Here is Cycle 1 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

🎯 Cycle 1 – Purpose and Strategic Role¶

📌 Core Mission¶

The Load & Performance Testing Agent is responsible for validating that ConnectSoft-generated services, APIs, event flows, and modules perform within defined SLOs (Service Level Objectives) under various types of stress, concurrency, and load conditions.

Its mission: ✅ Detect bottlenecks, 📉 Identify degradation, 🔁 Surface trends and regressions, 📊 And gate CI/CD pipelines when performance SLOs are breached.

🎯 Strategic Role in the ConnectSoft AI Software Factory¶

Function	Description
⚙️ CI Gatekeeper	Fails builds or microservice promotion if performance degrades across releases
📊 Performance Auditor	Provides structured, edition-aware load test metrics
📈 Trend Monitor	Tracks latency, throughput, and memory/cpu usage over time for services
🔍 Bottleneck Analyzer	Uses test correlation + telemetry to pinpoint slowest operations
🤖 Feedback Loop Agent	Feeds metrics into Studio, Knowledge Management, and optimization agents
🧪 Stress Designer	Designs synthetic spike/soak/stress test plans per service or scenario
🧠 Memory-Aware Validator	Compares performance to historical baselines from memory or previous versions

📘 Example Agent Outputs¶

Situation	Output
Service A’s `/checkout` endpoint response time degrades by +35%	`perf-metrics.json` flagged `status: fail`, attached to Studio tile
EventBus queue saturation under spike load	Resiliency Agent notified → Recovery strategies suggested
NotificationService fails to scale beyond 200 RPS in Soak test	`performanceScore: 0.42`, test type = `soak`, status = `degraded`
Edition-specific latency regression in `vetclinic-premium`	Doc generated with edition SLOs breached → flagged as `needs-tuning`

🧠 Example Agent Class / Cluster¶

agentCluster: QA
agentType: LoadPerformanceTestingAgent
agentId: QA.LoadTestAgent
executionClass: validator
traceCompatible: true

🤝 Where This Agent Fits in the Platform¶

flowchart TD
    GEN[MicroserviceGeneratorAgent]
    TEST[TestGeneratorAgent]
    QA[QAEngineerAgent]
    LOAD[🧪 Load & Performance Testing Agent]
    STUDIO[📊 Studio Agent]
    RES[ResiliencyAgent]
    KM[🧠 Knowledge Management Agent]

    GEN --> LOAD
    TEST --> LOAD
    LOAD --> STUDIO
    LOAD --> KM
    LOAD --> RES

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🧾 Example Studio Tile Summary¶

{
  "traceId": "proj-921-checkout",
  "editionId": "vetclinic",
  "moduleId": "CheckoutService",
  "loadTestResult": {
    "performanceScore": 0.68,
    "status": "needs-optimization",
    "spikeLatencyMs": 812,
    "baselineLatencyMs": 450
  }
}

✅ Summary¶

The Load & Performance Testing Agent:

🧪 Defines and executes load, spike, soak, and concurrency tests
📉 Flags services that regress in performance
🧠 Compares current results to historical performance from memory
📊 Feeds dashboards, gates pipelines, and integrates with Studio
🔁 Forms part of the QA, Resiliency, and Observability cluster

This agent is essential for ensuring that ConnectSoft-generated SaaS services scale and perform reliably — before, during, and after deployment.

Shall we proceed to Cycle 2 – Responsibilities?

Here is Cycle 2 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

📋 Cycle 2 – Responsibilities¶

This cycle defines the core functional responsibilities of the Load & Performance Testing Agent — the measurable tasks and outputs it is expected to perform as part of ConnectSoft’s QA and Observability clusters.

✅ Primary Responsibilities¶

Responsibility	Description
🧪 Design and execute performance tests	Selects appropriate test types (load, stress, spike, soak) for APIs, queues, workflows, or async services
⚙️ Run benchmark suites per module	Applies custom or template-based load profiles to each microservice, queue, or composite flow
📈 Capture key metrics	Collects latency (P50, P95), RPS (requests/sec), error rates, saturation levels, GC activity, memory/CPU profiles
📉 Compare against historical baselines	Uses memory snapshots, edition overlays, and prior `perf-metrics.json` to detect regression
✅ Classify result status	Assigns `pass`, `warning`, `fail`, or `needs-optimization` per test
🧠 Emit performance scores	Calculates normalized `performanceScore` between 0–1 based on thresholds and trends
🧾 Publish structured artifacts	Emits `perf-metrics.json`, `load-trace-map.yaml`, optional `flamegraph.svg` or telemetry logs
🧩 Feed Studio dashboards	Updates performance tiles, regression charts, and service quality indicators
🔁 Coordinate retries or test fixes	Suggests reduced load tests or focused re-runs in case of infrastructure flakiness or false negatives
📚 Store results in memory	Stores test type, result, confidence, and resource profile for future trend comparison and optimizations

🔬 Supported Test Categories¶

Test Type	Description
Load	Steady traffic increase to target TPS/RPS, until saturation
Spike	Sudden burst of traffic to test burst handling or autoscaling
Soak	Long-duration steady load (e.g. 1 hour) to test for degradation
Stress	Overload system with traffic beyond design limits to find failure mode
Concurrency	Simulates concurrent sessions, open connections, overlapping workflows
Latency Focus	Measures response time under variable payloads or path conditions
Edition-Aware Comparison	Run same test across multiple editions or tenants to find scope-specific regressions

🧩 Specialized Responsibilities (Optional)¶

Task	Description
📦 Package test artifacts	Export test suite in self-running container or test runner
🔁 Trigger from regression event	Re-run soak test if flakiness detected by QA or Studio
🧪 Performance integration validation	Run performance tests on full business workflows (checkout, onboarding, sync)
💬 Suggest infrastructure tuning	Flag configs like thread pool size, connection reuse, cache TTL for DevOps suggestions

🧠 Documentation Output (Structured Summary)¶

Each test responsibility results in:

📄 Structured logs
📊 Scored metrics
🔁 Status summary
📁 Files that can be linked from dashboards, traces, or memory

✅ Summary¶

The Load & Performance Testing Agent is responsible for:

Designing and executing multiple types of load tests
Generating structured output metrics
Validating results against edition-aware thresholds
Alerting regressions or bottlenecks
Updating memory and Studio systems for observability

This makes it a core quality assurance and resilience contributor to the ConnectSoft AI Software Factory.

Shall we proceed to Cycle 3 – Inputs Consumed?

Here is Cycle 3 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

📥 Cycle 3 – Inputs Consumed¶

This cycle defines the input artifacts, metadata, and telemetry that the Load & Performance Testing Agent consumes to generate, execute, and evaluate performance tests within the ConnectSoft AI Software Factory.

📂 Input Artifacts¶

Input	Description
`service.metadata.yaml`	Describes microservice endpoints, event queues, resource contracts, infrastructure profile
`generated.api.yaml`	OpenAPI/AsyncAPI spec describing REST/gRPC/event interfaces to test
`test-suite.plan.yaml`	Defines which tests (load/spike/soak) to execute per service/endpoint
`trace.plan.yaml`	Provides business feature context, flow groupings, and execution trace metadata
`edition.config.json`	Maps test coverage by `editionId`, including expected SLOs and traffic models
`perf-thresholds.yaml`	Thresholds (e.g., latency max, RPS minimum, failure rate ceiling) used for pass/fail classification
`perf-baseline.memory.json`	Prior `perf-metrics.json` from memory – used for trend diffing and regression detection
`studio.annotation.yaml`	Optional flags from Studio (e.g., “run soak test on checkout-service” or “ignore CPU deviation”)
`observability.config.yaml`	Defines how to capture spans, logs, and metrics from underlying systems

📘 Example: `test-suite.plan.yaml`¶

traceId: proj-933-checkout
moduleId: CheckoutService
editionId: vetclinic
tests:
  - type: load
    endpoint: /checkout/submit
    rps: 100
    duration: 5m
  - type: spike
    rps: 500
    duration: 1m

📘 Example: `perf-thresholds.yaml`¶

module: CheckoutService
editionId: vetclinic
thresholds:
  latencyMs:
    p95: 500
    p99: 750
  rpsMin: 80
  errorRateMax: 0.01
  cpuUsageMax: 75

📘 Example: `perf-baseline.memory.json`¶

{
  "traceId": "proj-899",
  "endpoint": "/checkout/submit",
  "testType": "load",
  "editionId": "vetclinic",
  "p95LatencyMs": 420,
  "rps": 110,
  "performanceScore": 0.91,
  "status": "pass"
}

🔄 Dynamic Context Inputs (injected via `SKContext` or memory overlay)¶

Context Variable	Description
`traceId`, `editionId`, `moduleId`	Contextualizes current performance test scope
`testType`	Tells agent what kind of load test to generate or re-run
`sourceAgent`	Refers to prior agent (e.g., MicroserviceGeneratorAgent or QA Agent) that triggered this test
`executionWindow`	Allows soak or stress tests to span hours if needed
`memoryCompare`	Boolean to indicate whether regression comparison should be done automatically

📎 Supporting Artifacts Consumed (Optional)¶

File	Use
`.feature` test file	Used to validate performance of test-executed flow
`runtime-profile.yaml`	Maps expected memory/CPU/network load under baseline execution
`event-trace.json`	Correlates async message flow durations and queue saturation levels

✅ Summary¶

The Load & Performance Testing Agent consumes:

📄 Structured service and test plans
📊 Historical performance memory entries
⚙️ Threshold configurations per edition/module
🔗 Contextual trace metadata
🧠 Observability and execution traces

These inputs enable it to generate precise test plans, evaluate regressions, and feed performance insights into Studio and CI pipelines.

Shall we continue with Cycle 4 – Outputs Produced?

Here is Cycle 4 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

📤 Cycle 4 – Outputs Produced¶

This cycle defines the structured outputs generated by the Load & Performance Testing Agent. These outputs inform regression detection, performance dashboards, memory enrichment, and Studio visualizations — and serve as quality gates in CI/CD pipelines.

📦 Primary Output Artifacts¶

File	Format	Description
`perf-metrics.json`	JSON	Core output file with detailed metrics, status, score, and test context
`load-trace-map.yaml`	YAML	Maps endpoints/events tested to latency/RPS/error metrics, traceable by `traceId`
`performance-score.log.jsonl`	JSON Lines	Line-by-line logging of score evolution, retries, and thresholds applied
`studio.performance.preview.json`	JSON	Summary for Studio dashboard showing status, performance score, spike behavior
`perf-flamegraph.svg` (optional)	SVG	Flamegraph from performance profiling tool (CPU, latency trees, blocking paths)
`regression-alert.yaml` (optional)	YAML	Emitted only on failure or significant degradation, for human review or notification agent

📘 Example: `perf-metrics.json`¶

{
  "traceId": "proj-933-checkout",
  "moduleId": "CheckoutService",
  "editionId": "vetclinic",
  "testType": "spike",
  "performanceScore": 0.58,
  "status": "degraded",
  "rps": 95,
  "latency": {
    "p50": 320,
    "p95": 810,
    "p99": 1200
  },
  "errorRate": 0.025,
  "cpuUsagePct": 74.2,
  "baselineComparison": {
    "regressed": true,
    "deltaLatencyP95": "+35%",
    "confidence": 0.92
  }
}

📘 Example: `load-trace-map.yaml`¶

traceId: proj-933-checkout
editionId: vetclinic
moduleId: CheckoutService
tests:
  - endpoint: /checkout/submit
    testType: spike
    result: degraded
    p95LatencyMs: 810
    errorRate: 0.025
    rps: 95

📘 Example: `studio.performance.preview.json`¶

{
  "traceId": "proj-933-checkout",
  "status": "degraded",
  "performanceScore": 0.58,
  "testType": "spike",
  "tags": ["CheckoutService", "vetclinic", "spike"],
  "regression": true,
  "tileSummary": "Spike test: p95 latency ↑35% vs. baseline. Performance degraded."
}

📘 Optional: `regression-alert.yaml`¶

triggeredBy: performance-regression
reason: "Latency exceeded edition threshold and regressed vs. memory baseline"
traceId: proj-933-checkout
editionId: vetclinic
performanceScore: 0.58
actionRequired: true
suggestions:
  - Rerun with reduced RPS
  - Notify Resiliency Agent
  - Review service timeout settings

📊 Metrics in `perf-metrics.json`¶

Metric	Description
`performanceScore`	Composite score [0–1] based on latency, error rate, RPS, CPU, memory
`status`	One of: `pass`, `warning`, `degraded`, `fail`
`latency.p95` / `.p99`	Key latency thresholds for trace and contract validation
`errorRate`	Total % of failed requests during run
`rps`	Achieved requests/sec at target load
`baselineComparison`	Summary of difference vs. last known good state

🧠 Memory Integration¶

perf-metrics.json and load-trace-map.yaml are ingested into long-term memory as vector-enhanced entries
Linked by traceId, editionId, moduleId, and testType

✅ Summary¶

The Load & Performance Testing Agent produces:

📊 Structured JSON/YAML metrics for regression evaluation
📎 Preview and tile metadata for Studio dashboards
🧠 Memory-aware artifacts used by trend analysis and knowledge agents
🔁 Alert triggers and performance score logs for retry/correction workflows

These outputs provide clear, traceable performance insights for CI/CD gates, dashboards, and continuous optimization.

Shall we continue with Cycle 5 – Execution Flow?

Here is Cycle 5 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

🔄 Cycle 5 – Execution Flow¶

This cycle outlines the end-to-end execution flow for the Load & Performance Testing Agent, from initialization to scoring and emission of results. It ensures a consistent, observable, and retry-capable lifecycle for each performance test run.

📊 High-Level Execution Flow¶

flowchart TD
    INIT[Start: Load Test Triggered]
    PARSE[Parse Input Artifacts]
    PLAN[Select Test Type + Parameters]
    PREP[Prepare Infrastructure + Targets]
    EXEC[Run Performance Test]
    OBS[Capture Metrics + Telemetry]
    COMP[Compare to Thresholds & Memory]
    SCORE[Calculate Performance Score]
    CLASS[Classify Status]
    EMIT[Emit Results + Artifacts]
    STORE[Push to Memory + Studio]

    INIT --> PARSE --> PLAN --> PREP --> EXEC --> OBS --> COMP --> SCORE --> CLASS --> EMIT --> STORE

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🧩 Detailed Step-by-Step Execution¶

1. Trigger & Initialization¶

Triggered by:
CI pipeline
Test plan
Studio annotation
Regression detection event
Loads:
traceId, editionId, moduleId
testType (e.g., spike, soak)

2. Parse Input Artifacts¶

Inputs parsed:
service.metadata.yaml
generated.api.yaml
perf-thresholds.yaml
Memory baseline from perf-metrics.json

3. Test Planning¶

Selects appropriate tool and runner (e.g., k6, Locust, JMeter)
Configures RPS, duration, concurrency, payload sizes
Loads or generates synthetic data if needed

4. Prepare Environment¶

Provisions isolated test environment if required
Verifies service health and telemetry hooks are connected
Clears queues or caches to reset state for cold/warm scenarios

5. Execute Performance Test¶

Runs selected test type for defined duration
Captures raw metrics:
RPS, latency (p50/p95/p99), error rates
System metrics: CPU, memory, I/O
Correlates traces if async/event-based test

6. Observe + Capture Telemetry¶

Extracts:
Span-level latency traces
App Insights metrics (if integrated)
System resource profile snapshots

7. Compare to Thresholds + Memory¶

Matches results to:
perf-thresholds.yaml
Memory baseline (last good state for edition/module/testType)
Annotates deltas (e.g., +32% p95 latency)

8. Score Generation¶

Computes performanceScore using weighted metrics
Records regression deltas and historical trends

9. Status Classification¶

Classifies result as:
✅ pass
⚠️ warning
📉 degraded
❌ fail
Flags test for retry or escalation if thresholds breached

10. Emit Artifacts¶

Writes:
perf-metrics.json
load-trace-map.yaml
Optional flamegraph.svg, regression-alert.yaml
Pushes Studio preview

11. Store + Publish¶

Pushes result to:
Memory store (baseline update)
Studio dashboard tile
QA history for edition/module/service

✅ Summary¶

The Load & Performance Testing Agent follows a robust execution flow that:

🔁 Ingests trace + service metadata
🧪 Executes targeted performance tests
📊 Captures and compares metrics
📎 Classifies and publishes results
🧠 Feeds memory, Studio, and regression workflows

This ensures every performance test is repeatable, observable, and edition-aware within ConnectSoft’s QA infrastructure.

Shall we proceed to Cycle 6 – Skills and Kernel Functions Used?

Here is Cycle 6 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

🧠 Cycle 6 – Skills and Kernel Functions Used¶

This cycle outlines the Semantic Kernel skills, planners, and runtime functions used by the Load & Performance Testing Agent. These allow it to dynamically select test types, build runners, capture metrics, compute scores, and communicate with Studio and memory layers.

🧠 Core Skills¶

Skill	Description
`TestPlanInterpreterSkill`	Parses `test-suite.plan.yaml`, `perf-thresholds.yaml`, and OpenAPI specs to build execution plans
`LoadRunnerExecutorSkill`	Executes load tests using external tools (e.g., k6, Locust, JMeter) via adapter or process bridge
`PerfMetricCollectorSkill`	Aggregates raw telemetry, logs, and system metrics
`PerformanceScorerSkill`	Calculates `performanceScore` from latency, throughput, and baseline deltas
`ThresholdEvaluatorSkill`	Classifies result as `pass`, `warning`, `degraded`, or `fail` based on thresholds and memory
`RegressionComparerSkill`	Compares current run vs. memory baseline to detect regressions
`PreviewPublisherSkill`	Generates `studio.performance.preview.json` with summary, trace, and tags
`MemoryPusherSkill`	Saves validated test results back into `perf-metrics.memory.json` for future use

🔁 Skill Orchestration (Execution Chain)¶

flowchart TD
    A[TestPlanInterpreterSkill]
    B[LoadRunnerExecutorSkill]
    C[PerfMetricCollectorSkill]
    D[RegressionComparerSkill]
    E[PerformanceScorerSkill]
    F[ThresholdEvaluatorSkill]
    G[PreviewPublisherSkill]
    H[MemoryPusherSkill]

    A --> B --> C --> D --> E --> F --> G --> H

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📦 Supporting Plugins / Connectors¶

Plugin	Purpose
`ProcessBridgePlugin`	To launch system-native load tools like `k6`, `JMeter`, `Locust`
`MetricsAdapterPlugin`	Converts Prometheus, App Insights, or OpenTelemetry metrics into SK-readable metrics format
`TimeSeriesReaderSkill`	Optional — pulls recent runs for comparative analysis in trend or spike tests
`ArtifactEmitterSkill`	Generates and saves: `.json`, `.yaml`, `.svg`, `.log.jsonl` files

🧠 Context Variables in SK Execution¶

Variable	Description
`traceId`	All tests are trace-scoped for memory and preview
`testType`	Injected into each skill to determine spike/load/stress handling
`editionId`	Ensures edition-aware thresholds are respected
`moduleId`	Links results to the right microservice or test scope
`previousScore`	Used to calculate delta-based regression warning or success
`retryAttempt`	Used in fallback retry skill chain (e.g. reduced RPS if first test failed)

📘 Example Skill Call (from YAML or SK planner)¶

- skill: LoadRunnerExecutorSkill
  input:
    endpoint: /checkout/submit
    duration: 5m
    rps: 200
    testType: spike

📎 Reused by Other Agents¶

Agent	Uses
Resiliency Agent	Reuses `PerformanceScorerSkill` and `ThresholdEvaluatorSkill` for chaos-injected flows
QA Engineer Agent	Pulls `PerfMetricCollectorSkill` to check test flow stability
Studio Agent	Calls `PreviewPublisherSkill` to render tiles
Knowledge Management Agent	Uses `MemoryPusherSkill` to persist knowledge of historical performance metrics

✅ Summary¶

The Load & Performance Testing Agent uses a modular set of Semantic Kernel skills that:

📄 Parse and interpret test plans
🏃 Execute dynamic load test runs
📊 Collect and score metrics
🧠 Compare against thresholds and memory
📤 Emit previews, logs, and trace-linked outputs

This makes the agent extensible, skill-driven, and tightly integrated with ConnectSoft’s AI agent ecosystem.

Shall we continue to Cycle 7 – Test Types & Metrics Captured?

Here is Cycle 7 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

🧪 Cycle 7 – Test Types & Metrics Captured¶

This cycle defines the types of performance tests supported by the agent and the metrics it captures during each test type. These tests are configurable, edition-aware, and traceable — designed to validate real-world system resilience, responsiveness, and scalability.

🧪 Supported Test Types¶

Test Type	Description	Use Case
Load Test	Gradual increase to a target request/second (RPS) to test sustained behavior	Steady-state scaling
Spike Test	Sudden burst of traffic (e.g., 0 → 500 RPS in 1s) to test burst capacity and autoscaling	Frontend bursts, async triggers
Soak Test	Low-to-medium steady load over long duration (e.g., 1–2 hrs)	Detects memory leaks, GC churn, degradation
Stress Test	Pushes system beyond limits to observe failure handling	Chaos agent coordination or SLO envelope validation
Concurrency Test	Simulates multiple users/sessions running simultaneously	API thread handling, auth bottlenecks
Latency Profiling	Measures response time for varying payload sizes	Test request mapping, queue response, DB latency
Composite Flow Test	Simulates end-to-end workflows across services	e.g., Book Appointment → Notify → Sync CRM

📊 Metrics Captured (Per Test Run)¶

⚙️ System-Level Metrics¶

Metric	Description
`cpuUsagePct`	Peak and average CPU usage during test window
`memoryUsageMb`	Working set and heap memory usage
`gcActivityCount`	Number of GC cycles triggered (esp. for .NET agents)
`networkUsageKb`	Bandwidth, packet drops, retransmissions (optional)

📞 Request Metrics¶

Metric	Description
`rps`	Requests per second (achieved vs. target)
`latencyP50`, `latencyP95`, `latencyP99`	Response time percentiles
`errorRate`	Proportion of failed requests (5xx, 4xx, timeouts)
`throughputBytes`	Total data sent/received per request
`retryCount`	How many retry attempts occurred internally (e.g., gRPC or SDK retries)

🧠 Memory/Trend Comparison Metrics¶

Metric	Description
`deltaLatencyP95`	Change in latency compared to memory baseline
`regressionScore`	Ratio of current performance vs. historical high-performance state
`confidenceScore`	Scored comparison quality (was baseline match clean?)
`editionDeviation`	Cross-edition anomaly detection (e.g., `vetclinic-premium` slower than `base`)

📘 Example Output (from `perf-metrics.json`)¶

{
  "testType": "spike",
  "rps": 500,
  "latency": {
    "p50": 210,
    "p95": 920,
    "p99": 1400
  },
  "errorRate": 0.03,
  "cpuUsagePct": 82.4,
  "memoryUsageMb": 648,
  "baselineComparison": {
    "deltaLatencyP95": "+31%",
    "regressed": true
  }
}

🧪 Additional Test Metadata (captured or inferred)¶

Field	Description
`testDuration`	Total run time of the test
`testStartTime`	UTC start timestamp
`testTarget`	`/api/checkout/submit` or `queue/NotifyEmail`
`editionId`	Which edition/tenant the test was scoped for
`traceId`	Used to link results to business flow and Studio tiles

✅ Summary¶

The Load & Performance Testing Agent supports:

🔬 Multiple test types — load, soak, spike, stress, latency
📊 Captures critical service and system metrics
🧠 Performs edition-aware comparisons and regression detection
🔗 Links results to business flows, memory baselines, and Studio dashboards

This gives ConnectSoft teams complete performance visibility across services, editions, and workloads.

Shall we continue with Cycle 8 – Validation Thresholds?

Here is Cycle 8 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

✅ Cycle 8 – Validation Thresholds¶

This cycle defines how the agent uses predefined or memory-derived thresholds to evaluate whether a performance test passes, fails, or is degraded. These thresholds are edition-aware, configurable, and test-type specific — enabling precise SLO enforcement across microservices and environments.

📏 Threshold Sources¶

Source	Description
`perf-thresholds.yaml`	Primary configuration file scoped by `moduleId`, `editionId`, and `testType`
Memory Baseline	Pulled from past successful `perf-metrics.json` for the same edition/module/endpoint
Studio Annotation	Allows overrides or temporary relaxations during exploratory or regression testing
Default Policy	Fallback thresholds used if no explicit configuration exists (e.g., max errorRate = 1%, latency p95 < 800ms)

📘 Example: `perf-thresholds.yaml`¶

module: CheckoutService
editionId: vetclinic-premium
defaults:
  testType: load
thresholds:
  latencyMs:
    p50: 300
    p95: 600
    p99: 900
  rpsMin: 100
  errorRateMax: 0.01
  cpuUsageMax: 75
  memoryUsageMax: 768

✅ Validation Rules by Metric¶

Metric	Rule
`latency.p95`	Must be ≤ configured or historical baseline + allowable delta
`errorRate`	Must be ≤ maxErrorRate (default: 0.01)
`rps`	Must achieve minimum requests/second as defined
`cpuUsagePct`	Must not exceed `cpuUsageMax` (platform-specific)
`deltaLatencyP95`	Degradation > 20% may trigger `degraded` or `fail` status
`baselineDeviation`	If historical memory comparison exists, score must not fall below 0.8x of past best

🚦 Classification Logic¶

Condition	Result
All thresholds met or better	✅ `pass`
Minor deviations (e.g., 10–20% latency increase)	⚠️ `warning`
Significant metric violation (e.g., error rate > 2× threshold)	📉 `degraded`
Multiple threshold violations, large regression	❌ `fail`

🧠 Memory-Based Comparison Example¶

"baselineComparison": {
  "p95LatencyBaseline": 560,
  "p95Current": 720,
  "deltaLatencyP95": "+28%",
  "status": "degraded"
}

🔁 Retry Conditions Triggered by Thresholds¶

Trigger	Retry Behavior
Spike test fails by <15% margin	Retry with reduced RPS or longer warmup
CPU exceeds limit during load test	Retry after cache warmup or different GC mode (if configurable)
Test flakiness across editions	Retry only on affected edition with tighter tracing/logging enabled

📊 Studio and CI Feedback¶

Preview tiles color-coded: ✅ Green, ⚠️ Yellow, ❌ Red
Test status and threshold delta reported in studio.performance.preview.json
CI may be gated on status: pass, or performanceScore ≥ 0.85

✅ Summary¶

The agent uses edition-specific thresholds, memory baselines, and flexible policies to:

✅ Classify test results consistently
📉 Detect regressions early
🔁 Suggest retries or remediations
📊 Feed CI gates and Studio visualizations with deterministic status

This ensures that ConnectSoft services meet performance SLOs reliably and repeatably across environments and editions.

Shall we continue with Cycle 9 – CI/CD Integration?

Here is Cycle 9 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

🚀 Cycle 9 – CI/CD Integration¶

This cycle defines how the Load & Performance Testing Agent integrates with ConnectSoft’s CI/CD pipelines, enabling automated enforcement of performance SLOs during build, release, and deployment workflows.

🔗 Integration Points in CI/CD¶

Stage	Agent Behavior
✅ Post-Build	Agent runs after microservice/image is built and deployed to a test or ephemeral environment
🔁 Test/QA Stage	Executes load, spike, or concurrency tests using generated artifacts
⚖️ Validation/Gating	Evaluates `perf-metrics.json`, classifies test, and controls promotion to staging/prod
📤 Publishing	Emits artifacts to `docs/`, `artifacts/`, or `Studio preview` output folders
📊 Telemetry Upload	Optionally pushes metrics and logs to Azure Monitor or custom dashboard pipelines

🧪 Sample Azure DevOps YAML Step¶

- task: ConnectSoft.RunLoadTests@1
  inputs:
    traceId: $(Build.BuildId)
    moduleId: CheckoutService
    editionId: vetclinic
    testSuitePath: tests/performance/test-suite.plan.yaml
    thresholdsPath: tests/performance/perf-thresholds.yaml
    failOnDegraded: true

✅ CI Validation Logic¶

Input File	Expected Outcome
`perf-metrics.json`	Must be emitted with `status: pass` or `warning`
`performanceScore`	Must exceed configured minimum (e.g., 0.85)
`load-trace-map.yaml`	Used to generate trace-linked test coverage map
`studio.performance.preview.json`	Attached to build as preview summary
`doc-validation.log.jsonl`	Captured in artifact drop for debugging failures

🚦 Gating Strategy¶

Configuration	Behavior
`failOnDegraded: true`	CI fails if `status: degraded` or `fail` is returned
`warnOnRegression: true`	Does not block build but logs warning with delta metrics
`editionOverride: true`	Runs test on multiple editions and aggregates result
`retryOnFlakiness: true`	Automatically re-runs failed load test once with adjusted RPS or duration

📎 Artifacts Published to CI¶

File	Description
`perf-metrics.json`	Core metrics and score result
`studio.performance.preview.json`	Attached to Studio dashboards post-build
`load-trace-map.yaml`	Trace-linked load results per endpoint or event
`regression-alert.yaml` (if emitted)	Flags failing service for action
`flamegraph.svg` (optional)	Visual performance report uploaded to build summary

📘 Build Badge Example¶

Metric	Badge
`performanceScore ≥ 0.9`	Hold "Alt" / "Option" to enable pan & zoom
`0.75 ≤ score < 0.9`	Hold "Alt" / "Option" to enable pan & zoom
`score < 0.75`	Hold "Alt" / "Option" to enable pan & zoom

🧠 Memory Updates After CI Completion¶

If test status: pass, perf-metrics.json is persisted in long-term memory as new baseline
If status: degraded, regression-alert.yaml is emitted for review
Edition-specific trends tracked across builds in doc-coverage.metrics.json or studio.analytics.json

✅ Summary¶

The Load & Performance Testing Agent integrates deeply into CI/CD by:

🧪 Automatically executing and validating load tests per edition/module
📊 Publishing metrics, scores, and Studio previews
🚦 Enforcing performance gates for build promotion
🔁 Retrying and recovering from flakiness or deviation
🧠 Feeding long-term memory for baseline improvement

This ensures that ConnectSoft's SaaS factory ships scalable, performant software by default.

Shall we continue with Cycle 10 – Observability Integration?

Here is Cycle 10 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

📈 Cycle 10 – Observability Integration¶

This cycle details how the agent interfaces with ConnectSoft's observability stack to collect, correlate, and report telemetry and performance insights across the system. It bridges performance tests with production-like traces, logs, and metrics to offer deep visibility.

🔍 Observability Sources¶

Source	Captured Signals
OpenTelemetry Spans	Response latency, async duration, trace paths
Application Insights	Requests, exceptions, custom metrics (CPU, GC, throughput)
Prometheus (optional)	RPS, error rate, resource utilization, HTTP/gRPC metrics
Event Hubs / Queues	Queue depth, message delay, delivery lag
System Metrics (Host OS)	CPU %, memory (working set), GC frequency, disk I/O latency

🔗 Correlated Fields¶

Field	Used for...
`traceId`	Ties spans, logs, metrics, and test results to the originating test
`moduleId`	Filters telemetry by tested microservice
`testType`	Classifies telemetry context for load/spike/soak flows
`editionId`	Enables edition-scoped metric visualization and deviation detection

📊 Metrics Sent to Observability Dashboards¶

Metric	Aggregation
`rps`, `latency.p95`, `errorRate`	Per test type and per service
`cpuUsagePct`, `memoryUsageMb`	During test window
`spanDurationMs`, `queueLagMs`	From trace export
`performanceScore`	Saved per test run; visible in Studio & Grafana dashboards
`regressionDelta`	Reported if baseline comparison triggered deviation alert

📘 Telemetry Pipeline Flow¶

flowchart TD
    LOAD[Load Test Execution]
    METRICS[PerfMetricCollectorSkill]
    OTel[OpenTelemetry Exporter]
    AI[Application Insights]
    PROM[Prometheus (optional)]
    STUDIO[Studio Dashboards]
    MEMORY[Perf Baseline Store]

    LOAD --> METRICS
    METRICS --> OTel --> AI
    METRICS --> PROM
    METRICS --> STUDIO
    METRICS --> MEMORY

Hold "Alt" / "Option" to enable pan & zoom

📂 Logs & Visuals¶

Type	Purpose
`flamegraph.svg`	Visual call graph (CPU or span time) for bottleneck discovery
`trace-summary.json`	Trace span summary with start/stop, nesting, and error attribution
`doc-coverage.metrics.json`	Updated with latency and score trends per module/edition
`studio.performance.preview.json`	Includes score, regression status, and delta summaries for humans and agents

📘 Example: `trace-summary.json`¶

{
  "traceId": "proj-888-checkout",
  "spanCount": 7,
  "longestSpan": "SendConfirmationEmail",
  "durationMs": 1170,
  "spanDeltaVsBaseline": "+25%",
  "bottleneckDetected": true
}

📎 Optional Alerting Rules (on dashboards or in CI)¶

Trigger	Action
p95 latency increases >30% vs. baseline	Mark as `degraded`, alert Resiliency Agent
CPU exceeds 80% for >10s	Suggest retry with warmed cache
Trace path has new bottleneck span	Emit `regression-alert.yaml` for Studio + developer review

✅ Summary¶

The Load & Performance Testing Agent:

🔗 Correlates performance test results with real observability signals
📊 Publishes detailed metrics to Application Insights, dashboards, and Studio
🧠 Tracks regressions using span/metric comparison and memory overlays
🔁 Enables agents and humans to trace, visualize, and fix bottlenecks faster

This provides complete end-to-end traceability from synthetic load → real metrics → actionable feedback.

Shall we continue with Cycle 11 – Failure Scenarios & Regression Triggers?

Here is Cycle 11 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

❌ Cycle 11 – Failure Scenarios & Regression Triggers¶

This cycle outlines the specific failure modes, regression signals, and triggering conditions under which the agent classifies a test result as degraded or fail. It ensures that SLO violations, service bottlenecks, or performance drops are automatically identified, reported, and optionally retried or escalated.

❌ Primary Failure Conditions¶

Condition	Trigger
📉 `performanceScore < 0.75`	Computed from latency, error rate, throughput, and baseline deviation
⚠️ Threshold breach	p95 latency > configured or baseline limit
🔁 RPS below minimum	Achieved RPS < `rpsMin` for current test type and edition
🚨 High error rate	Error rate > `errorRateMax` (typically >1%)
🔥 Resource exhaustion	CPU > 90% sustained or memory usage exceeds allocation
🕸️ Span-level anomaly	New slowest span, blocking queue detected in trace
📉 Historical regression	>25% degradation vs. memory baseline (e.g., latency delta)
❌ Exception spike	Exceptions increase by >2× vs. average for the flow/module during test window

📉 Regression Detection Triggers¶

Type	Description
`deltaLatencyP95 > 25%`	Compared against last successful run for same `traceId` + `editionId`
`performanceScore drops by > 0.15`	Indicates significant quality degradation since previous build
`test previously passed` but now fails	Triggers `regression-alert.yaml` with cause summary
`studio.performance.preview.status` downgrades	(e.g., `pass` → `degraded`) triggers alert and dashboard update

📘 Example: `regression-alert.yaml`¶

traceId: proj-888-checkout
testType: spike
trigger: "Latency p95 regressed 31% from baseline"
status: degraded
editionId: vetclinic
moduleId: CheckoutService
suggestedActions:
  - Analyze flamegraph or span trace
  - Retry with reduced load
  - Notify ResiliencyAgent or DeveloperAgent

📊 Scoring-Based Failure Signals¶

Score Range	Classification
`≥ 0.90`	✅ `pass`
`0.75 – 0.89`	⚠️ `warning`
`0.50 – 0.74`	📉 `degraded`
`< 0.50`	❌ `fail`

🧠 Memory and Trend Flags¶

Behavior	Trigger
Mark baseline as obsolete	If 3 consecutive regressions are seen on same test+edition+module
Suggest flamegraph generation	If regression is span-based and not CPU-induced
Alert Knowledge Management Agent	If recurring regression pattern matches prior trace cluster
Suggest configuration hint	If GC frequency, thread starvation, or heap bloat is inferred from resource profiles

🚦 Studio Dashboard Output on Failure¶

Field	Behavior
`status`	Set to `degraded` or `fail`
`tileColor`	Turns red (fail) or yellow (degraded)
`regression`	Set to `true`
`tileSummary`	Explains delta: “p95 latency ↑ +31% vs. baseline. CPU sustained at 88%.”
`actions`	Include retry, flamegraph view, memory trace overlay comparison

✅ Summary¶

The agent classifies failure when:

📉 Thresholds or performance score fall below accepted levels
🧠 Memory regression signals are detected
🧾 Historical deltas exceed tolerance
🔍 Traces or metrics reveal systemic bottlenecks or exceptions

It ensures deterministic, explainable, and trace-linked regression reporting, automatically integrated into Studio, CI, and human workflows.

Shall we continue to Cycle 12 – Collaboration with Other Agents?

Here is Cycle 12 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

🤝 Cycle 12 – Collaboration with Other Agents¶

This cycle details how the Load & Performance Testing Agent collaborates with other agents in the ConnectSoft ecosystem, forming a performance validation mesh across code, infrastructure, QA, observability, and decision-making workflows.

🔗 Core Collaborating Agents¶

Agent	Interaction
QA Engineer Agent	Supplies `.feature` tests to be validated under load; interprets test flows requiring performance validation
Microservice Generator Agent	Provides `service.metadata.yaml`, test targets, OpenAPI specs
Resiliency & Chaos Engineer Agent	Coordinates chaos+load test schedules and validates system recovery behavior under stress
Studio Agent	Consumes `studio.performance.preview.json` and renders dashboards, status tiles, score histories
Developer Agent	May be notified when performance regressions occur; reads `regression-alert.yaml`, reviews `perf-metrics.json`
Knowledge Management Agent	Stores and retrieves memory entries for `perf-metrics.memory.json`, historical comparisons, and edition trends
CI Agent	Executes `ConnectSoft.RunLoadTests` task in pipeline; evaluates test gating conditions
Bug Investigator Agent	Uses `perf-metrics.json` to correlate with functional test flakiness or system instability reports

📘 Collaboration Flow Example¶

flowchart TD
    GEN[Microservice Generator Agent]
    QA[QA Engineer Agent]
    LOAD[🧪 Load & Performance Agent]
    CHAOS[Resiliency Agent]
    STUDIO[Studio Agent]
    KM[Knowledge Management Agent]
    DEV[Developer Agent]

    GEN --> LOAD
    QA --> LOAD
    CHAOS --> LOAD
    LOAD --> KM
    LOAD --> STUDIO
    LOAD --> DEV

Hold "Alt" / "Option" to enable pan & zoom

🧠 Collaboration Modalities¶

Modality	Mechanism
Input ingestion	Consumes `trace.plan.yaml`, `test-suite.plan.yaml`, `perf-thresholds.yaml`, `.feature`
Event-triggered	Responds to `TestGenerated`, `ChaosInjected`, `BuildCompleted` events
Artifact sharing	Publishes `perf-metrics.json`, `load-trace-map.yaml`, `studio.performance.preview.json`
Memory interface	Loads and pushes entries via `MemoryPusherSkill` and `RegressionComparerSkill`
Studio sync	Invokes `PreviewPublisherSkill` to update performance status and summary
CI feedback	Emits `status: degraded/fail` to pipeline for gating or retrying builds

📘 Studio Collaboration Artifacts¶

File	Used by Studio
`studio.performance.preview.json`	Shows trace-aware performance tile
`regression-alert.yaml`	Triggers badge, highlights regression origin
`perf-metrics.json`	Linked via Studio trace tiles; previewed with confidence, score, RPS
`trace-summary.json`	Used to show slowest span, root cause, and response duration trends

🧾 Developer/Reviewer Feedback Loop¶

Trigger	Action
`performanceScore < 0.75`	Notifies `DeveloperAgent` for potential optimization
`traceId` regression in Studio	Allows reviewer to click → inspect `perf-metrics.json` and associated diagrams
Manual annotation	Developer may override or flag false positive (e.g., memory spike unrelated to app)

🔁 QA/Chaos Coordination¶

Scenario	Behavior
`chaos-injection: true`	Load test rerun after fault to validate recovery time
`QA.flakyFeature: true`	Runs latency test to isolate whether instability is infra- or logic-related
`soak-timeout: breached`	Load Agent emits alert and triggers Chaos Agent to inspect async queues or cache collapse patterns

✅ Summary¶

The Load & Performance Testing Agent:

🔗 Collaborates closely with QA, Resiliency, Developer, and Memory agents
📎 Produces artifacts and telemetry consumed by Studio and CI pipelines
📤 Responds to upstream events and helps validate downstream impact
🧠 Writes and reads memory for trend-based comparison and historical tracking

It operates as the bridge between runtime performance and software correctness, driving both automation and visibility in the ConnectSoft AI Software Factory.

Shall we proceed to Cycle 13 – Surface Coverage (API, Event, Async, Mobile)?

Here is Cycle 13 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

🌐 Cycle 13 – Surface Coverage (API, Event, Async, Mobile)¶

This cycle defines the types of systems and interfaces the agent is capable of testing under load. It supports broad coverage across service interfaces, async workflows, user interaction channels, and real-time systems — essential for validating distributed and event-driven SaaS architectures.

🌐 Supported Surface Types¶

Surface	Description	Example Targets
HTTP REST APIs	Most common load target — tests CRUD operations, workflows	`/api/checkout`, `/appointments/schedule`
gRPC Services	Concurrent connection load, streaming, binary payloads	`AppointmentService.Book()`, `ClientSync.Stream()`
Async Event Handlers	Message consumers for queues, pub/sub, and buses	Azure Service Bus, RabbitMQ, Kafka topics
SignalR / WebSocket	Real-time message channels, session scalability	Live chat, client notifications, dashboard feeds
Mobile/Frontend APIs	Load tests simulate real-user flows across sessions	Login + Booking flow with parallel clients
Webhook Consumers	Inbound events from external systems	`POST /webhooks/email-confirmed`, `POST /lab-result-received`
Composite Workflows	Multi-service call chains triggered from BFF or frontend	e.g., `/book-now` triggers internal: client→invoice→notify
Long-running Jobs / CRON APIs	Schedule-based async APIs that enqueue work	`/daily-inventory-recalculation`, `/sync-resumes`

📦 Test Config Examples by Surface Type¶

REST API (Standard Load Test)¶

testType: load
endpoint: /api/checkout
method: POST
rps: 150
duration: 5m

Async Queue (Spike Test)¶

testType: spike
queue: notify-sms-queue
rps: 300
payloadTemplate: sms-payload.json

gRPC (Soak Test)¶

testType: soak
service: NotificationService
rpc: SendConfirmation
rps: 50
duration: 2h

WebSocket Session Load¶

testType: concurrency
target: /realtime-feed
connectionCount: 1000
sessionDuration: 15m

📊 Metrics Collected per Surface Type¶

Type	Additional Metrics
gRPC	Streaming stability, connection reuse, frame size variance
Async Queue	Queue depth, processing lag, time-to-ack
SignalR/WebSocket	Connection churn rate, reconnect frequency, latency spikes
Webhooks	External delivery rate, retry response lag
Frontend/Mobile API	Roundtrip latency (real-user simulation), login/auth cache impact

🧠 Edition-Aware Considerations¶

Different editions or tenants may implement fallback behaviors, queue partitions, or lower concurrency limits
Mobile vs. Enterprise editions might throttle notifications, affect async fan-outs
The agent scopes load profile and thresholds based on editionId

🧪 Surface-Specific Agent Behaviors¶

Surface	Agent Enhancements
REST API	Applies JSON schema-based fuzzing or payload generation
Queue	Tracks async processing chain, dead-letter impact, subscriber lag
Mobile simulation	Optional integration with BrowserStack, Playwright, or mock frontends
WebSocket/Realtime	Validates per-session memory, latency, and packet drop under user scale

✅ Summary¶

The Load & Performance Testing Agent supports wide and deep surface coverage, including:

🔗 REST, gRPC, event queues, pub/sub
📱 Mobile APIs and real-time channels
🧠 Async and composite workflow validation
📊 Edition-aware testing across scalable surfaces

This allows end-to-end performance validation across ConnectSoft’s modular and event-driven SaaS systems.

Shall we proceed with Cycle 14 – Edition/Tenant-Specific Testing & Thresholding?

Here is Cycle 14 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

🏷️ Cycle 14 – Edition/Tenant-Specific Testing & Thresholding¶

This cycle explains how the agent handles multi-edition and multi-tenant performance validation, ensuring each ConnectSoft SaaS edition is tested independently with edition-aware inputs, thresholds, memory, and expectations.

🏷️ What Is an Edition?¶

An Edition represents a product variation or tenant context, e.g.:

Edition ID	Description
`vetclinic`	Base edition for veterinary clinics
`vetclinic-premium`	Premium tier with SMS/email scaling, high concurrency
`multitenant-lite`	Lightweight multi-tenant mode, throttled I/O
`franchise-enterprise`	High-volume deployment with autoscaling queues

Each edition can have different:

APIs and endpoints
Message throughput expectations
Load characteristics and limits
Infrastructure allocations (CPU, queue depth, memory)
Threshold policies (latency, error rate, SLO)

📥 Inputs Affected by Edition¶

Artifact	Behavior
`perf-thresholds.yaml`	Thresholds scoped per `editionId` and `moduleId`
`test-suite.plan.yaml`	Load profile adjusted based on tenant capacity and product tier
`perf-baseline.memory.json`	Retrieved only from memory entries for same `editionId`
`studio.performance.preview.json`	Tile labeled with edition-aware score and tag

📘 Example: Threshold File with Multiple Editions¶

module: NotificationService
thresholds:
  - editionId: vetclinic
    latencyP95: 500
    rpsMin: 80
  - editionId: vetclinic-premium
    latencyP95: 650
    rpsMin: 120
  - editionId: multitenant-lite
    latencyP95: 400
    rpsMin: 60

📊 Edition-Aware Memory Comparison¶

The agent compares:

Only same editionId
Same testType, moduleId, and endpoint
Overlays trends across builds within the same edition only

"baselineComparison": {
  "editionId": "vetclinic-premium",
  "p95Delta": "+22%",
  "regressed": true
}

✅ Edition-Specific Test Execution¶

Edition	Adjustments
`lite` editions	Lower concurrency, shorter duration, adjusted RPS
`premium` editions	Spike/soak tests enabled, full resource profile captured
`enterprise` editions	Queues, autoscaling, async lag tracked aggressively
Multi-tenant setups	Agent uses `tenantId`-partitioned test data or payload decorators

🧠 Studio Visualization¶

Tiles grouped or filtered by edition
Cross-edition comparison reports available for:

/report/performance-trends?edition=vetclinic-premium

Studio badge color and summary based on edition policy

🧾 Artifact Paths Per Edition¶

Artifact	Path
`perf-metrics.json`	`perf/metrics/vetclinic/checkoutservice.json`
`regression-alert.yaml`	`perf/alerts/franchise-enterprise/notifications.yaml`
`studio.performance.preview.json`	Includes `editionId` in preview metadata

✅ Summary¶

The agent fully supports edition-scoped testing by:

🏷️ Respecting edition-specific thresholds, test profiles, and expectations
🧠 Comparing only against matching-edition memory
📊 Visualizing results per edition in Studio
📤 Emitting edition-aware artifacts for traceability and downstream logic

This enables SaaS quality control across thousands of tenants and configurations.

Shall we continue with Cycle 15 – Performance Scoring Model?

Here is Cycle 15 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

📊 Cycle 15 – Performance Scoring Model¶

This cycle defines how the agent calculates a normalized performanceScore (range: 0.0–1.0) that represents the overall health and efficiency of the system under test. This score enables consistent comparison across services, builds, and editions — powering Studio dashboards, regression alerts, and CI/CD gates.

🎯 Purpose of the Score¶

Quantify performance in a single metric
Drive pass/warning/fail classification
Feed Studio visualization tiles
Compare current vs. memory baselines
Trigger alerts or retries
Rank or prioritize services in need of tuning

📈 Performance Score Range¶

Score Range	Meaning
`0.90 – 1.00`	✅ Excellent – passed all thresholds
`0.75 – 0.89`	⚠️ Acceptable – warning, minor degradation
`0.50 – 0.74`	📉 Degraded – needs investigation
`0.00 – 0.49`	❌ Failed – critical regression or bottleneck

🧮 Score Formula (Default Weights)¶

performanceScore = 
  0.35 * latencyScore +
  0.25 * throughputScore +
  0.20 * errorRateScore +
  0.10 * resourceUtilizationScore +
  0.10 * baselineDeltaScore

Each component returns a normalized score (0–1), weighted accordingly.

🔹 Score Components Explained¶

Component	Description	Normalization Rule
latencyScore	Based on P95 or P99 latency vs. threshold	1.0 if ≤ threshold, decreases linearly after
throughputScore	RPS achieved vs. RPS minimum	1.0 if ≥ minimum, falls off sharply if under
errorRateScore	Lower is better (ideal <1%)	1.0 if ≤ threshold, 0.0 if ≥ 5%
resourceUtilizationScore	CPU/memory usage under pressure	Penalized for CPU >90%, memory >85% of quota
baselineDeltaScore	Comparison to memory baseline	Penalty for P95 latency increase >20%, bonus if improved

📘 Example Scoring Result¶

{
  "performanceScore": 0.82,
  "scoreComponents": {
    "latencyScore": 0.84,
    "throughputScore": 0.90,
    "errorRateScore": 0.98,
    "resourceUtilizationScore": 0.60,
    "baselineDeltaScore": 0.75
  }
}

→ Result: ⚠️ status: warning, score = 0.82

🧠 Edition-Aware Score Calibration¶

Weighting or expectations can be adjusted per editionId
Example: multitenant-lite may apply less weight to throughput, more to memory use
Historical trends tracked by KnowledgeManagementAgent influence thresholds

🧪 Scoring Override Options¶

Mechanism	Purpose
`scoreOverride: true`	Allows human agent to manually mark pass/fail for flakey environments
`customWeighting.yaml`	Overrides formula for specific module/edition/testType combinations
`studio.annotation.yaml`	May apply `excludeFromScore: true` for exploratory tests

📊 Studio Visualization Usage¶

Tile Field	Value
`performanceScore`	Shown as numeric badge or heatmap
`scoreDeltaVsBaseline`	Renders arrow or change indicator
`scoreStatus`	Maps to color: green/yellow/red
`hoverDetails`	Expanded score breakdown and metric deltas

✅ Summary¶

The Load & Performance Testing Agent:

Calculates a multi-factor performanceScore to rate system behavior
Enables consistent pass/warn/fail classification
Powers trend charts, alerts, and Studio previews
Adjusts dynamically per edition, module, or historical baseline
Supports scoring transparency via full breakdown in metrics file

This provides a unified, explainable, and traceable performance health signal across the platform.

Shall we continue with Cycle 16 – Artifact Outputs?

Here is Cycle 16 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

📁 Cycle 16 – Artifact Outputs¶

This cycle documents the structured artifacts generated by the Load & Performance Testing Agent during each test run. These artifacts are used by downstream agents (e.g., Studio, QA, Knowledge, Dev), stored in memory, and integrated into dashboards and CI/CD pipelines.

📦 Core Output Artifacts¶

File	Format	Description
`perf-metrics.json`	JSON	Main result file including performanceScore, latency, RPS, error rate, and resource usage
`load-trace-map.yaml`	YAML	Maps service endpoints or events to test results and metrics, trace-linked
`studio.performance.preview.json`	JSON	Tile metadata for Studio dashboards, with summary, score, status
`regression-alert.yaml` (optional)	YAML	Generated if regression is detected compared to memory baseline
`flamegraph.svg` (optional)	SVG	Visual call trace or CPU flamegraph from load tool or APM
`score.log.jsonl`	JSONL	Step-by-step breakdown of scoring components and logic used
`trace-summary.json`	JSON	Span and latency breakdown for async or distributed flows
`doc-coverage.metrics.json` (updated)	JSON	Aggregates score trends and test coverage for module/edition/reporting

📘 Example: `perf-metrics.json`¶

{
  "traceId": "proj-934-appointment-booking",
  "editionId": "vetclinic-premium",
  "testType": "soak",
  "moduleId": "AppointmentsService",
  "performanceScore": 0.91,
  "status": "pass",
  "latency": {
    "p50": 320,
    "p95": 580,
    "p99": 750
  },
  "rps": 105,
  "errorRate": 0.004,
  "cpuUsagePct": 68.2,
  "baselineComparison": {
    "deltaLatencyP95": "+8%",
    "regressed": false
  }
}

📘 Example: `load-trace-map.yaml`¶

traceId: proj-934-appointment-booking
editionId: vetclinic-premium
moduleId: AppointmentsService
tests:
  - endpoint: /appointments/book
    testType: soak
    latencyP95: 580
    errorRate: 0.004
    rps: 105
    status: pass

📘 Example: `studio.performance.preview.json`¶

{
  "traceId": "proj-934-appointment-booking",
  "moduleId": "AppointmentsService",
  "editionId": "vetclinic-premium",
  "performanceScore": 0.91,
  "status": "pass",
  "tileSummary": "Soak test: 580ms p95, RPS 105. Within baseline.",
  "regression": false,
  "testType": "soak"
}

📘 Optional: `regression-alert.yaml`¶

traceId: proj-911-checkout
editionId: vetclinic
moduleId: CheckoutService
status: degraded
reason: "p95 latency increased 34% vs. baseline"
suggestedActions:
  - Review flamegraph
  - Notify DeveloperAgent
  - Retry with tuned concurrency

📘 Scoring Log (`score.log.jsonl`)¶

Each line includes component weight and result:

{
  "latencyScore": 0.88,
  "throughputScore": 0.91,
  "errorRateScore": 0.99,
  "baselineDeltaScore": 0.76,
  "resourceUtilizationScore": 0.85,
  "finalScore": 0.89
}

🧠 Memory Integration¶

All artifacts are tagged by:

traceId, editionId, moduleId, testType
And stored in memory via MemoryPusherSkill

✅ Summary¶

The Load & Performance Testing Agent emits:

🧾 Validated and traceable metrics (perf-metrics.json)
📎 Mappable trace-path YAMLs and Studio previews
📊 Scoring logs and dashboards for developer or reviewer review
🧠 Memory-compatible outputs for regression comparison
⚠️ Regression alerts when test results deviate significantly

These artifacts form the observable, automatable backbone of ConnectSoft’s performance QA strategy.

Shall we continue with Cycle 17 – Memory & History Use?

Here is Cycle 17 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

🧠 Cycle 17 – Memory & History Use¶

This cycle explains how the agent leverages long-term memory to enhance performance evaluation with historical context, enabling trend analysis, baseline comparison, regression detection, and automated scoring calibration.

📦 What the Agent Stores in Memory¶

Entry	Description
`perf-metrics.memory.json`	Historical `perf-metrics.json` stored per `traceId`, `editionId`, `moduleId`, and `testType`
`score.log.jsonl`	Past scoring breakdowns for learning patterns and confidence tracking
`trace-summary.json`	Span-based historical latency profiles used for root cause pattern matching
`flamegraph.svg`	Stored for visual analysis of bottleneck shifts over time
`load-trace-map.yaml`	Summarized test result paths reused in documentation and Studio context

📥 Memory Query on Test Start¶

When initiating a new test, the agent:

Queries memory store (via RegressionComparerSkill)
Filters by:
editionId
moduleId
testType
endpoint (if scoped)
Retrieves most recent validated test run (status: pass)
Loads historical performanceScore, latency.p95, error rate

🔁 What the Agent Compares¶

Metric	Compared To
`latency.p95`	Last passing value ± allowed delta
`performanceScore`	Prior score to detect degradation trend
`rps`	Minimum sustained throughput seen in historical best run
`spanDuration`	Used in `trace-summary.json` delta to flag new bottlenecks
`cpuUsage`	Tracked for gradual infrastructure stress (esp. in soak tests)

📘 Memory Example Entry (Baseline)¶

{
  "editionId": "vetclinic-premium",
  "moduleId": "NotificationService",
  "testType": "spike",
  "performanceScore": 0.94,
  "latency": {
    "p95": 530
  },
  "rps": 120,
  "traceId": "proj-877"
}

📊 Trend Insights Enabled¶

Use Case	Behavior
📉 Regression detection	If new test has `deltaLatencyP95 > 25%`, mark `regressed: true`
✅ Baseline refresh	If new test passes and score improves, baseline is overwritten
📈 Trend charting	Studio charts score history using memory logs
🧠 Agent self-tuning	Memory-enhanced scoring adjusts expectations over time (e.g., for known slow modules)

📎 Memory Keys Used¶

memoryKey:
  - moduleId
  - editionId
  - testType
  - endpoint (if API-specific)
  - traceId (version lineage)

🧠 Memory-Linked Agent Behavior¶

Trigger	Outcome
Memory regression detected	Emit `regression-alert.yaml`
No baseline available	Flag test as `exploratory` in preview
3 consistent regressions	Suggest auto-tuning test or threshold policy
Score improving consistently	Auto-promote result as new baseline with `confidenceScore > 0.85`

✅ Summary¶

The Load & Performance Testing Agent uses memory to:

🧠 Compare current performance against trusted historical runs
🔍 Detect regressions and anomalies intelligently
🧾 Store performance history across editions and services
📊 Power dashboards, scoring evolution, and auto-tuning heuristics

This ensures that every test run is traceable in time and aware of its evolution, driving smarter quality automation.

Shall we continue with Cycle 18 – Retry & Correction Path?

Here is Cycle 18 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

🔁 Cycle 18 – Retry & Correction Path¶

This cycle outlines how the agent handles retry logic, correction heuristics, and fallback behaviors when a load test fails, degrades unexpectedly, or encounters infrastructure or environment issues.

🔁 When a Retry Is Triggered¶

Trigger	Condition
❌ `status: fail`	Hard failure due to threshold or system crash
📉 `performanceScore < 0.5`	Score too low compared to baseline or policy
⚠️ `status: degraded` and flakiness pattern matches	Potential environmental flakiness (e.g., first test after deploy)
💥 Infrastructure anomaly	CPU spike, warm-up gap, app cold start, GC stall
🛠️ Agent instructed	Via Studio annotation or pipeline flag: `retryOnFail: true`

🔁 Retry Strategy¶

Type	Retry Behavior
Standard Retry	Re-execute with same parameters (after cooldown)
Throttled Retry	Reduce RPS or concurrency by 30–50%
Staged Retry	Shorten duration for validation (e.g., spike reduced from 60s → 15s)
Warm Start Retry	Insert pre-test call to warm caches or cold-started services
Retry with Memory Guidance	Use memory pattern to detect known performance instability and adjust

🧪 Retry Metadata (Embedded in Logs)¶

{
  "traceId": "proj-945",
  "retryAttempt": 2,
  "originalStatus": "degraded",
  "strategy": "throttled",
  "adjustments": {
    "rps": 80,
    "duration": "3m"
  },
  "retryResult": {
    "performanceScore": 0.79,
    "status": "warning"
  }
}

📎 Retry Constraints¶

Rule	Behavior
Max attempts	3 retries per trace by default
Cooling period	Wait 30–60 seconds before retry if test infrastructure reused
Artifact tagging	`perf-metrics.json` includes `retryAttempt` field
Failure after retries	`regression-alert.yaml` generated and escalated to Studio and Dev agents

🛠️ Correction Mechanisms¶

If Condition	Then
Degraded from cold start	Retry with warm-up step or extended duration
Memory mismatch but no actual regression	Allow manual override via `studio.annotation.yaml`
Known flakiness pattern	Skip retry but mark `status: needs-review`
Failing test is exploratory	Downgrade failure impact and skip CI gate via `excludeFromGate: true` flag

👤 Human-Aware Retry¶

If configured or required:

Agent pauses and awaits human annotation
Studio reviewers can apply retryWith: action (e.g., change duration or RPS)
Retry triggered via event or approved in Studio interface

📊 Studio Preview after Retry¶

Preview tile updates to include:

retryAttempt: 2
Status before/after
Change in score
Auto-tuning explanation or warning
Badge: “Recovered after retry” or “Escalated for review”

✅ Summary¶

The Load & Performance Testing Agent:

🔁 Retries intelligently based on cause, confidence, and test configuration
📉 Applies throttling, warm-up, or staged fallback strategies
🧠 Uses memory, trace logs, and Studio instructions to refine recovery
📊 Clearly logs retry path and impact for audit and visualization

This guarantees robust, explainable recovery when real-world variability impacts test outcomes.

Shall we continue with Cycle 19 – Studio Dashboard Exports?

Here is Cycle 19 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

📊 Cycle 19 – Studio Dashboard Exports¶

This cycle details how the agent exports its outputs to ConnectSoft’s Studio interface, powering performance visibility tiles, regression alerts, score trends, and real-time trace-linked diagnostics — enabling both human review and agent chaining.

🖥️ Primary Studio Artifact: `studio.performance.preview.json`¶

Field	Description
`traceId`	The originating trace scope (test or feature run)
`editionId`	Specifies which edition/tenant test applied to
`moduleId`	Target microservice or async system
`testType`	Load, spike, soak, stress, etc.
`performanceScore`	Composite score (0–1) shown on tile
`status`	One of: `pass`, `warning`, `degraded`, `fail`
`regression`	Boolean indicating memory-detected performance drop
`tileSummary`	Short human-readable summary for tile hover and diff display
`retryAttempt`	Number of retries taken, if any
`actions`	Optional hints or buttons (retry, view trace, annotate)

📘 Example: `studio.performance.preview.json`¶

{
  "traceId": "proj-955-notify-client",
  "editionId": "vetclinic",
  "moduleId": "NotificationService",
  "testType": "spike",
  "performanceScore": 0.62,
  "status": "degraded",
  "regression": true,
  "retryAttempt": 1,
  "tileSummary": "Spike test: p95 latency +32% vs baseline, error rate 2.1%",
  "actions": ["view-trace", "retry-with-throttle"]
}

📊 Tile Behavior in Studio UI¶

Attribute	Effect
`performanceScore`	Shows numeric badge or progress bar
`status`	Color-coded badge: green (pass), yellow (warn), red (fail)
`regression: true`	Adds “⚠️ Regression Detected” marker
`tileSummary`	Visible on hover or in tile preview
`traceId`	Enables click-through to full trace, metrics, flamegraphs
`actions`	Shows dropdown for retry, assign, or annotate options

📈 Studio Charts Powered by This Agent¶

Chart	Source
🕸️ Performance Over Time	Aggregated scores across builds in `doc-coverage.metrics.json`
🔁 Retry Heatmaps	Count and outcome of retries per module/testType
🧠 Regression Deltas	Plot of `deltaLatencyP95` across editions or traces
⚡ Test Coverage Map	From `load-trace-map.yaml` summarizing tested paths per edition
📂 Score Breakdown	Bar chart from `score.log.jsonl` showing weighted impact of latency, errors, CPU

📘 Badge Summary View (Rendered by Studio Agent)¶

status: degraded
score: 0.62
retryAttempt: 1
regression: true
summary: "p95 latency up 32%, errors at 2.1%, degraded from previous score 0.84"
badgeColor: red

📤 Published Outputs Used by Studio¶

File	Purpose
`studio.performance.preview.json`	Primary tile input
`perf-metrics.json`	Full breakdown on click-through or debug overlay
`regression-alert.yaml`	Triggers Studio notifications or inbox messages
`trace-summary.json`	Feeds span viewer and slow-path diagnostics
`flamegraph.svg`	Opens in modal or inline SVG diagnostic panel
`doc-coverage.metrics.json`	Score history for all modules, editions, and builds

✅ Summary¶

The Load & Performance Testing Agent:

📊 Exports a trace-linked, score-rich preview JSON for Studio
🖼️ Powers performance dashboards, regression heatmaps, and retry indicators
📎 Links all test runs to trace paths, metrics, and observability overlays
🤖 Enables other agents and humans to trace, retry, annotate, or escalate intelligently

This transforms every performance test into a real-time, navigable, and actionable UI tile in ConnectSoft Studio.

Shall we complete this spec with Cycle 20 – Final Blueprint & Future Vision?

Here is Cycle 19 of the Load & Performance Testing Agent – Detailed Realization Specification:

🧪 Load & Performance Testing Agent¶

📊 Cycle 19 – Studio Dashboard Exports¶

This cycle details how the agent exports its outputs to ConnectSoft’s Studio interface, powering performance visibility tiles, regression alerts, score trends, and real-time trace-linked diagnostics — enabling both human review and agent chaining.

🖥️ Primary Studio Artifact: `studio.performance.preview.json`¶

Field	Description
`traceId`	The originating trace scope (test or feature run)
`editionId`	Specifies which edition/tenant test applied to
`moduleId`	Target microservice or async system
`testType`	Load, spike, soak, stress, etc.
`performanceScore`	Composite score (0–1) shown on tile
`status`	One of: `pass`, `warning`, `degraded`, `fail`
`regression`	Boolean indicating memory-detected performance drop
`tileSummary`	Short human-readable summary for tile hover and diff display
`retryAttempt`	Number of retries taken, if any
`actions`	Optional hints or buttons (retry, view trace, annotate)

📘 Example: `studio.performance.preview.json`¶

{
  "traceId": "proj-955-notify-client",
  "editionId": "vetclinic",
  "moduleId": "NotificationService",
  "testType": "spike",
  "performanceScore": 0.62,
  "status": "degraded",
  "regression": true,
  "retryAttempt": 1,
  "tileSummary": "Spike test: p95 latency +32% vs baseline, error rate 2.1%",
  "actions": ["view-trace", "retry-with-throttle"]
}

📊 Tile Behavior in Studio UI¶

Attribute	Effect
`performanceScore`	Shows numeric badge or progress bar
`status`	Color-coded badge: green (pass), yellow (warn), red (fail)
`regression: true`	Adds “⚠️ Regression Detected” marker
`tileSummary`	Visible on hover or in tile preview
`traceId`	Enables click-through to full trace, metrics, flamegraphs
`actions`	Shows dropdown for retry, assign, or annotate options

📈 Studio Charts Powered by This Agent¶

Chart	Source
🕸️ Performance Over Time	Aggregated scores across builds in `doc-coverage.metrics.json`
🔁 Retry Heatmaps	Count and outcome of retries per module/testType
🧠 Regression Deltas	Plot of `deltaLatencyP95` across editions or traces
⚡ Test Coverage Map	From `load-trace-map.yaml` summarizing tested paths per edition
📂 Score Breakdown	Bar chart from `score.log.jsonl` showing weighted impact of latency, errors, CPU

📘 Badge Summary View (Rendered by Studio Agent)¶

status: degraded
score: 0.62
retryAttempt: 1
regression: true
summary: "p95 latency up 32%, errors at 2.1%, degraded from previous score 0.84"
badgeColor: red

📤 Published Outputs Used by Studio¶

File	Purpose
`studio.performance.preview.json`	Primary tile input
`perf-metrics.json`	Full breakdown on click-through or debug overlay
`regression-alert.yaml`	Triggers Studio notifications or inbox messages
`trace-summary.json`	Feeds span viewer and slow-path diagnostics
`flamegraph.svg`	Opens in modal or inline SVG diagnostic panel
`doc-coverage.metrics.json`	Score history for all modules, editions, and builds

✅ Summary¶

The Load & Performance Testing Agent:

📊 Exports a trace-linked, score-rich preview JSON for Studio
🖼️ Powers performance dashboards, regression heatmaps, and retry indicators
📎 Links all test runs to trace paths, metrics, and observability overlays
🤖 Enables other agents and humans to trace, retry, annotate, or escalate intelligently

This transforms every performance test into a real-time, navigable, and actionable UI tile in ConnectSoft Studio.

Shall we complete this spec with Cycle 20 – Final Blueprint & Future Vision?

🧪 Load & Performance Testing Agent¶

🎯 Cycle 1 – Purpose and Strategic Role¶

📌 Core Mission¶

🎯 Strategic Role in the ConnectSoft AI Software Factory¶

📘 Example Agent Outputs¶

🧠 Example Agent Class / Cluster¶

🤝 Where This Agent Fits in the Platform¶

🧾 Example Studio Tile Summary¶

✅ Summary¶

🧪 Load & Performance Testing Agent¶

📋 Cycle 2 – Responsibilities¶

✅ Primary Responsibilities¶

🔬 Supported Test Categories¶

🧩 Specialized Responsibilities (Optional)¶

🧠 Documentation Output (Structured Summary)¶

✅ Summary¶

🧪 Load & Performance Testing Agent¶

📥 Cycle 3 – Inputs Consumed¶

📂 Input Artifacts¶

📘 Example: test-suite.plan.yaml¶

📘 Example: perf-thresholds.yaml¶

📘 Example: perf-baseline.memory.json¶

🔄 Dynamic Context Inputs (injected via SKContext or memory overlay)¶

📎 Supporting Artifacts Consumed (Optional)¶

✅ Summary¶

🧪 Load & Performance Testing Agent¶

📤 Cycle 4 – Outputs Produced¶

📦 Primary Output Artifacts¶

📘 Example: perf-metrics.json¶

📘 Example: load-trace-map.yaml¶

📘 Example: studio.performance.preview.json¶

📘 Optional: regression-alert.yaml¶

📊 Metrics in perf-metrics.json¶

🧠 Memory Integration¶

✅ Summary¶

🧪 Load & Performance Testing Agent¶

🔄 Cycle 5 – Execution Flow¶

📊 High-Level Execution Flow¶

🧩 Detailed Step-by-Step Execution¶

1. Trigger & Initialization¶

2. Parse Input Artifacts¶

3. Test Planning¶

4. Prepare Environment¶

5. Execute Performance Test¶

6. Observe + Capture Telemetry¶

7. Compare to Thresholds + Memory¶

8. Score Generation¶

9. Status Classification¶

10. Emit Artifacts¶

11. Store + Publish¶

✅ Summary¶

🧪 Load & Performance Testing Agent¶

🧠 Cycle 6 – Skills and Kernel Functions Used¶

🧠 Core Skills¶

🔁 Skill Orchestration (Execution Chain)¶

📦 Supporting Plugins / Connectors¶

🧠 Context Variables in SK Execution¶

📘 Example Skill Call (from YAML or SK planner)¶

📎 Reused by Other Agents¶

✅ Summary¶

🧪 Load & Performance Testing Agent¶

🧪 Cycle 7 – Test Types & Metrics Captured¶

🧪 Supported Test Types¶

📊 Metrics Captured (Per Test Run)¶

⚙️ System-Level Metrics¶

📞 Request Metrics¶

🧠 Memory/Trend Comparison Metrics¶

📘 Example Output (from perf-metrics.json)¶

🧪 Additional Test Metadata (captured or inferred)¶

✅ Summary¶

🧪 Load & Performance Testing Agent¶

✅ Cycle 8 – Validation Thresholds¶

📏 Threshold Sources¶

📘 Example: perf-thresholds.yaml¶

✅ Validation Rules by Metric¶

🚦 Classification Logic¶

🧠 Memory-Based Comparison Example¶

🔁 Retry Conditions Triggered by Thresholds¶

📊 Studio and CI Feedback¶

✅ Summary¶

📘 Example: `test-suite.plan.yaml`¶

📘 Example: `perf-thresholds.yaml`¶

📘 Example: `perf-baseline.memory.json`¶

🔄 Dynamic Context Inputs (injected via `SKContext` or memory overlay)¶

📘 Example: `perf-metrics.json`¶

📘 Example: `load-trace-map.yaml`¶

📘 Example: `studio.performance.preview.json`¶

📘 Optional: `regression-alert.yaml`¶

📊 Metrics in `perf-metrics.json`¶

📘 Example Output (from `perf-metrics.json`)¶

📘 Example: `perf-thresholds.yaml`¶

📘 Example: `trace-summary.json`¶

📘 Example: `regression-alert.yaml`¶