressed package size comparison

Comparison with ancestor 0092d9260d449c1e14199d0aad2d3bcc8dd00b85

Diff per package

package	diff	status	size	ancestor	threshold
datadog-agent-x86_64-rpm	0.01MB	⚠️	1207.25MB	1207.23MB	140.00MB
datadog-agent-x86_64-suse	0.01MB	⚠️	1207.25MB	1207.23MB	140.00MB
datadog-agent-amd64-deb	0.01MB	⚠️	1197.93MB	1197.92MB	140.00MB
datadog-heroku-agent-amd64-deb	0.01MB	⚠️	506.10MB	506.09MB	70.00MB
datadog-iot-agent-x86_64-rpm	0.01MB	⚠️	113.84MB	113.83MB	10.00MB
datadog-iot-agent-x86_64-suse	0.01MB	⚠️	113.84MB	113.83MB	10.00MB
datadog-iot-agent-amd64-deb	0.01MB	⚠️	113.77MB	113.76MB	10.00MB
datadog-agent-aarch64-rpm	0.01MB	⚠️	949.59MB	949.59MB	140.00MB
datadog-agent-arm64-deb	0.01MB	⚠️	940.30MB	940.29MB	140.00MB
datadog-iot-agent-aarch64-rpm	0.01MB	⚠️	109.29MB	109.28MB	10.00MB
datadog-iot-agent-arm64-deb	0.01MB	⚠️	109.22MB	109.21MB	10.00MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	79.00MB	79.00MB	10.00MB
datadog-dogstatsd-x86_64-rpm	0.00MB	✅	79.08MB	79.08MB	10.00MB
datadog-dogstatsd-x86_64-suse	0.00MB	✅	79.08MB	79.08MB	10.00MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	56.10MB	56.10MB	10.00MB

Decision

⚠️ Warning

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 1ca87359-abf0-4191-8820-9e6c4e22606e

Baseline: 0092d92
Comparison: f98bcf7
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	tcp_syslog_to_blackhole	ingress throughput	+1.56	[+1.49, +1.63]	1	Logs
➖	quality_gate_logs	% cpu utilization	+1.41	[-1.81, +4.62]	1	Logs
➖	file_tree	memory utilization	+0.38	[+0.26, +0.51]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.37	[-0.44, +1.18]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.18	[-0.29, +0.65]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.09	[-0.60, +0.78]	1	Logs
➖	quality_gate_idle	memory utilization	+0.06	[+0.03, +0.09]	1	Logs bounds checks dashboard
➖	file_to_blackhole_0ms_latency	egress throughput	+0.03	[-0.79, +0.85]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.02	[-0.06, +0.10]	1	Logs bounds checks dashboard
➖	file_to_blackhole_0ms_latency_http1	egress throughput	+0.01	[-0.81, +0.84]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.12, +0.14]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.02	[-0.80, +0.76]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.02	[-0.66, +0.62]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	-0.03	[-0.86, +0.79]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-2.02	[-2.69, -1.35]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http1	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http1	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http2	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http2	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency_linear_load	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	lost_bytes	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	lost_bytes	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	lost_bytes	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	lost_bytes	10/10
✅	quality_gate_logs	memory_usage	10/10

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 memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.