Add types to KMT tasks

[gjulianm]

What does this PR do?

Adds type information for the Python code in the tasks directory, only for KMT related tasks.

Motivation

Improve reliability of the code by adding a typing layer that can catch problems without needing to run the code, which is useful in this case as tests of the KMT tasks would be complex and computationally expensive.

Additional Notes

Enforcement of these types, either via pre-commit or CI checks, will come in a separate PR after discussion with the agent platform team.

Some parts of the code were moved to avoid import loops.

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: f0fd215d-74d8-4f03-a055-c296cac83f8f Baseline: dd8188170eb12b0964a29426cf42fc8bd9e9c69b Comparison: 26fbf879915de024da4e33c82324b39af753ee13

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

Experiments with missing or malformed data

• basic_py_check

Usually, this warning means that there is no usable optimization goal data for that experiment, which could be a result of misconfiguration.

No significant changes in experiment optimization goals

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

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Experiments ignored for regressions

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

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	+0.28	[-6.29, +6.86]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_tree	memory utilization	+0.41	[+0.34, +0.49]
➖	file_to_blackhole	% cpu utilization	+0.28	[-6.29, +6.86]
➖	process_agent_standard_check_with_stats	memory utilization	+0.28	[+0.25, +0.31]
➖	idle	memory utilization	+0.27	[+0.24, +0.30]
➖	trace_agent_msgpack	ingress throughput	+0.02	[+0.01, +0.04]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.00, +0.00]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.00, +0.00]
➖	trace_agent_json	ingress throughput	-0.03	[-0.06, +0.01]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.04	[-0.10, +0.02]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.10	[-1.53, +1.33]
➖	process_agent_real_time_mode	memory utilization	-0.12	[-0.16, -0.09]
➖	process_agent_standard_check	memory utilization	-0.29	[-0.32, -0.26]
➖	otel_to_otel_logs	ingress throughput	-1.04	[-1.66, -0.42]

Explanation

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".

[gjulianm]

/merge

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