⚡ Resiliency & Chaos Agent Specification¶

🎯 Purpose¶

The Resiliency & Chaos Agent simulates, monitors, and validates how ConnectSoft-generated services respond to failure, latency, overload, and systemic instability. It ensures that every microservice and workflow is equipped with:

✅ Retry logic
🔁 Fallback paths
🧯 Circuit breakers
🕸️ Dependency isolation
📊 Observability under failure

It injects faults to test real-world failure conditions and verifies that the system degrades gracefully and recovers autonomously.

🧠 Strategic Role in the ConnectSoft Factory¶

Role	Description
🧪 Chaos Validator	Simulates failure (e.g., dropped database, message loss, timeout) to validate fallback logic
🧠 Resilience Scorer	Computes `resiliencyScore` from observed behavior under chaos
🧰 Recovery Monitor	Checks for recovery signals: retries, alerts, scaling, failover
🔎 Failure Pattern Analyzer	Captures trace of cascading failures, slow retries, unhandled exceptions
📊 Studio Visualizer	Publishes fault maps, impact graphs, and score previews to Studio
📥 Load Agent Partner	Coordinates fault injection with ongoing load to assess system stability under pressure
🧾 SLA Risk Detector	Warns when fault handling degrades user experience or breaches expected boundaries

🌐 Example Use Cases¶

Scenario	Resiliency Agent Behavior
📉 Dependency returns 500 errors for 30 seconds	Verifies that service retries or falls back to cached data
🔌 Message broker queue pauses for 2 minutes	Detects delay propagation or failure to buffer events
⏳ Downstream timeout > 5s	Confirms that circuit breaker opens or retries stop within defined limits
💥 Memory spike or exception storm	Detects if failure is isolated or cascades to other services
🔄 After recovery	Checks if retry count stops, and system heals automatically or needs human intervention

🎯 Why It’s Essential¶

Modern SaaS systems are:

Distributed → Failures happen often
Asynchronous → Latency and ordering matter
Tenant-sensitive → Impact may differ by edition
Mission-critical → SLA breaches must be caught pre-release

The Resiliency Agent makes failure safe, testable, traceable, and explainable.

🧭 Position in Platform¶

flowchart TD
    GEN[Microservice Generator Agent]
    QA[QA Engineer Agent]
    LOAD[Load Testing Agent]
    RES[⚡ Resiliency & Chaos Agent]
    KM[Knowledge Management Agent]
    STUDIO[Studio Agent]
    DEV[Developer Agent]

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

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

✅ Summary¶

The Resiliency & Chaos Agent is:

🔁 A chaos-injection and fault validation system
🧠 A recovery and resilience scoring engine
📊 A studio-integrated reporter of degradation and recovery paths

Its mission is to ensure graceful failure, fast recovery, and safe dependency handling in all ConnectSoft-generated systems — across editions, tenants, and environments.

📋 Responsibilities¶

This section defines the primary responsibilities of the Resiliency & Chaos Agent. It acts as a distributed fault injector, a recovery pattern verifier, and a resilience assessor across services, workflows, queues, and communication paths.

✅ Core Responsibilities¶

Responsibility	Description
🔥 Chaos Injection Execution	Inject failures (e.g., network drops, latency, timeouts, service unavailability) using fault plans
🔁 Observe Recovery Behavior	Detect whether services retry, fail fast, fallback, or recover autonomously
⚙️ Validate Resilience Patterns	Ensure retry policies, circuit breakers, timeouts, bulkheads, and fallback routes are functioning
🧠 Compare to Expected Recovery Plans	Uses service metadata or memory to validate declared vs. actual recovery behavior
📊 Emit Resiliency Score	Scores the system’s ability to handle failure gracefully (0–1 scale)
📎 Capture Fault Injection Trace	Correlates failures with spans, logs, and event chain breaks
📤 Export Failure Reports	Emits structured outputs: `resiliency-metrics.json`, `chaos-injection-report.yaml`, `studio.preview.json`
🧠 Update Memory and Trends	Logs which services degrade, recover, or cascade under failure conditions
🚦 Escalate or Gate Based on Criticality	Fails CI or blocks deploy if essential recovery behavior fails
📘 Produce Studio Diagnostics	Annotates tiles, timelines, or impact maps based on fault path severity

📘 Example Agent Use Cases¶

Scenario	Responsibility Triggered
Kill service pod under spike load → system does not retry	Detect failed retry behavior, mark degraded
Queue blocked for 1 min → consumer does not recover	Observe missing `OnError`, emit `status: fail`
API returns 503, fallback service invoked	Mark fallback successful, score positively
Chaos config says “should retry 3x” → only 1 retry happens	Mismatch detected, emits deviation report
Message is lost → system compensates via scheduled job	Compensation detected and scored as recovery path

🧠 Coordination Responsibilities¶

Partner	Behavior
Load & Performance Agent	Chaos run timed to overlap with spike or soak test
QA Agent	Confirms resiliency behavior during end-to-end test flows
Knowledge Agent	Logs recovery behavior patterns and validates if past regressions are recurring
Developer Agent	Subscribed to alerts for missing timeouts, retries, or fallback failures

✅ Summary¶

The Resiliency & Chaos Agent is responsible for:

🔥 Injecting faults in services and communication layers
🧠 Observing if recovery is handled automatically, gracefully, or dangerously
📊 Scoring services' ability to contain and absorb failure
🧾 Producing detailed diagnostics, deviations, and recovery documentation
📎 Powering both human and automated visibility in Studio and memory

This ensures ConnectSoft services are not just fast — but resilient, self-healing, and failure-aware.

📥 Inputs Consumed¶

This section outlines the structured input artifacts, runtime data, policies, and context that the agent consumes to plan and execute chaos experiments and resilience validations.

📂 Core Input Artifacts¶

Input File	Description
`resiliency-profile.yaml`	Describes what types of chaos to inject, where, and under what constraints
`service.metadata.yaml`	Declares endpoints, events, and dependencies the agent can target
`trace.plan.yaml`	Business flow context to identify critical paths for fault injection
`chaos-strategy.yaml`	Edition- or module-specific strategies (e.g., inject latency into email service only in premium tier)
`fallback-rules.yaml`	Expected fallbacks per endpoint, event, or flow (e.g., SMS fallback if email fails)
`resilience-thresholds.yaml`	Policy file: max acceptable time-to-recover, error propagation limits, retry budget
`perf-metrics.json`	From Load Agent, used to test resilience under pressure (joint test validation)

🔁 Dynamic Context from Execution Environment¶

Context Variable	Use
`editionId`	Ensures chaos injection aligns with SLA/SLO for the given edition
`traceId`	Links chaos result to a business or technical test trace
`moduleId`	Scopes fault domain to a service under test
`injectedFaultType`	Used to track and correlate effect of injected failure
`loadPressure`	When testing under stress/spike scenarios

📘 Example: `resiliency-profile.yaml`¶

moduleId: NotificationService
editionId: vetclinic-premium
faults:
  - type: latency
    target: EmailSender
    delayMs: 1500
  - type: message-drop
    target: SmsQueue
    frequency: 0.1
  - type: dependency-down
    target: AuditLogger
    duration: 10s
scenarios:
  - name: fallback-to-sms
    expectedFallback: true

📘 Example: `fallback-rules.yaml`¶

fallbacks:
  - endpoint: /appointments/book
    primary: EmailNotification
    fallback: SmsNotification
    fallbackPolicy: triggeredIfLatency > 1000ms or dependencyDown

📘 Example: `resilience-thresholds.yaml`¶

thresholds:
  maxRecoveryTimeMs: 3000
  maxPropagationErrorRate: 0.02
  retryBudget:
    maxRetries: 3
    maxDelayBetweenRetriesMs: 2000

🧠 Inputs from Memory¶

Input	Used For
`past-chaos-runs.memory.json`	Compare current test to historical failures or improvements
`span-graph.memory.json`	Known latency graphs per flow or service
`fallback-success-rates.memory.json`	Used to predict regression in retry or failover logic

🔎 Additional Runtime Inputs¶

Type	Description
OpenTelemetry traces	Used to observe span-level behavior under injected chaos
Log anomalies	Correlated with chaos events to validate observability coverage
Studio annotations	e.g., "Inject latency into ClientSync for edition lite"

✅ Summary¶

The Resiliency & Chaos Agent consumes:

📄 Chaos definitions and fallback rules
📊 Thresholds and SLO policy constraints
🧠 Historical data for regression detection
🔗 Live telemetry, logs, and trace flow graphs

These inputs ensure the agent can plan, inject, observe, and accurately judge the resilience of any module under test, in a trace-aware and edition-scoped manner.

📤 Outputs Produced¶

This section details the structured artifacts, diagnostic outputs, and signals generated by the Resiliency & Chaos Agent. These outputs feed back into the Studio dashboards, developer workflows, QA trace analysis, and memory-based recovery scoring.

📦 Core Output Artifacts¶

File	Format	Purpose
`resilience-metrics.json`	JSON	Main result: resilienceScore, fault coverage, recovery latencies
`chaos-injection.trace.yaml`	YAML	Documents what fault was injected, where, and when
`studio.resilience.preview.json`	JSON	Preview metadata for Studio dashboards
`fallback-path.map.yaml`	YAML	Maps detected fallback paths (e.g., primary → cached result)
`recovery-flow.svg`	SVG	Optional: visual diagram of recovery flow or retry traces
`resilience-alert.yaml`	YAML	Generated when fallback failed or recovery took too long
`span-resilience-annotations.json`	JSON	Span-level trace annotations (retry, timeout, degradation markers)

📘 Example: `resilience-metrics.json`¶

{
  "traceId": "proj-961-booking-resilience",
  "moduleId": "BookingService",
  "editionId": "vetclinic",
  "chaosType": "timeout",
  "faultInjected": true,
  "resilienceScore": 0.72,
  "status": "warning",
  "recoveryLatencyMs": 1180,
  "fallbackDetected": true,
  "autoHealObserved": false,
  "failureEscalated": false
}

📘 Example: `chaos-injection.trace.yaml`¶

traceId: proj-961-booking-resilience
faultType: timeout
injectedAt: BookingService/AvailabilityClient
duration: 1500ms
faultInjected: true
testType: recovery

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

{
  "traceId": "proj-961-booking-resilience",
  "editionId": "vetclinic",
  "moduleId": "BookingService",
  "resilienceScore": 0.72,
  "status": "warning",
  "chaosType": "timeout",
  "summary": "Timeout simulated at AvailabilityClient. Recovery latency: 1180ms. Fallback path succeeded."
}

📘 Optional: `fallback-path.map.yaml`¶

traceId: proj-961-booking-resilience
moduleId: BookingService
fallbacks:
  - from: "AvailabilityClient"
    to: "LocalCacheReader"
    trigger: "timeout > 1000ms"
    success: true
  - from: "NotificationClient"
    to: "No-opFallback"
    trigger: "connection refused"
    success: false

🔎 Trace-Linked Annotations¶

span-resilience-annotations.json includes annotations like:

[
  {
    "span": "AvailabilityClient.getSlots",
    "event": "timeout",
    "fallback": "LocalCacheReader",
    "recoveryTimeMs": 1180,
    "status": "success"
  },
  {
    "span": "NotificationClient.send",
    "event": "connectionError",
    "status": "failed",
    "message": "No retry policy applied"
  }
]

📊 Used By Studio For¶

Tile coloring and scoring
Visualizing fallback graphs
Enabling human re-run or tuning suggestions
Showing retry/fallback failures per span in trace viewer
Triggering human/agent alerts if recovery patterns break

✅ Summary¶

The Resiliency & Chaos Agent emits:

📊 Structured JSON/YAML for recovery scoring and diagnostics
📎 Trace-linked fault injection and fallback annotations
🧠 Memory-stored resilience profiles
📤 Studio-compatible previews for dashboards and visualization

These outputs allow reliable, explainable resilience validation and alerting across the AI Software Factory platform.

🔁 Execution Flow¶

This section describes the step-by-step execution lifecycle of the Resiliency & Chaos Agent — from target identification to fault injection, behavior observation, scoring, and artifact generation. The flow is highly orchestrated, edition-aware, and trace-linked.

🔁 High-Level Flow Overview¶

flowchart TD
    A[Start: Triggered via traceId or test plan]
    B[Load Target Metadata]
    C[Generate Chaos Plan]
    D[Inject Fault(s)]
    E[Monitor Recovery Behavior]
    F[Capture Telemetry + Span Data]
    G[Classify System Response]
    H[Score Resiliency + Generate Reports]
    I[Emit Studio Preview + Artifacts]
    A --> B --> C --> D --> E --> F --> G --> H --> I

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📋 Step-by-Step Execution¶

1. Trigger Agent Execution¶

Triggered by:
- chaos-enabled: true in test-suite.plan.yaml
- A ResilienceValidationRequired tag from QA or Load Agent
- Scheduled chaos runs per service or edition

2. Load Target Metadata¶

Loads:
- service.metadata.yaml
- chaos-profile.yaml
- Observability config
- Edition and module scope (editionId, moduleId)

3. Generate Chaos Plan¶

Constructs injection points and types:
- Latency injection
- Service unavailability
- Exception throwing
- Network partition or drop
- CPU/memory pressure
Applies edition-aware test tailoring

4. Inject Faults¶

Uses:
- FaultInjectorSkill
- Sidecar injection if supported
- Infrastructure toggles (e.g., kill pod, delay endpoint)
Tags all spans with chaos-test=true and faultType=...

5. Monitor System Behavior¶

Observes:
- Retry attempts
- Fallback activation
- Response time under fault
- Log signals like ServiceUnavailable, TimeoutException
Tracks user-facing degradation or failure modes

6. Capture Telemetry¶

Spans, logs, and traces captured during chaos window
Snapshots performance counters, exception trees, circuit state
Identifies isolation boundaries (what fails vs. what survives)

7. Classify Response¶

Categories:
- ✅ isolated – fault was contained
- ⚠️ degraded – system slowed or reduced quality
- ❌ cascaded – fault caused wider failure
Annotates trace path with failure zones

8. Score and Summarize¶

Calculates resiliencyScore (0–1 scale)
Logs failure type, recovery duration, fallback activation success

9. Emit Artifacts¶

Generates:
- resiliency-metrics.json
- chaos-trace-summary.json
- regression-alert.yaml (if degraded/cascaded)
- studio.resilience.preview.json

📥 Inputs Required¶

chaos-profile.yaml
trace.plan.yaml
test-suite.plan.yaml (optional)
Memory baseline (optional)

📤 Outputs Produced¶

Resilience reports
Studio tiles
Alerts for humans or Developer Agent
Memory entries for future scoring

✅ Summary¶

The agent performs:

💥 Controlled chaos injection
🧠 Intelligent recovery monitoring
📊 Trace-linked scoring and analysis
🧾 Studio and memory output generation

It provides the foundation for autonomous fault validation, ensuring ConnectSoft services are resilient by design.

💥 Chaos Injection Methods¶

This section defines the types of controlled failures the Resiliency & Chaos Agent can inject to test how services behave under abnormal or degraded conditions. These fault types simulate real-world failures such as latency spikes, timeouts, crashes, message loss, and dependency instability.

💣 Chaos Injection Categories¶

Type	Description
Latency Injection	Artificial delay introduced into upstream or internal service calls
Timeout Simulation	Forces an operation to exceed time budget or deadline
HTTP Error Injection	Injects HTTP 5xx or 4xx errors into specific service paths
Dependency Kill	Shuts down a dependent service/container temporarily
Message Drop	Silently drops messages from a queue or pub/sub topic
Queue Saturation	Simulates backlog buildup or rate-limited consumers
Network Blackhole	Drops traffic to/from specific IP, pod, or hostname
CPU Throttling	Limits CPU resources for a container/pod under load
Memory Pressure	Allocates memory aggressively to induce GC or crash scenarios
State Corruption / Replay	Re-sends stale messages or replays duplicate events to test idempotency

🧪 Sample Fault Plan Snippet¶

chaosPlan:
  traceId: proj-962-notify
  editionId: vetclinic-premium
  module: NotificationService
  injections:
    - type: latency
      target: EmailService
      delayMs: 800
      duration: 60s
    - type: http-error
      target: SmsGateway
      statusCode: 500
      rate: 0.4

🛠 Injection Tools Used¶

Tool	Purpose
`tc` (Traffic Control)	Latency, packet drop, network blackhole (Linux)
`Istio Fault Injection`	For service mesh-based latency and aborts
`Chaos Mesh` / `Litmus`	Pod/container kill, CPU/memory chaos
`Custom Proxy Injector`	Middleware that injects HTTP errors based on headers or routes
`Queue Chaos Adapter`	Manipulates queue behavior (drop, delay, burst) for Service Bus or Kafka

📦 Test Target Granularity¶

Target Level	Example
Endpoint	`/api/send-confirmation`
Service	`EmailService`, `InventoryService`
Queue	`notify-email-queue`
Host/Pod	`checkout-2-vm-12`, `pod/inventory-sync-5`
Edition	`vetclinic-premium` only (e.g., SMS logic path)

🧠 Chaos Plans Can Be:¶

Generated dynamically from trace plans
Manually authored by Prompt Architect or QA agents
Versioned by edition and service cluster
Simulated only in ephemeral testing environments or isolated tenants

📘 Example: Injecting Queue Delay¶

injection:
  type: queue-delay
  target: vetclinic/notify-sms-queue
  delayMs: 1500
  messagePattern: "*BookAppointment*"

🔐 Safety and Control Mechanisms¶

Guardrail	Description
🔄 Rollback timeout	Auto-clears fault injection after N seconds
🧪 Test-environment isolation	Never runs in production tenants or live clusters
🧯 Canary mode	Injects fault only for a small % of traffic or trace sample
⛔ Manual override	Dev/Studio agents can disable chaos during sensitive deployments

✅ Summary¶

The Resiliency & Chaos Agent injects controlled, versioned, trace-linked chaos into:

🔗 APIs, queues, pods, services, and flows
🌩️ Fault types including latency, errors, timeouts, message drops, saturation
🧠 Edition-aware environments and workloads
🧪 Coordinated chaos+load validation with retry and fallback detection

This enables real-world failure simulation and resilience scoring inside the ConnectSoft AI Software Factory.

📏 Validation Dimensions¶

This section defines the key dimensions the agent evaluates when assessing system resilience. These dimensions allow the agent to score, classify, and explain how well a service or workflow withstands injected chaos, infrastructure faults, and runtime anomalies.

🧪 Core Validation Dimensions¶

Dimension	Description
🧯 Fault Isolation	Whether the failure is contained within a bounded service/module (doesn’t cascade)
🛡 Fallback Activation	Whether defined fallbacks (graceful degradation, cached response, async retry) are triggered
🔁 Retry Policy Execution	Whether retries are invoked with correct delay/backoff; max retries enforced
🧠 Graceful Degradation	Whether user-facing workflows return valid partial response or helpful error
🕵️‍♂️ Failure Detection Latency	How quickly the system detects the fault (e.g., timeout, 5xx, async NACK)
⚡ Recovery Speed	Time to stabilize after failure resolved (from span, health check, telemetry)
🔌 Circuit Breaker Behavior	Whether circuit open/half-open transitions occurred properly
🧩 Fallback Coverage	Whether all critical paths have fallback enabled
📶 Service Health Impact	Whether downstream services were overloaded or destabilized
🔍 Telemetry Clarity	Whether logs, spans, metrics clearly describe the failure and resolution path

🎯 Sample Evaluation Scenario¶

Chaos Injected: Drop all responses from CRMService Observed Behavior:

BookingService used fallback → response returned with warning
Trace showed retry attempts with exponential backoff
Circuit breaker opened after 3 failures
Observability metrics reported alert in < 3s
Full recovery within 10s after fault removal

→ Score: High Resilience → Status: ✅ pass → Generated: resiliencyScore: 0.92

📘 Failure Isolation Matrix Example¶

Component	Expected Isolation	Result
`CRMService`	Isolated	✅
`BillingService`	Should be unaffected	✅
`NotificationService`	Delayed but recovered	⚠️ (tracked span delay: +1.2s)

🧠 Thresholds and Heuristics¶

Metric	Target
Recovery time < 10s	✅
Retry attempts ≤ max retries	✅
Fallback invoked	✅
Error exposed to user	❌ only if no fallback available
Unrelated service impacted	❌ indicates failure propagation

📊 Diagnostic Summary (Generated)¶

resiliencyAssessment:
  fallbackActivated: true
  retries: 2
  recoveryTimeMs: 7600
  impactedModules:
    - NotificationService (latency +1100ms)
  circuitBreakerState: triggered
  traceCoverage: complete
  status: pass
  resiliencyScore: 0.91

✅ Summary¶

The Resiliency & Chaos Agent validates systems using multidimensional criteria, including:

Fault isolation
Retry and fallback mechanics
Recovery time and circuit breakers
Impact containment
Trace and telemetry observability

This enables precise, explainable assessments of system resilience, even in complex distributed workflows.

🔁 Integration with Load & Performance Testing Agent¶

This section explains how the Resiliency & Chaos Agent coordinates with the Load & Performance Testing Agent to simulate real-world degradation under pressure — combining chaos injection and traffic stress to validate the system’s adaptive behaviors.

🔗 Coordinated Execution Strategy¶

The Resiliency & Chaos Agent can be configured to run:

Mode	Description
Sequential Mode	Load test runs → chaos injected after system reaches stable load
Concurrent Mode	Chaos and load run simultaneously (e.g., spike test + fault injection)
Recovery Follow-Up	Load test → chaos → observe recovery window and re-test stability
Preload / Post-chaos soak	Load ramps up or down around chaos window to observe impact trends

📘 Combined Test Scenario¶

traceId: proj-960-checkout
editionId: vetclinic-premium
chaosProfile: inject-service-timeout
linkedLoadTest:
  testType: spike
  rps: 250
  duration: 2m
resiliencyGoals:
  recoveryWithinMs: 3000
  maxAllowedErrorRate: 0.05

📊 Benefits of Load + Chaos Pairing¶

Test Goal	Agent Behavior
Detect failover bottlenecks under stress	Chaos disables `PrimaryEmailService` while Load Agent sends 200 RPS
Validate retries during queue surge	Load test inflates message queue depth, Chaos injects processing delay
Identify compound degradation patterns	Latency, CPU, and error metrics correlated with chaos window
Catch brittle fallback chains	Simultaneous chaos and high-concurrency load expose retry collapse or memory exhaustion

🤝 Coordination Details¶

Aspect	Behavior
Trace ID Sharing	Both agents use same `traceId` to correlate results
Timeline Sync	Chaos execution window defined relative to Load test duration
Metric Overlay	Performance and resiliency scores plotted on unified dashboard
Studio Tile Fusion	Shared preview tile for combo runs (e.g., "Chaos + Spike")
Shared Memory Logs	Results from both tests stored under unified trace key for historical diffing

🧠 Collaboration Examples¶

Scenario	Agents
Chaos: CPU throttling + Load: Soak	Detects memory leaks in retry logic under long execution
Chaos: Dependency timeout + Load: Concurrency	Detects circuit breaker misfires at 500+ concurrent sessions
Chaos: Queue delay + Load: async producer	Measures end-to-end async lag under pressure

📂 Output Artifacts in Coordinated Run¶

File	Owner	Description
`resilience-metrics.json`	Resiliency Agent	Chaos events + fallback analysis
`perf-metrics.json`	Load Agent	Latency, score, resource use
`combined-score.log.json`	Both	Overlay summary with unified traceId
`studio.resiliency.preview.json`	Resiliency Agent	UI tile with chaos + load metadata
`regression-alert.yaml`	Either	Emitted if SLOs breached under chaos

✅ Summary¶

The Resiliency & Chaos Agent integrates tightly with the Load & Performance Agent to:

🔁 Simulate realistic system failure during high load
🎯 Validate fault isolation, retry success, and recovery speed
📊 Correlate resiliency breakdowns with performance degradation
🤝 Produce unified outputs and trace-linked visualizations

Together, they enable multi-dimensional testing of fault tolerance under realistic operating conditions.

📡 Observability & Tracing Hooks¶

This section defines how the agent integrates with ConnectSoft’s observability stack to trace service behavior during chaos injection and validate fault recovery patterns. It observes both runtime telemetry and business flow recovery to determine the true resiliency of a system.

🧭 Core Observability Signals Tracked¶

Signal Type	Used For
OpenTelemetry Spans	Detects latency shifts, span retries, circuit breaker activations
App Insights Logs	Captures fallback attempts, unhandled exceptions, timeouts
Custom Metrics	`retry.count`, `fallback.success`, `queue.recovery.time`
Dependency Failures	Span and log analysis for failed HTTP/gRPC/DB calls
Dead-letter Queue Activity	Detects message loss patterns during chaos injection
CPU/Memory Drift	Detect resource leaks or non-recovered memory
Error Rate Trends	Tracks increase/decrease during injection windows
Recovery Latency	Time to stabilize after fault stops (measured via tail latency + error rate normalization)

📊 Span Metadata Tracked¶

Span Field	Description
`traceId`	Links chaos injection to all service behaviors
`component`	E.g., `NotificationService`, `AppointmentsService`
`span.status`	`error`, `ok`, `timeout`, `retrying`
`attributes.fallback`	True/False indicator for fallback path taken
`retry.count`	Count of retries triggered during span lifecycle
`breaker.open`	Flag indicating circuit breaker activation
`recovery.durationMs`	How long the service took to restore normal latency

📘 Example: Observed Span Breakdown¶

{
  "spanName": "EmailSender.Send()",
  "status": "ok",
  "attributes": {
    "retry.count": 2,
    "fallback": true,
    "breaker.open": false,
    "recovery.durationMs": 2400
  }
}

→ Indicates successful fallback via SMS channel, after 2 retries on email provider.

📥 Chaos Injection Events Traced¶

Injected timeout, latency, unavailable, drop span annotations
Wrapped spans receive a chaos.injectionId
Allows downstream spans and logs to correlate with chaos scenario

🔗 Trace Propagation¶

Mechanism	Description
`traceId` injection	Unique per test or chaos scenario
`chaosId` span attribute	Groups spans by injection instance
Studio trace viewer	Can filter by `traceId` + `chaosId` to explore effect paths
Span graph overlay	Highlights error spans, retries, degraded zones (in Studio or PDF report)

📤 Observability Outputs¶

File	Format	Purpose
`chaos-trace-summary.json`	JSON	Summarized view of system behavior under fault
`resiliency-metrics.json`	JSON	Key performance and recovery metrics during chaos
`flamegraph.svg`	SVG	CPU or path-time visualization during failure window
`trace-path.dot` or `.mmd`	Graph	Visual representation of the propagation of error or recovery

✅ Summary¶

The Resiliency & Chaos Agent integrates tightly with observability layers to:

📡 Trace chaos effects end-to-end
🧠 Detect fallback, retry, breaker, and recovery behavior
🔗 Correlate traces, logs, metrics to specific chaos events
📊 Feed Studio dashboards, score calculation, and root cause mapping

This ensures resilience is measured not only by failure injection, but by verified recovery and trace-aligned behavior.

📐 Policies and Thresholds¶

This section defines the policies, resiliency standards, and thresholds the agent uses to determine acceptable fault tolerance behavior, per service, flow, edition, and environment. These rules are central to classifying services as resilient, degraded, or fragile.

🧭 What Are Resiliency Policies?¶

Resiliency policies define expected behaviors under specific failure conditions. These are applied based on:

Service tier (critical, internal, async)
Edition (premium, lite, enterprise)
Flow type (synchronous API, async event, composite workflow)
Failure type (timeout, exception, message drop, etc.)

They are specified in configuration files or inferred from the architecture model.

📘 Example: `resiliency-policy.yaml`¶

module: AppointmentService
editionId: vetclinic-premium
policies:
  timeoutMs: 2000
  maxRetryCount: 3
  retryBackoff: exponential
  fallbackEnabled: true
  circuitBreaker:
    threshold: 0.5
    durationMs: 10000
    failureRateWindow: 10
  observability:
    requiresSpan: true
    traceErrorOnFailure: true
  chaosTolerance:
    latencySpikeThreshold: 30   # percent
    errorRateThreshold: 5       # percent

🧪 Agent Uses Policies To...¶

Action	Policy Reference
Simulate failure	Chaos test injects error, timeout, delay, or queue drop
Validate retries	Observes retry span count, exponential delay
Evaluate fallback	Confirms fallback service (e.g., cache, stub) activates
Check circuit breaker	Simulates repeated failure to observe circuit open/close
Check observability	Ensures span+log are emitted upon failure or recovery

🎯 Threshold Examples¶

Metric	Threshold
Retry success rate	≥ 90% for retriable flows
Fallback accuracy	≥ 95% correctness or coverage
Latency spike absorption	≤ 30% deviation under chaos
Error containment	Failure localized to no more than 1 service hop
Observability coverage	≥ 95% of faults generate span+log with cause and action

🧠 Policy-Sensitive Result Classification¶

Behavior	Result
Fallback works, retries succeed	✅ `resilient`
Fallback triggered but latency spikes 40%	⚠️ `fragile`
Retry storm (≥ 5 retries), CPU spikes	❌ `degraded`
Circuit never opens under failure	❌ `fail`
Fault spans not emitted	⚠️ `observability-missing`

📊 Studio Tile Mapping¶

Field	Source
`resiliencyScore`	Derived from policy adherence
`status`	`resilient`, `fragile`, `degraded`, `fail`
`policyDeviations`	List of broken rules or unmet expectations
`tileSummary`	`"Fallback succeeded, but latency ↑34%, CPU 90% under retry storm"`

📘 Example Result Summary¶

traceId: proj-955-notify
moduleId: NotificationService
editionId: vetclinic
resiliencyPolicyEvaluation:
  retry:
    attempted: 3
    successful: 2
    pattern: exponential
  fallback:
    triggered: true
    latencySpike: 34%
  circuitBreaker:
    opened: false
    expected: true
  observability:
    spanMissing: false
status: fragile
resiliencyScore: 0.76

✅ Summary¶

The Resiliency & Chaos Agent:

Applies per-module, per-edition resiliency policies
Evaluates behavior under controlled chaos
Detects gaps in retry, fallback, observability, containment
Outputs clear deviation maps and status classes

This ensures services are resilient-by-default and chaos-ready, with fully explainable and policy-driven scoring.

🧠 Recovery Pattern Detection¶

This section outlines how the agent detects and evaluates resilience mechanisms like fallback flows, retries, circuit breakers, graceful degradation, and autoscaling in response to injected chaos or observed failures.

🛠️ What Are Recovery Patterns?¶

Recovery patterns are behaviors a system exhibits in response to injected chaos. Examples include:

Pattern	Purpose
✅ Retry with Backoff	Automatically attempts operation again with increasing delays
⛔ Circuit Breaker	Temporarily blocks downstream requests to prevent overload
🔄 Failover	Routes traffic to backup service or node
🧭 Fallback Response	Returns a predefined degraded response (e.g., “please try again later”)
🚨 Graceful Degradation	Disables non-essential features while core remains functional
📈 Autoscaling	Reacts to pressure by allocating new instances or threads
🛑 Timeout & Drop	Cancels call after threshold, avoids system hang
🔔 Alerting & Telemetry	Emits custom events/logs when fault is encountered and recovered

🔍 Detection Techniques¶

Method	How It Works
🔁 Span correlation	Measures retry intervals, duplicate attempts across layers
🧭 Fallback route detection	Detects alternate HTTP status, endpoint, or degraded response
⛔ Circuit breaker event	Watches metrics for circuit open/close toggles via logs or spans
📉 Degraded service signature	Identifies 200 OK with reduced data payload or message body indicating fallback
⌛ Timeout events	Captured from OpenTelemetry spans exceeding timeout threshold
📈 Resource scale-out	Monitors system metrics for dynamic scaling or threadpool expansion
🧠 Observability traces	Matches fault → fallback → resolution paths in trace tree or flamegraph
📥 Custom headers or logs	e.g., `"X-Fallback-Used": true` or log event: `"recovered from failure"`

📘 Example: Detected Retry Pattern¶

recoveryPattern: retry-backoff
target: /checkout/process-payment
detected: true
attempts: 3
intervalsMs: [0, 100, 300]
result: success after retry
confidenceScore: 0.94

📘 Example: Fallback Detection in Trace¶

{
  "spanId": "notify-email",
  "statusCode": 200,
  "responseTag": "fallback-mode",
  "message": "Queued for manual delivery",
  "recoveryPattern": "fallback-response"
}

🧠 Scoring Recovery Behavior¶

Pattern	Bonus to resiliencyScore
Detected fallback (HTTP 2xx w/ fallback flag)	+0.1
Retries recovered error	+0.15
Circuit opened + auto-reset	+0.1
Graceful degraded experience sustained	+0.2
Alert/log emitted during fault and resolution	+0.05

🧪 Visual Flow (Trace Path)¶

graph TD
A[📤 Service Call] --> B[❌ Injected Failure]
B --> C[🔁 Retry Span 1]
C --> D[🔁 Retry Span 2]
D --> E[✅ Success]

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

🧾 Artifact Update: `resiliency.metrics.json`¶

{
  "traceId": "proj-987-checkout",
  "fallbackUsed": true,
  "retries": 2,
  "circuitBreakerActivated": false,
  "autoscaled": true,
  "gracefulDegradation": true
}

✅ Summary¶

The Resiliency & Chaos Agent actively detects recovery behaviors in the system, including:

🔁 Retried operations
🧭 Fallback paths
⛔ Circuit breaking
🚀 Autoscaling
📉 Graceful degradation
🔔 Recovery alerts/logs

This allows it to score not just failure survival, but intelligent system response — a key dimension of ConnectSoft's resilience architecture.

🏷️ Edition-Aware Chaos Profiles¶

This section defines how the Resiliency & Chaos Agent tailors its fault injection and validation strategies per edition or tenant context, ensuring realistic failure modes are tested within the capacity, risk tolerance, and recovery scope of each product tier.

🏷️ What is an Edition-Aware Chaos Profile?¶

An Edition-Aware Chaos Profile defines:

🔧 Which failures to simulate
⏱️ How aggressively to inject them
🧠 What recovery strategies are expected
📉 What the tolerable degradation levels are

Each edition may have different configurations depending on:

Edition	Constraints or Enhancements
`vetclinic`	Conservative failure injection, basic fallbacks only
`vetclinic-premium`	Enables retry logic and multi-layer fallback validation
`franchise-enterprise`	Allows pod/network chaos, autoscaling, circuit breaker stress tests
`multitenant-lite`	Minimal chaos injection due to shared infrastructure

📘 Example: Chaos Profile¶

editionId: vetclinic-premium
chaosProfile:
  enabled: true
  maxSeverity: medium
  faultTypes:
    - timeout
    - downstream-unavailable
    - partial-db-failure
    - async-delay
  retryPolicyExpected: true
  fallbackPolicyExpected: true
  circuitBreakerExpected: true
  allowServiceCrash: false

🧠 Policy Scope per Edition¶

Validation Area	Behavior
Retry logic	Required in `premium` and `enterprise` editions
Fallback support	Optional in `lite`, required in `premium`
Circuit breaker presence	Skipped in `vetclinic`, validated in `franchise-enterprise`
Chaos severity	`low` for `lite`, `high` allowed for enterprise

🧩 Edition-Specific Injection Scenarios¶

Edition	Chaos Types
`vetclinic`	Timeout injection, slow downstream API
`vetclinic-premium`	Delayed queue messages, database throttling, partial failure
`franchise-enterprise`	Service crash, memory saturation, failover node validation
`multitenant-lite`	Single-agent timeout, very mild chaos (e.g., injected delay only)

📊 Studio Implications¶

Tiles are grouped and color-coded by edition
Tiles with maxSeverity=high require green score for promotion
Edition-aware thresholds affect what resiliencyScore is required to pass
Dashboards compare resilience maturity per edition over time

🧠 Memory Segmentation¶

Previous chaos test memory entries are partitioned by editionId
Baseline expected responses (e.g., degraded retry response) are stored per edition
Allows edition-specific regression tracking (e.g., fallback removed accidentally)

✅ Summary¶

The Resiliency & Chaos Agent:

🔁 Customizes chaos injection per edition configuration
🧠 Applies realistic recovery expectations based on tenant SLAs
📊 Drives Studio insights and memory traces for each product tier
✅ Ensures no over-testing or under-testing based on capabilities or risk profile

This enables safe, scoped, and trust-aware resilience validation across all ConnectSoft SaaS deployments.

📊 Scoring Model (Resiliency Score)¶

This section defines the agent’s method for calculating a resiliencyScore — a numeric representation (range: 0.0 – 1.0) of how well a service or system resists, degrades, or recovers from faults during chaos tests. The score enables traceable, automated classification of resilience maturity across services, modules, and editions.

🎯 Purpose of the Score¶

Quantify fault-tolerance capabilities
Compare recoverability across builds or editions
Power CI/CD gates, Studio dashboards, regression alerts
Correlate resilience with performance (used jointly with performanceScore)
Drive architectural feedback (e.g., need for retries, fallbacks, timeout tuning)

📈 Resiliency Score Range¶

Score	Meaning
0.90–1.00	✅ Highly resilient: self-healing, transparent recovery
0.75–0.89	⚠️ Acceptable: partial fallback or graceful degradation
0.50–0.74	📉 Degraded: retries or timeouts work inconsistently
<0.50	❌ Fragile: system fails visibly or crashes under fault injection

🧮 Default Score Formula¶

resiliencyScore =
  0.35 * recoveryBehaviorScore +
  0.25 * fallbackAvailabilityScore +
  0.20 * errorSurfaceScore +
  0.10 * retryEffectivenessScore +
  0.10 * observabilityCompletenessScore

Each subscore is normalized between 0.0 – 1.0.

🧩 Component Breakdown¶

Component	Description
recoveryBehaviorScore	How fast and cleanly system returns to nominal after injected failure
fallbackAvailabilityScore	Whether a fallback route (cached data, stub response, alternate node) was used
errorSurfaceScore	How gracefully errors were surfaced (e.g., handled vs. 500/timeout)
retryEffectivenessScore	Did retry policies succeed under retryable errors
observabilityCompletenessScore	Were traces, logs, and metrics emitted during failure handling

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

{
  "resiliencyScore": 0.72,
  "status": "degraded",
  "scoreBreakdown": {
    "recoveryBehaviorScore": 0.66,
    "fallbackAvailabilityScore": 0.90,
    "errorSurfaceScore": 0.70,
    "retryEffectivenessScore": 0.55,
    "observabilityCompletenessScore": 0.80
  },
  "regression": true
}

📉 Regression Triggers¶

Drop of >15% in score vs. baseline
Failure to recover in a previously passing test scenario
Observability loss during failure (missing span, no log)
Retry failure on previously recovered operation

🧠 Memory-Based Scoring Context¶

The agent uses memory entries to:

Load last resiliencyScore for the same service/edition
Compare retry/fallback behavior changes
Mark score delta in Studio tile summary
Escalate to human or Dev agent if resilience is trending down

📊 Studio Tile Indicators¶

Field	Value
`resiliencyScore`	0.72
`status`	degraded
`regression`	true
`tileSummary`	“Fallback used, retry failed, error exposed. Score down 16%.”

✅ Summary¶

The Resiliency Score:

📊 Quantifies fault-tolerance maturity in a consistent, automated way
🔁 Informs retries, fallbacks, error surfaces, and self-healing checks
🧠 Compares test runs across time, editions, and modules
📈 Powers dashboards and triggers alerts for regression or drift

It gives ConnectSoft a first-class signal for service resilience, parallel to performanceScore.

⏱️ Retry, Delay, and Timeout Testing¶

This section outlines how the agent performs resilience testing on retry behavior, timeout policies, and delay handling, validating that ConnectSoft-generated services respond predictably under latency, flakiness, or partial failure — especially when interacting with downstream or async components.

🧪 Purpose of Retry/Timeout Testing¶

✅ Validate retry mechanisms (idempotent, bounded, exponential backoff)
🔁 Test retry loops under temporary failure
⏱️ Ensure timeouts are enforced (not infinite hangs)
📉 Observe impact of delays on caller services and user experience
🚦 Detect cascading failure patterns (e.g. no fallback → retries → crash)

🔁 Scenarios Simulated¶

Fault Type	Description
Downstream Unavailable	Target service or endpoint responds with 503 / timeout
Artificial Latency	Inject 500ms–5s delay into dependency
Flaky Retry	Return 2–3 failures before eventual success
Hanging Call	Force indefinite wait and ensure timeout fires
Queue Backpressure	Simulate slow consumer in async workflows

📋 Expected Behaviors¶

Pattern	Expected Response
Retry with backoff	2–3 retries spaced with increasing delay
Timeout triggered	Caller aborts after configured period (e.g. 2s)
Circuit breaker	Trips after N failures and rejects calls for cool-down
Fallback behavior	Secondary path or cached response used
Queue retries	Dead-lettering or delayed retries logged properly

🧠 Metrics Captured¶

Metric	Use
`retryCount`	Number of retry attempts observed in trace
`totalDurationMs`	Includes retry backoff + timeout overhead
`timeoutTriggered`	True/false per span or call
`circuitBreakerTripped`	Indicates system entered protected mode
`fallbackActivated`	True if downstream failure was masked by resilience strategy

📘 Example: Observed Span Behavior¶

{
  "spanName": "CheckInventory",
  "retryCount": 3,
  "timeoutTriggered": false,
  "fallbackActivated": true,
  "totalDurationMs": 980
}

📂 Artifacts Generated¶

File	Description
`resiliency-span-analysis.json`	Per-call span breakdown of retries, delays, fallbacks
`timeout-behavior.report.yaml`	Summary of timeout test scenarios and enforcement verification
`trace-summary.json`	Includes resilience markers in span trees
`studio.preview.json`	Updated with retry/timing anomalies per test target

🧠 Memory Usage¶

Stores prior retry count averages
Tracks deviation in timeout behavior over time
Highlights services that added or lost fallback behavior between builds

✅ Summary¶

The Resiliency & Chaos Agent performs specialized tests for:

🔁 Retries (backoff, bounded, success conditions)
⏱️ Timeouts (enforced within expected window)
🛑 Circuit breakers and fallback activation
📊 Observability markers in spans and metrics
📉 Alerts for retry storms, long retries, missing fallbacks

This ensures that ConnectSoft services degrade predictably and recover gracefully, preventing escalation into system-wide failure.

🖥️ Outputs to Studio¶

This section describes how the agent exports chaos test results, recovery observations, and scoring metadata to Studio, allowing human reviewers and other agents to visualize fault impact, recovery success, and service degradation paths.

🧾 Primary Studio Artifact: `studio.resiliency.preview.json`¶

Field	Description
`traceId`	Which test or execution trace the chaos was scoped to
`editionId`	Edition or tenant affected by the test
`moduleId`	Service or module being validated
`chaosType`	e.g., timeout, network loss, 500-injection, dependency kill
`resiliencyScore`	Final score after all validation (range: 0–1)
`status`	One of: `pass`, `warning`, `degraded`, `fail`
`recoveryDetected`	Boolean – whether fallback or retry was successfully observed
`tileSummary`	Human-readable 1-liner for dashboards
`actions`	Suggested next steps or developer annotations (e.g., fix timeout handling)

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

{
  "traceId": "proj-982-confirm-email-flow",
  "editionId": "vetclinic",
  "moduleId": "NotificationService",
  "chaosType": "dependency-timeout",
  "resiliencyScore": 0.73,
  "status": "warning",
  "recoveryDetected": true,
  "tileSummary": "Timeout injected in EmailProvider → retry after 2s succeeded.",
  "actions": ["view-trace", "annotate-recovery-pattern"]
}

🎯 Tile Behavior in Studio UI¶

Attribute	Behavior
`resiliencyScore`	Shows numeric badge (e.g., 0.73)
`status`	Badge color: green (pass), yellow (warn), red (fail)
`tileSummary`	Shown in list preview and full tile card
`actions`	Enables reviewer to inspect retry traces or comment on fallback paths
`chaosType`	Rendered as badge or icon (🕳️ network drop, ⌛ timeout, 🔥 kill)

📊 Studio Dashboards Enabled¶

Panel	Source
Resiliency Score Over Time	Trendline from `resiliencyScore` across editions/builds
Chaos Coverage Map	% of modules tested by chaos category
Recovery Pattern Tree	Tree of observed retry → fallback → manual recovery paths
Failure Mode Frequency	How often each chaos type caused failure or degradation
Edition Resilience Gaps	Compare score distribution across editions and modules

📘 Alert Behavior (Optional)¶

If resiliencyScore < 0.6 or no recovery was observed, the agent may emit:

regression-alert.yaml (flag for DeveloperAgent)
annotation-suggestion.yaml for Studio UI reviewers
Flags tile as needs-review if unhandled error path was observed

📎 Linked Artifacts to Studio¶

File	Use
`resiliency-metrics.json`	Full result and score per test type
`resiliency.trace-summary.json`	Injected chaos span + recovery paths with timing
`flamegraph.svg`	Optional — to show degraded recovery path
`studio.resiliency.preview.json`	Drives Studio tile and trace overlay
`recovery-pattern.map.yaml`	Documents fallback/retry branches observed (used in `trace-explorer`)

✅ Summary¶

The Resiliency & Chaos Agent exports:

📊 Trace-linked resilience previews for Studio
🎯 Recovery and fallback signal overlays
🟨 Visual status tiles for edition and module risk scoring
📁 Structured metrics for trend and gap analysis
🔁 Hooks into reviewer feedback and retry UI

This ensures that resilience validation becomes explainable, visible, and actionable inside Studio dashboards, closing the loop between chaos testing, agent scoring, and human oversight.

🤝 Collaboration Interfaces¶

This section outlines how the Resiliency & Chaos Agent collaborates with other agents and systems across the ConnectSoft AI Software Factory — from test coordination and chaos planning to Studio visualization, memory enrichment, and corrective feedback.

🔗 Key Collaborating Agents¶

Agent	Collaboration
QA Engineer Agent	Shares recovery test flows, failure assertions, .feature files
Load & Performance Agent	Runs coordinated chaos+load tests (spike while fault injected)
Developer Agent	Receives alerts, recovery gaps, and fallback issues via Studio preview
Studio Agent	Visualizes chaos outcomes, trace degradations, fallback paths
Knowledge Management Agent	Stores resiliency results, past incidents, recovery patterns
Bug Investigator Agent	Uses chaos output to explain non-deterministic bugs and flakiness
CI/CD Agent	Executes chaos validation steps during test or canary pipelines

🧭 Coordination with Load & Performance Agent¶

Scenario	Behavior
Inject chaos during spike	Load agent coordinates RPS → chaos agent injects latency/drop/timeout
Validate recovery speed	Chaos agent watches span recovery delay and stability after pressure
Scoring overlap	`resiliencyScore` and `performanceScore` together determine pass/fail in edge scenarios

📘 Collaboration Flow Diagram¶

flowchart TD
    LOAD[Load Agent]
    CHAOS[⚡ Resiliency Agent]
    QA[QA Agent]
    DEV[Developer Agent]
    STUDIO[Studio Agent]
    KM[Knowledge Agent]

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

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

📦 Shared Artifacts¶

Artifact	Used by
`resiliency-metrics.json`	Developer, QA, Studio
`chaos-trace-map.yaml`	Bug Investigator, Studio
`fallback-failure.yaml`	Developer, Studio
`studio.resilience.preview.json`	Studio, Human reviewers
`resiliency.memory.json`	Knowledge Management Agent

🤖 Event-Based Collaboration¶

Event	Triggered Action
`InjectChaosDuringSpike`	Both agents run test concurrently
`FallbackBroken`	Alert Developer Agent and Studio with retry chain failure
`RetryMismatch`	QA and Chaos agents flag incorrect retry config (e.g. missing exponential backoff)
`AutoHealedAfterDisruption`	Memory and Studio updated with `recovered: true` tag

👤 Human Interaction Hooks¶

Integration	Description
Studio Action Tile	Retry test, adjust chaos level, or request flamegraph
Developer Notification	Summary of resiliency issues (e.g., circuit breaker not tripped)
Test Planner Agent	Allows injecting recovery test steps into `.feature` flows

✅ Summary¶

The Resiliency & Chaos Agent collaborates by:

🔁 Coordinating chaos+load+test execution with QA and Load agents
📎 Exporting results to Studio, Dev, CI, and memory systems
📊 Powering dashboards and incident feedback loops
⚙️ Ensuring fallback, retry, timeout, and recovery are tested as a system

This agent serves as a resilience orchestrator, validating real-world recovery under pressure — across all software factory agents.

☢️ Failure Classifications¶

This section defines how the Resiliency & Chaos Agent classifies resilience-related failures and degradations, assigning them levels of severity and guiding next steps for response, retries, or escalation.

🚦 Classification Tiers¶

Classification	Meaning	CI/Studio Impact
✅ Resilient	Recovered as expected; fallbacks, retries, or circuit breakers worked cleanly	Marked as `pass`
⚠️ Recoverable with Warning	Partial degradation occurred (e.g., retry delay > expected), but functionality preserved	Marked as `warning`
📉 Degraded Recovery	Fallback succeeded but exceeded latency/error limits; user-visible slowdown	Marked as `degraded`, `resiliencyScore < 0.75`
❌ Unrecovered Failure	No fallback or retry occurred; system crashed, blocked, or leaked resources	Marked as `fail`, triggers alert
🚫 Catastrophic	Multiple services failed in cascade or data integrity was compromised	Triggers Studio-wide escalation, blocking CI/CD if gated

📎 Classification Heuristics¶

Failure Mode	Classification
✅ API returns 503, fallback returns 200	`resilient`
⚠️ Circuit breaker trips for 3s but resumes	`warning`
📉 Retry storm with high tail latency	`degraded`
❌ Queue consumer crashes on bad payload, no recovery	`fail`
🚫 Event loop stalls multiple services for >30s	`catastrophic`

📊 Metrics Used to Classify¶

Signal	Used for
Retry success rate	Determines resilience vs degradation
Retry delay duration	If retries succeed but exceed thresholds, mark as degraded
Latency after chaos injected	High latency = degraded fallback
Error rate post-injection	Indicates whether fallback reduced error volume
Span coverage / trace gaps	If request trace disappears = failure
CPU/memory leak	Indicates partial but unsustainable recovery

📘 Example Classification Output¶

resilienceClassification:
  traceId: proj-988-checkout
  editionId: vetclinic-premium
  status: degraded
  reason: "Fallback activated, but latency p95 = 1680ms (expected < 900)"
  recoveryPath: Retry + Fallback
  resiliencyScore: 0.66

🧠 Impact on Memory¶

Classification	Behavior
`pass`	Stored as new success baseline
`warning/degraded`	Stored as partial pass, may trigger flag in regression comparison
`fail`	Escalated and marked for intervention in memory
`catastrophic`	Triggers long-term memory flag for post-mortem storage and linkage to incident trace trees

👤 Human or Agent Actions Triggered¶

Classification	Suggestion
`warning`	Retry test with increased warmup
`degraded`	Suggest architectural refactor or stricter timeout
`fail`	Auto-annotate fallback config gaps and notify DeveloperAgent
`catastrophic`	Studio sends broadcast, blocks deploy, prompts ChaosReviewAgent for system-wide analysis

✅ Summary¶

The Resiliency & Chaos Agent:

📋 Classifies test outcomes into deterministic resilience statuses
🔁 Maps them to retry, fallback, or escalation flows
📊 Feeds Studio dashboards and CI/CD gates
🧠 Links results to long-term recovery confidence and chaos trends

This ensures clear decision support from chaos experiments — helping ConnectSoft agents and developers automate what to fix, rerun, or refactor.

🔁 Correction & Feedback Loops¶

This section outlines how the agent responds to resilience test failures or degradations, either by triggering correction workflows, notifying responsible agents/humans, or guiding automatic tuning of retry policies, fallback mechanisms, or service behaviors.

🧠 Why Correction Matters¶

Resilience issues aren't just bugs — they are often systemic risks that:

Appear under pressure or edge cases
Are recoverable through adaptive tuning or fallback logic
Require coordination across services or editions

This agent helps resolve them both automatically (via memory & retry feedback) and collaboratively (via Studio, Dev, QA agents).

🔁 Agent-Led Feedback Actions¶

Trigger	Correction Behavior
❌ `resiliencyScore < 0.6`	Emit `resilience-alert.yaml` and open Studio task
📉 Observed retry loop or unhandled fault	Generate `correction-plan.yaml` with fallback or timeout suggestions
🧠 Pattern match to memory	Suggest inherited fallback from similar module or edition
🚫 Absence of fallback in code	Notify DeveloperAgent with minimal resilience stub suggestion
🕵️ Detection of flapping	Flag ChaosAgent to re-run with backoff or latency chaos type

📘 Example: `correction-plan.yaml`¶

traceId: proj-974-notify
moduleId: NotificationService
failurePoint: EmailClient.Send()
observedBehavior: Retry loop without backoff
suggestedFix:
  - Add exponential retry with jitter
  - Cap retries at 3 with fallback to SMS
linkedDocs:
  - fallback-strategy.md
  - resiliency-patterns-library.json

🧑‍💻 Human Feedback Integration¶

Studio allows reviewers (Dev, Ops, QA) to:

Annotate failed resilience test (e.g. "we don’t support fallback here on purpose")
Approve or reject correction plan
Flag incident as known issue (linked to Jira or planning agent record)
Request re-test with tuned config (e.g., retry count increased)

📦 Feedback Outputs¶

Artifact	Purpose
`resilience-alert.yaml`	Summarizes root cause, affected module, and test result
`studio.resiliency.preview.json`	Updated tile with human comments and resolution actions
`retry-policy.patch.yaml` (optional)	Synthetic patch suggested for retry/backoff addition
`retest-request.yaml`	Trigger agent re-execution with alternate chaos profile or threshold tuning

🔁 Retry Feedback Support¶

If correction plan is applied (manual or automatic):

Agent re-runs the test (once or looped)
Tracks before/after resiliencyScore
Updates Studio preview to show improvement or continued failure
Pushes both results to memory for historical trend

📘 Example Studio Tile Update (After Correction)¶

{
  "resiliencyScore": 0.84,
  "status": "recovered",
  "tileSummary": "Retry logic added. Failure point auto-handled with fallback to SMS.",
  "correctionPlan": "Applied from memory suggestion",
  "retryCount": 1
}

✅ Summary¶

The Resiliency & Chaos Agent supports multi-path feedback and correction by:

📤 Emitting structured plans when resilience tests fail
🤖 Suggesting retry/fallback strategies automatically
🧠 Using memory to recommend prior fix patterns
📎 Enabling Studio reviewers to guide, approve, or escalate fix paths
🔁 Re-running tests to confirm remediation effectiveness

This makes resilience testing not just diagnostic — but also adaptive and self-correcting, enabling ConnectSoft systems to evolve toward greater fault tolerance over time.

🧠 Memory Use & Historical Trends¶

This section outlines how the agent leverages ConnectSoft’s memory layer to enhance its analysis of system resilience — tracking patterns over time, detecting regressions in recoverability, and providing continuous improvement insights across editions, modules, and workloads.

🧠 What the Agent Stores in Memory¶

Artifact	Description
`resiliency-metrics.memory.json`	Structured record of previous test results: chaos injection, recovery time, success rate, scoring
`recovery-paths.graph.yaml`	Observed fallback or retry paths per endpoint/event, stored as dependency graph
`resiliency-score.log.jsonl`	Historical log of calculated scores and degradation reasons
`failure-matrix.yaml`	Historical mapping of injected fault types → system behaviors (e.g., silent failure, retry, fallback)
`observability.coverage.memory.json`	Tracks which services exposed useful logs/traces/metrics under fault over time

📥 How the Agent Reads Memory¶

Upon chaos execution:

Loads previous test results by:
- moduleId
- editionId
- faultType
- traceId (if scoped to a user scenario)
Retrieves baseline resiliencyScore
Compares expected recovery paths and time-to-heal
Flags “resilience drift” if score or patterns deviate from prior trusted runs

🔁 Memory-Based Behavior Triggers¶

Condition	Agent Action
Regression in `resiliencyScore`	Emit `regression-alert.yaml`, notify DeveloperAgent
Missing fallback used to exist	Mark path as “regressed fallback”
Increased recovery time	Suggest retry delay increase or async queue decoupling
New fault survived	Promote to memory with `confidenceScore ≥ 0.9`
Repeat failure on known fault	Escalate as “Known Weakness Not Fixed”

📘 Memory Entry Example¶

{
  "traceId": "proj-911-checkout",
  "moduleId": "CheckoutService",
  "editionId": "vetclinic-premium",
  "faultType": "service-timeout",
  "resiliencyScore": 0.84,
  "fallbackActivated": true,
  "retryOccurred": true,
  "recoveryTimeMs": 920,
  "status": "pass",
  "timestamp": "2025-05-12T14:11:00Z"
}

📊 Trend Analysis Features¶

Trend Type	Purpose
Resilience Drift	Detect slow regression in retry quality, error recovery time, observability gaps
Recovery Time Distribution	Highlights which editions/modules consistently fail to recover quickly
Fallback Path Stability	Visualizes whether the same fallback strategy holds across versions
Fault-Type Sensitivity	Compares modules’ ability to handle fault types: timeout, dropped event, 500 errors, etc.

🧠 Memory Keys for Indexing¶

memoryKey:
  - moduleId
  - editionId
  - faultType
  - testType (e.g., chaos + spike)
  - traceId (optional)

✅ Summary¶

The Resiliency & Chaos Agent uses memory to:

🧠 Detect resilience regressions and highlight areas of concern
🔁 Compare fallback paths, retry outcomes, and healing trends
📊 Power Studio dashboards and analytics on fault tolerance over time
📎 Enable continuous improvement in system recovery design

This gives ConnectSoft a long-term memory for chaos, ensuring that fault tolerance doesn’t silently erode over time.

🧭 Final Blueprint & Future Roadmap¶

This final section summarizes the agent's architecture, confirms its responsibilities across the platform, and outlines future enhancements — enabling ever smarter, safer, and more autonomous resilience validation.

🧩 Blueprint Overview¶

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

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

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

🧠 Core Responsibilities Recap¶

Domain	Responsibility
🔧 Chaos Injection	Inject latency, error, restart, queue fault, CPU/memory pressure
🎯 Resilience Validation	Detect fallback behavior, retry policies, circuit breaker effectiveness
📉 Fault Impact Analysis	Evaluate service degradation or failure scope
📊 Score Output	Emit `resiliencyScore` with classification (resilient, partial, brittle)
🔗 Trace Enrichment	Capture span traces of failure and recovery
🧠 Memory Update	Log historical fault recovery performance
🖥️ Studio Visualization	Render summary tiles, regressions, fault trees
🔁 Retry Simulation	Simulate delayed retries, dropped messages, network timeout scenarios

✅ Delivered Outputs¶

Artifact	Purpose
`chaos-execution.log.jsonl`	Step-by-step fault injection, trace, and recovery checks
`resiliency-metrics.json`	Scores, impact, fallback trace, recovery time
`chaos.profile.yaml`	Defines which faults to inject by edition/module/testType
`regression-alert.yaml`	Raised if system fails to recover or scope of failure exceeds bounds
`studio.resiliency.preview.json`	Preview tile for Studio including trace, score, and fault class

🔭 Future Roadmap¶

Enhancement	Description
Fault Graph Inference	Automatically infer service dependencies and inject upstream/downstream chaos
AI-Generated Chaos Plans	Use Prompt Architect Agent to generate fault plans from architectural intent
Multi-Tier Cascade Detection	Detect chain reactions and global impact from localized chaos
Retry Pattern Coverage Index	Map all flows with/without proper retries and fallback blocks
Adaptive Fault Tuning	Increase or reduce chaos intensity based on past resilience success/failure
Secure Chaos for Tenants	Tenant-isolated resilience testing with synthetic identities and scoped fault domains
Chaos Regression Trends	Track how resilience improves over time per service/edition
Auto-Backpressure Injection	Simulate client slowdown or load spike to test queue and retry overload handling

🧠 Positioning in Platform¶

The Resiliency & Chaos Agent is ConnectSoft’s fault-tolerance sentinel. It ensures services can fail gracefully, degrade predictably, and recover rapidly — validating that all SaaS flows are prepared for the real world.

🎓 Final Summary¶

The agent:

💥 Injects targeted, edition-aware faults
📈 Measures real-world resilience with scoring
🧠 Tracks patterns of degradation and recovery
🖥️ Visualizes risk, scope, and recovery in Studio
🔁 Coordinates retries, fallback detection, circuit verification
🔗 Connects chaos outcomes to observability, memory, and human review

With this, ConnectSoft can automatically validate operational resilience across services, tenants, and runtime conditions — ensuring safe evolution at scale.

⚡ Resiliency & Chaos Agent Specification¶

🎯 Purpose¶

🧠 Strategic Role in the ConnectSoft Factory¶

🌐 Example Use Cases¶

🎯 Why It’s Essential¶

🧭 Position in Platform¶

✅ Summary¶

📋 Responsibilities¶

✅ Core Responsibilities¶

📘 Example Agent Use Cases¶

🧠 Coordination Responsibilities¶

✅ Summary¶

📥 Inputs Consumed¶

📂 Core Input Artifacts¶

🔁 Dynamic Context from Execution Environment¶

📘 Example: resiliency-profile.yaml¶

📘 Example: fallback-rules.yaml¶

📘 Example: resilience-thresholds.yaml¶

🧠 Inputs from Memory¶

🔎 Additional Runtime Inputs¶

✅ Summary¶

📤 Outputs Produced¶

📦 Core Output Artifacts¶

📘 Example: resilience-metrics.json¶

📘 Example: chaos-injection.trace.yaml¶

📘 Example: studio.resilience.preview.json¶

📘 Optional: fallback-path.map.yaml¶

🔎 Trace-Linked Annotations¶

📊 Used By Studio For¶

✅ Summary¶

🔁 Execution Flow¶

🔁 High-Level Flow Overview¶

📋 Step-by-Step Execution¶

1. Trigger Agent Execution¶

2. Load Target Metadata¶

3. Generate Chaos Plan¶

4. Inject Faults¶

5. Monitor System Behavior¶

6. Capture Telemetry¶

7. Classify Response¶

8. Score and Summarize¶

9. Emit Artifacts¶

📥 Inputs Required¶

📤 Outputs Produced¶

✅ Summary¶

💥 Chaos Injection Methods¶

💣 Chaos Injection Categories¶

🧪 Sample Fault Plan Snippet¶

🛠 Injection Tools Used¶

📦 Test Target Granularity¶

🧠 Chaos Plans Can Be:¶

📘 Example: Injecting Queue Delay¶

🔐 Safety and Control Mechanisms¶

✅ Summary¶

📏 Validation Dimensions¶

🧪 Core Validation Dimensions¶

🎯 Sample Evaluation Scenario¶

📘 Failure Isolation Matrix Example¶

🧠 Thresholds and Heuristics¶

📊 Diagnostic Summary (Generated)¶

✅ Summary¶

🔁 Integration with Load & Performance Testing Agent¶

🔗 Coordinated Execution Strategy¶

📘 Combined Test Scenario¶

📊 Benefits of Load + Chaos Pairing¶

🤝 Coordination Details¶

🧠 Collaboration Examples¶

📂 Output Artifacts in Coordinated Run¶

✅ Summary¶

📡 Observability & Tracing Hooks¶

🧭 Core Observability Signals Tracked¶

📊 Span Metadata Tracked¶

📘 Example: Observed Span Breakdown¶

📥 Chaos Injection Events Traced¶

🔗 Trace Propagation¶

📤 Observability Outputs¶

✅ Summary¶

📐 Policies and Thresholds¶

🧭 What Are Resiliency Policies?¶

📘 Example: resiliency-policy.yaml¶

📘 Example: `resiliency-profile.yaml`¶

📘 Example: `fallback-rules.yaml`¶

📘 Example: `resilience-thresholds.yaml`¶

📘 Example: `resilience-metrics.json`¶

📘 Example: `chaos-injection.trace.yaml`¶

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

📘 Optional: `fallback-path.map.yaml`¶

📘 Example: `resiliency-policy.yaml`¶

🧾 Artifact Update: `resiliency.metrics.json`¶

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

🧾 Primary Studio Artifact: `studio.resiliency.preview.json`¶

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

📘 Example: `correction-plan.yaml`¶