🧪 A/B Testing and Experimentation Agent¶

🎯 Purpose¶

The A/B Testing and Experimentation Agent is responsible for transforming high-level growth hypotheses and product variations into structured, executable experiments. It validates ideas proposed by agents such as the Growth Strategist, Marketing Specialist, and Customer Success Agent — ensuring that every strategy is measurable, controlled, and supported by statistically reliable evidence.

🧠 Core Goal¶

This agent operationalizes the principle:

“If we can’t measure it, we can’t improve it.”

It provides a formal mechanism to test feature changes, messaging, flows, or incentives before fully committing to rollout — allowing ConnectSoft-built SaaS products to learn before scaling.

🔍 Key Roles in the ConnectSoft AI Factory¶

Acts as the execution bridge between strategic hypotheses and telemetry feedback
Generates multi-variant experiments across UI, messaging, onboarding, pricing, incentives, and more
Ensures all experiments are versioned, attributed, and tracked
Automates A/B test lifecycle management, from setup → execution → result ingestion
Embeds results into long-term memory for future strategy conditioning

📦 What This Agent Enables¶

Capability	Description
🔁 Loop Testing	Helps test onboarding, referral, upsell, and other growth loops
📈 KPI Validation	Validates impact on `activation_rate`, `retention_7d`, `conversion_rate`, etc.
🧪 Multi-Variant Strategy	Supports testing multiple options simultaneously
🧠 Memory-Driven Test Avoidance	Avoids redundant or previously failed experiments
📊 Observability Integration	Connects to KPI pipelines and telemetry layers

🎯 Without It…¶

Growth hypotheses would launch unvalidated
Editions would be exposed to risk-prone experiments
No feedback loop would exist between idea and impact
Strategic decision-making would regress to guesswork

The A/B Testing Agent turns every hypothesis into a controlled learning opportunity.

🧠 Core Role in the ConnectSoft Factory¶

The A/B Testing and Experimentation Agent is the validation engine of the ConnectSoft AI Software Factory — ensuring that any strategic idea, growth hypothesis, or UX adjustment is tested in a measurable, statistically valid, and feedback-looped manner before being adopted or scaled.

🧬 Positioned at the Intersection of Strategy and Observability¶

It acts as the bridge between creation and confirmation:

Before: Receives strategy YAMLs and hypotheses from the Growth Strategist or Marketing Specialist Agent
During: Defines, configures, and launches experiments (variants, KPIs, rollout policies)
After: Listens to telemetry (via Observability Agent) and feeds results back into memory

🧭 Flow Positioning¶

Input From	Role
Growth Strategist Agent	Receives structured hypotheses + target KPIs
Marketing Specialist Agent	Variant options, headlines, email subjects, CTAs, etc.
Customer Success Agent	Retention-focused experiments, surveys, user-facing flows

Output To	Role
Observability Agent	Push test telemetry configuration and receive result feedback
Memory System	Store results, variant outcomes, performance scores
Growth Strategist Agent	Replay test results for future strategy generation

🔁 Position in Agent Execution Flow¶

flowchart TD
    GS[Growth Strategist Agent] -->|Hypothesis & Loop Blueprint| AB[A/B Testing Agent]
    MSA[Marketing Specialist Agent] -->|Copy & Variant Suggestions| AB
    AB -->|Experiment Config| OBS[Observability Agent]
    OBS -->|KPI Movement| AB
    AB -->|Results + Score| GS
    AB -->|Outcome Embedding| Memory

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

🧩 Lifecycle Coordination¶

Phase	Description
Design	Generates test structure from inputs
Launch	Emits structured test definition
Observe	Captures test data and metric telemetry
Analyze	Validates significance, calculates uplift
Learn	Feeds outcomes to memory, triggers recommendations

🏗️ Operates Across¶

🧪 Onboarding Flow Variants
💬 Marketing Message Experiments
🛒 Pricing Page CTA Adjustments
📧 Email Sequence Tests
📦 Edition-Specific Rollout Comparisons
🧭 UX Microinteraction Variants

✅ Summary¶

The A/B Testing Agent is not an analytics tool — it is a learning orchestrator.

It brings scientific rigor to software evolution.
It turns strategy into controlled experiments.
It closes the loop between ideas and outcomes.

🧩 Cluster Placement and Positioning¶

The A/B Testing and Experimentation Agent is part of the Growth, Marketing & Customer Success Cluster within the ConnectSoft AI Software Factory. It is positioned as the testing and validation engine of the growth lifecycle — enabling safe iteration, evidence-backed rollout, and continual refinement of product strategies.

📦 Cluster and Layer Placement¶

Layer	Cluster	Role Description
🎯 Execution Engine	Growth, Marketing & CS	Executes structured A/B tests for strategies and user-facing flows
🧠 Feedback Linker	Telemetry and Observability	Captures result metrics and links them back to originating hypotheses
🧪 Scientific Core	Experimentation Sub-Layer	Applies experimental design principles to every agent-proposed hypothesis

🔁 Execution Timeline Within the Factory¶

Stage	Agent or Component Involved	Description
🎯 Hypothesis Created	Growth Strategist Agent	Defines measurable growth idea
🧩 Variants Proposed	Marketing Specialist Agent	Generates content, messaging, or UI options
🧪 Test Constructed	A/B Testing Agent	Builds experiment from hypotheses and variants
📡 Test Launched	Observability Agent + Runtime Instrumentation	Routes data and sets up telemetry
📊 Result Collected	Observability Agent	Monitors KPI and variant performance
🧠 Outcome Persisted	Memory and Growth Strategist Agent	Stores test result and conditions future strategies

🧱 Functional Role in Cluster Map¶

flowchart TB
    subgraph GROWTH STRATEGY
        GS[Growth Strategist Agent]
    end

    subgraph EXPERIMENTATION ENGINE
        AB[A/B Testing Agent]
    end

    subgraph MARKETING DESIGN
        MSA[Marketing Specialist Agent]
    end

    subgraph TELEMETRY AND LEARNING
        OBS[Observability Agent]
        MEM[Memory Graph]
    end

    GS --> AB
    MSA --> AB
    AB --> OBS
    OBS --> AB
    AB --> MEM
    AB --> GS

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

🧩 Sub-Cluster: Experimentation Layer¶

The A/B Testing Agent is the only required testing orchestrator in the ConnectSoft Factory. It supports:

Variant design
Control/test configuration
Traffic split simulation
Result ingestion
Statistical significance validation
Rollback planning

🧠 Specialized Positioning¶

Type	Description
🔍 Validator	Validates that hypotheses are grounded in statistically sound methods
🔄 Integrator	Bridges upstream agents (strategists, marketers) and downstream data ops
🧪 Generator	Generates structured, executable test blueprints

✅ Summary¶

The A/B Testing and Experimentation Agent is deeply embedded in the Growth Intelligence Loop, ensuring that:

Every campaign, change, or idea is testable
Every test is measurable
Every result is learned from

Without it, ConnectSoft would ship software without knowing what works.

🚀 Strategic Contribution¶

The A/B Testing and Experimentation Agent is a critical enabler of evidence-based growth in the ConnectSoft AI Software Factory. It provides the infrastructure and intelligence to move from assumptions to validated outcomes — making every strategic or product change a source of compounding knowledge.

📈 Why This Agent Matters¶

Strategic Vector	Contribution
🧪 Hypothesis Validation	Converts unproven ideas into controlled tests that produce measurable outcomes
📊 Data-Driven Culture	Embeds statistical rigor into every growth decision
🔄 Continuous Learning Loop	Enables tight feedback between strategy, rollout, and telemetry
📉 Risk Mitigation	Prevents unvalidated experiments from damaging the user experience or KPIs
📦 Edition-Specific Precision	Tests ideas per edition and persona to avoid one-size-fits-all approaches

🔁 Impact on Factory-Wide Outcomes¶

Without This Agent	With This Agent
Guesswork-based decisions	Validated, hypothesis-driven evolution
Risk of global rollouts with negative impact	Controlled exposure and rollback policies
No tracking of idea efficacy	Structured test histories and outcome-based memory
Redundant or repeated experiments	Memory-powered deduplication and performance scoring

🧬 Strategic Leverage¶

The agent improves every loop in the system by answering:

✅ Did the onboarding checklist improve activation?
✅ Which subject line improved open rates for new users?
✅ Which CTA boosted trial-to-paid conversion in the enterprise edition?
✅ Is the new UI layout driving more feature adoption or causing confusion?

🔍 Test Everywhere, Learn Anywhere¶

It allows for:

UI/UX microinteraction testing
Funnel-stage experiments (awareness → conversion → retention)
Marketing message and channel A/B testing
Lifecycle journey optimizations
Edition-specific rollout comparisons
Post-NPS or churn-triggered experiments

🧠 Knowledge Compounding¶

Each test becomes a data point in ConnectSoft’s collective memory:

Variant success/failure is traceable to a hypothesis
Tests are linked to personas and editions
Success metrics become recommendation fuel for the Growth Strategist Agent

✅ Summary¶

The A/B Testing Agent transforms every part of the factory into a scientific growth engine. Its strategic contribution lies not just in measuring, but in:

Guiding change with confidence
Accelerating iteration cycles
Avoiding repeat mistakes
Creating a reusable knowledge graph of what works — and why

Without it, growth becomes a shot in the dark. With it, growth becomes a discipline.

⚡ Activation Triggers¶

The A/B Testing and Experimentation Agent activates when a hypothesis, campaign variation, or product experiment is ready for structured validation. It listens for upstream events in the ConnectSoft Factory and evaluates whether conditions are met for test construction and rollout orchestration.

🔔 Trigger Sources¶

Triggering Agent / System	Trigger Event	Description
Growth Strategist Agent	`HypothesisGenerated`	A validated growth hypothesis is published in YAML format
Marketing Specialist Agent	`VariantReady`	Multiple content or UI variants (e.g., headline, CTA, layout) available
Customer Success Agent	`RetentionExperimentSuggested`	An idea to reduce churn or re-engage users is submitted
Product Owner Agent	`FeatureFlagged`	A feature is gated behind flags and eligible for exposure testing
Observability Agent	`SignalDipDetected`	A KPI degradation triggers automatic candidate test construction
User Feedback Ingestion	`NegativeSentimentClustered`	NLP or NPS analysis identifies issues in a feature or flow

🧠 Smart Trigger Inference (Optional)¶

In addition to event-based triggers, the agent can self-activate based on internal logic:

Trigger Logic	Example
⏳ Recurring Time Window	“Run retention uplift tests every 30 days for all editions”
🔁 Loop Saturation Detected	“Growth loop variant A has hit plateau – time to test new variant B”
📉 KPI Threshold Breach	“Activation dropped >15% in Startup edition after UI rollout”
📦 Edition-Specific Coverage Gap	“No tests have been run for Enterprise trial conversion this quarter”

🎛️ Trigger Configuration Example (YAML)¶

trigger:
  type: HypothesisGenerated
  source: growth-strategist-agent
  persona: startup_founder_hr
  edition: pro
  feature: onboarding_checklist
  primary_kpi: activation_rate
  test_window_days: 14
  rollout_percentage: 50

🧭 Dependency Check Before Activation¶

Before proceeding, the agent validates presence of required inputs:

🎯 At least one testable hypothesis or variant
📊 Linked KPI definition and expected delta
📦 Target persona + edition context
🧪 No conflicting tests currently active for same scope

🔁 Retry Logic (If Blocked)¶

Condition	Action
Missing input(s)	Wait and re-check every X minutes, or request clarification via parent agent
Test collision or override needed	Alert orchestrator for manual approval or suggest alternate variant
Invalid trigger parameters	Log as rejected test and send reason back to source agent

✅ Summary¶

The A/B Testing Agent doesn’t run blindly — it waits for valid, structured signals from trusted sources. Activation is governed by:

✅ Hypothesis maturity
✅ Input completeness
✅ Edition/persona relevance
✅ Safe execution windows

Its job is to say: “Now is the right time to test — and here’s exactly how.”

📋 Responsibilities¶

The A/B Testing and Experimentation Agent is responsible for the entire lifecycle of experiments — from parsing hypotheses to emitting telemetry-linked variant definitions, validating results, and persisting learnings.

It is not a passive receiver — it actively manages:

✅ Experiment construction
✅ Exposure logic
✅ Telemetry binding
✅ Statistical result interpretation
✅ Traceable memory integration

🧪 Core Responsibilities¶

Responsibility	Description
🔍 Hypothesis Parsing	Interpret input YAML or prompt into measurable statements
🧠 Variant Mapping	Convert options into testable variants (e.g., A vs B vs control)
📦 Experiment Blueprint Generation	Create YAML output describing the test configuration
🎛️ Exposure Configuration	Define rollout strategy (percentage, duration, persona/edition targeting)
📡 Telemetry Instrumentation Binding	Link test variants to KPI observability signals
🧾 KPI Mapping and Metadata Tagging	Label all variants with metrics, test IDs, personas, editions
🧪 A/A and A/B Pattern Detection	Detect baseline drift and false positives
📊 Significance Validation	Apply Bayesian or frequentist validation on test results
🔄 Result Feedback and Scoring	Attach outcomes to strategy memory graph and confidence weights
🧠 Memory Deduplication Logic	Avoid tests that have been run with similar context, persona, edition
🔐 Audit Trail Generation	Emit structured test logs for governance, rollback, and reproducibility

📈 KPI Types Supported¶

Type	Description
`activation_rate`	% of users completing onboarding steps
`trial_to_paid`	% of trial users who become paying customers
`feature_adoption`	Engagement with a specific feature or module
`retention_7d/30d`	% of users returning after X days
`click_through_rate`	% of users interacting with a call-to-action (CTA)
`open_rate`	Email/notification open performance
`nps_delta`	Net Promoter Score movement pre/post exposure

🔄 Example Test Management Lifecycle¶

1. Receive strategy: Variant A (default UI), Variant B (Checklist Onboarding)
2. Construct experiment blueprint with 50/50 traffic split
3. Bind to KPI: activation_rate
4. Launch telemetry hooks via Observability Agent
5. Monitor traffic + conversion data
6. Validate uplift: Variant B improves activation by +12.8%
7. Store result and mark strategy as 'validated'

📦 Output Responsibilities¶

Emit full experiment blueprint (.yaml)
Emit registration metadata for telemetry binding
Update memory graph with test lineage and result
Notify Growth Strategist Agent of final confidence score

✅ Summary¶

This agent is not just a test generator — it is a scientific orchestrator. Its responsibilities include:

End-to-end automation of experiment setup and tracking
Full alignment to KPI measurement logic
Traceable learning and non-repetition through memory

It brings industrial-grade test discipline to the factory’s autonomous strategies.

🔽 Inputs¶

The A/B Testing and Experimentation Agent operates on a rich, structured input set composed of strategic context, experimentable variants, KPIs, and edition/persona targeting. Inputs may come directly via events or as linked memory items from other agents in the ConnectSoft Factory.

📥 Primary Input Channels¶

Source Agent	Input Type	Description
Growth Strategist Agent	`GrowthHypothesisBlueprint`	YAML file defining hypothesis, KPIs, reasoning trace, and test window
Marketing Specialist Agent	`VariantSet`	Set of content variations (subject lines, CTAs, landing pages, etc.)
Customer Success Agent	`RetentionExperiment`	Flow or message variants aimed at reducing churn or improving engagement
Product Owner Agent	`FeatureFlagTargeting`	Flags and rules that allow segment-based feature exposure
Observability Agent	`MetricSignal`	KPI anomalies or thresholds triggering test necessity

📎 Example Growth Hypothesis Input¶

hypothesis_id: hyp-onboarding-01
persona_id: startup_founder_hr
edition: pro
hypothesis: >
  Users who follow a task-based onboarding checklist will activate faster than those dropped into the default dashboard.

primary_kpi: activation_rate
test_window_days: 14
variants:
  - name: checklist_ui
    description: Onboarding flow with visual task list
  - name: default_dashboard
    description: Standard product dashboard
targeting:
  rollout_percentage: 50
  control_group: true

🧠 Input Categories¶

Category	Example(s)
🎯 Hypotheses	Behavioral theories and expected outcome predictions
🧪 Variants	Content or UX changes to compare
📊 KPIs	Target metrics to validate test success
👥 Targeting Rules	Persona, edition, region, trial stage
⏱️ Timing	Exposure window, test duration, time-based segmentation
🔁 Previous Tests	Memory-linked references to avoid repetition

🧩 Implicit Inputs (from Memory)¶

Input	Purpose
Test lineage trace	Prevent testing same hypothesis multiple times
Variant effectiveness history	Reuse high-performing elements in new test setups
Edition-specific performance	Adjust rollout thresholds based on risk appetite

❗Input Validation Rules¶

Rule	Enforced Behavior
Must include at least 2 variants	Otherwise agent logs “insufficient variant input”
KPIs must be registered + observable	Otherwise agent waits for Observability Agent to define hooks
Persona/edition must be scoped	Or experiment is blocked due to undefined targeting
Hypothesis trace must be linked	Enables scoring and memory update on test conclusion

✅ Summary¶

The A/B Testing Agent doesn’t create out of nothing — it synthesizes from clear, structured inputs:

📄 YAML hypotheses
🧩 Marketing variant suggestions
🎯 KPI targets
👥 Targeting filters
🔁 Memory constraints

The quality of the test starts with the clarity of the input.

📤 Outputs¶

The A/B Testing and Experimentation Agent produces structured, machine-executable test definitions, traceable results, and memory updates that fuel future growth strategies. Its outputs are designed to be consumed by telemetry engines, orchestration agents, and long-term memory subsystems.

📦 Primary Output Artifacts¶

Output Type	Description
🧪 `ExperimentBlueprint.yaml`	Declarative test specification, including KPIs, variants, targeting rules
🧠 `MemoryUpdateRecord`	Embeds outcome into test lineage graph with result confidence
📊 `MetricBindingDefinition`	Binds each variant to KPIs monitored by the Observability Agent
🔁 `TestExecutionRequest`	Signals test runners to initiate exposure (real or simulated)
📥 `FeedbackToSourceAgent`	Summary + confidence score sent back to Growth Strategist or CS Agent
🗂 `AuditLogEntry`	Structured log of test definition, execution, and result for traceability

🧾 Example: `ExperimentBlueprint.yaml`¶

experiment_id: exp-202406-ab-001
hypothesis_id: hyp-onboarding-checklist-2024
persona: startup_founder_hr
edition: pro
variants:
  - id: variant_a
    name: checklist_ui
    control: false
  - id: variant_b
    name: default_dashboard
    control: true
rollout:
  exposure: 50
  control_group: true
  duration_days: 14
kpis:
  - activation_rate
  - time_to_first_action
instrumentation:
  telemetry_bindings:
    activation_rate: metric://onboarding/activation

🧠 Memory Entry Output¶

{
  "experiment_id": "exp-202406-ab-001",
  "persona": "startup_founder_hr",
  "edition": "pro",
  "hypothesis_id": "hyp-onboarding-checklist-2024",
  "variant_winner": "checklist_ui",
  "uplift_percent": 12.3,
  "confidence_score": 0.92,
  "test_window": "2024-06-01 to 2024-06-15"
}

🔄 Result Distribution¶

Destination	Purpose
Observability Agent	For KPI collection and statistical validation
Growth Strategist Agent	To influence next iteration of strategic blueprints
Memory Vector DB	To be retrieved in similar future strategy generation
Audit Trail Store	To allow governance, reproducibility, or rollback

🧭 Format Characteristics¶

YAML for blueprints (human-readable, CI/CD friendly)
JSON for telemetry feedback and memory records
Markdown-formatted summaries for human-in-the-loop feedback (optional)
Tagging metadata for edition, persona, release window, and hypothesis lineage

✅ Summary¶

Outputs from this agent are:

🔗 Traceable — linked to origin hypothesis and edition/persona context
🧪 Executable — ready to be consumed by systems that manage rollout and measurement
🧠 Memorable — structured for long-term recall and strategy reuse
📊 Auditable — structured logs for compliance and rollback

The output is not just a test — it’s a scientific artifact in the factory’s growth engine.

🔁 Process Flow Overview¶

The A/B Testing and Experimentation Agent follows a deterministic, multi-phase execution pipeline to ensure that every experiment is structured, validated, traceable, and connected to downstream learning loops.

This flow guarantees autonomy, repeatability, and observability across the lifecycle of A/B and multivariate tests.

🧪 High-Level Lifecycle¶

1. Receive trigger event
2. Validate inputs (variants, KPIs, targeting, edition/persona)
3. Generate test blueprint
4. Bind telemetry and schedule rollout
5. Monitor KPI signals during execution window
6. Validate uplift and calculate confidence
7. Emit results, update memory, notify upstream agents

🧭 Detailed Phase Flow¶

Phase	Description
1. Initialization	Parse and validate hypothesis, persona, edition, and variant sets
2. Eligibility Check	Ensure no conflicting experiment exists, required KPIs are observable
3. Blueprint Synthesis	Generate complete YAML experiment spec with telemetry bindings
4. Execution Trigger	Send instructions to Observability Agent or A/B test runner module
5. Monitoring Phase	Await signals and metric deltas from Observability Agent
6. Validation Phase	Calculate statistical significance, uplift percentage, and winner variant
7. Feedback Emission	Notify Growth Strategist or source agent, and emit memory updates
8. Memory Persistence	Store experiment lineage, result, and metadata in the factory’s memory graph

🔂 Process Flow Diagram¶

flowchart TD
    EVT[Trigger Event]
    EVT --> VAL[Input Validation]
    VAL --> SYN[Generate Blueprint]
    SYN --> REG[Register Telemetry]
    REG --> RUN[Trigger Execution]
    RUN --> MON[Monitor Signals]
    MON --> VAL2[Validate Results]
    VAL2 --> OUT[Emit Results + Memory]
    OUT --> NOTIF[Notify Source Agent]

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

🛑 Error Branches and Loopbacks¶

Condition	Action Taken
Missing KPI/telemetry binding	Retry registration or request Observability Agent support
No variants defined	Send error upstream to Marketing Specialist or Growth Strategist
Prior similar test found	Abort, attach memory reference, and notify of redundancy
Invalid YAML structure	Auto-correct or log for human intervention
KPI signal delay	Retry at exponential intervals during test window

🧠 Self-Regulation¶

✅ Stateless execution per run
✅ Deterministic output structure
✅ Feedback-controlled memory embedding
✅ Confidence scoring based on actual uplift and data volume

✅ Summary¶

The A/B Testing Agent follows a modular, auditable pipeline that transforms raw ideas into validated learnings:

Each phase is scoped, observable, and recoverable
All data flows are traceable across the agent network
The output is learning, not just logging

Its process is what makes the ConnectSoft Factory scientifically scalable.

🧠 Skills and Kernel Functions¶

The A/B Testing and Experimentation Agent uses a set of Semantic Kernel skills and functions to parse hypotheses, generate blueprints, calculate uplift, validate significance, and interact with other agents. These skills are modular, reusable, and extensible — supporting both A/B and multivariate experimentation workflows.

🧩 Core Skill Categories¶

Skill Category	Description
📄 Blueprint Generation	Transforms input hypothesis and variant set into structured YAML output
🧪 Variant Comparison Logic	Maps variants to control/test format, assigns tracking IDs
🎯 KPI Mapping	Aligns each variant to measurable KPIs using telemetry references
📊 Significance Estimation	Calculates statistical uplift and p-value / confidence score
🧠 Memory Deduplication	Searches vector store for prior similar tests to avoid redundancy
📥 Observability Binding	Emits structured test bindings for KPI monitoring and metric routers
🔁 Feedback Construction	Summarizes results into structured memory updates and agent notifications

🧠 Kernel Skills Used¶

Skill Name	Function	Description
`hypothesis-parser`	`ParseHypothesisYamlAsync`	Parses YAML from Growth Strategist Agent into hypothesis object
`variant-normalizer`	`NormalizeVariantsForTestAsync`	Ensures test-ready structure, handles defaults
`blueprint-generator`	`GenerateExperimentBlueprintYamlAsync`	Creates full test spec from inputs
`metric-binder`	`BindKpisToTelemetryAsync`	Maps KPIs to Observability Agent-compatible IDs
`uplift-calculator`	`CalculateUpliftFromKpiDataAsync`	Computes % improvement, confidence intervals, etc.
`memory-checker`	`FindSimilarTestInMemoryAsync`	Prevents duplicate or redundant tests
`result-embedder`	`EmitResultToMemoryGraphAsync`	Records outcome as knowledge graph update

🧠 Example: `GenerateExperimentBlueprintYamlAsync`¶

// Semantic Kernel planner function signature
[Function("GenerateExperimentBlueprintYamlAsync")]
public Task<string> GenerateBlueprintAsync(HypothesisInput input)

Takes structured input and emits:

experiment_id: exp-202407-ab-013
hypothesis_id: hyp-user-invite-flow
...

🔌 Plugin Integrations¶

External Component	Plugin Used	Purpose
Observability Agent	`TelemetryConnectorPlugin`	Register KPI bindings and metrics
Memory System (Vector DB)	`VectorSearchPlugin`	Find/test similarity, deduplicate inputs
Result Engine	`StatisticalValidatorPlugin`	Validate statistical significance

🧠 Agent Prompt Planner (Optional)¶

Supports multi-step plans for:

A/B vs multivariate branching
Fallback strategies
Retest recommendations
Controlled exposure adjustment

✅ Summary¶

The A/B Testing Agent relies on atomic, well-typed kernel functions to deliver:

🧪 Precise test definitions
📊 Scientifically validated outcomes
🔁 Self-correcting experimentation cycles

Skills turn this agent into a repeatable experimentation machine, not just a code generator.

🛠 Technologies and Tooling¶

The A/B Testing and Experimentation Agent is built atop the ConnectSoft AI Software Factory stack, with a strong focus on Semantic Kernel-based orchestration, event-driven execution, and cloud-native scalability. It uses a modular and observable design, aligning fully with ConnectSoft’s architectural principles.

🧠 Core Platform Stack¶

Layer	Technology / Tool	Purpose
🤖 Agent Execution	Semantic Kernel (SK)	Planner, prompt routing, skill orchestration
🧬 Language Model	Azure OpenAI (GPT-4o or GPT-4-turbo)	Interpretation, planning, variant synthesis, summarization
🧩 Orchestration	MCP Servers	Structured invocation, long-running memory, shared triggers
🧠 Memory Graph	Vector DB (e.g., Qdrant / Azure AI Search)	Test deduplication, prior learnings, variant embeddings
📊 Observability Layer	Azure Monitor / Application Insights / Grafana	Metric collection, test telemetry, dashboarding
📝 Blueprint Storage	Git-backed `.yaml` registries	Persistent test specs for audit and CI/CD integration
🔗 Event Bus	Azure Service Bus / Dapr PubSub	Trigger routing, telemetry dispatch, async agent signaling
⚙️ Runtime Execution	Azure Functions / Kubernetes (AKS)	Executing telemetry collectors and exposure logic

🔌 Internal ConnectSoft Components¶

Component	Role in A/B Agent
`blueprint-core`	Generates YAML specs for tests
`connectsoft.memory`	Deduplication and knowledge retention
`connectsoft.metrics.kpi`	Maps test outputs to metric IDs and telemetry streams
`agent-runtime-shell`	Handles lifecycle of launched test flows (A/B switches, sampling)
`experiment-result-core`	Parses, scores, and embeds test results from Observability Agent

🧪 Tools for Experiment Validation¶

Tool	Functionality
Bayesian Validator	Posterior probability scoring for uplift
Frequentist Engine	P-value calculation, t-test, confidence intervals
Memory Validator	Ensures test uniqueness and prevents redundancy
Multivariate Router	Handles >2 variants in complex UX or copy testing

🖥️ Sample Technology Flow¶

flowchart LR
    SK[Semantic Kernel Agent] -->|plans| Plugin[Blueprint Generator Plugin]
    Plugin --> YAML[ExperimentBlueprint.yaml]
    YAML --> Bus[Azure Service Bus]
    Bus --> Telemetry[Observability Agent]
    Telemetry --> Metrics[Azure Monitor / Grafana]
    Metrics --> Validator[Statistical Validator]
    Validator --> Memory[Vector DB]

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

☁️ Cloud-Native Design Principles¶

Serverless execution for test triggers (Azure Functions)
Kubernetes agents for scalable test exposure logic (AKS)
CI/CD integrated test registration pipeline (via GitOps or YAML PRs)
Telemetry hooks auto-bound via infrastructure-as-code

✅ Summary¶

The A/B Testing Agent uses:

🧠 Semantic Kernel + OpenAI for intelligence
🔗 Azure-native infra for orchestration
📊 Integrated observability for test evaluation
🧬 Modular plugins to extend functionality

It’s a scientific agent, deployed as cloud-native code, designed to learn at scale.

🧾 System Prompt¶

The System Prompt defines the core identity, role, boundaries, and principles of the A/B Testing and Experimentation Agent. It is the foundational instruction embedded at agent initialization time and drives all its downstream planning, validation, and blueprinting logic.

🧠 System Prompt Definition¶

You are the A/B Testing and Experimentation Agent in the ConnectSoft AI Software Factory.

Your primary goal is to construct scientific, statistically valid A/B test blueprints from structured hypotheses, marketing variants, and growth strategy inputs. You ensure each experiment is safe to run, observable, edition-aware, persona-targeted, and yields actionable results.

You must:
- Enforce test validity and avoid redundant or low-confidence experiments
- Output standardized YAML test blueprints that other agents and systems can consume
- Bind KPIs to telemetry events for post-experiment evaluation
- Store successful results into the memory system for reuse
- Collaborate with agents like the Growth Strategist, Marketing Specialist, Observability Agent, and Customer Success Agent

Always operate with:
- Scientific rigor (control groups, confidence scoring, KPI alignment)
- Edition-specific awareness
- Full traceability and reproducibility
- Fail-safe logic for collisions, missing KPIs, or ambiguous hypotheses

NEVER generate vague or unverifiable tests. You are a scientific validator — not a creative generator.

🔍 Key Constraints and Intent¶

Attribute	Description
🎯 Role Clarity	Agent defines and structures experiments — it does not invent ideas
📊 Scientific Grounding	Ensures all tests are statistically sound and measurable
🔗 Collaboration Ready	Designed to interact cleanly with upstream strategy and downstream telemetry
🧩 Blueprint-Oriented	Outputs reproducible YAML specs for all test definitions
🧠 Memory-Aware	Uses and contributes to memory graph to avoid repeated experiments

🔐 Safety & Guardrails¶

Guardrail	Enforcement Logic
Missing KPIs	Abort with error and request Observability Agent to define metric bindings
Ambiguous hypothesis	Reject and notify Growth Strategist Agent
Redundant experiment detected	Link to prior result and skip test generation
Unsupported persona/edition mix	Skip test and notify orchestrator for human validation

📣 Embedded Identity¶

This system prompt makes the agent behave like a growth scientist embedded in a scalable SaaS lab:

“Your job is not to guess what works — your job is to prove what works, and make sure it is learned forever.”

✅ Summary¶

This system prompt turns the A/B Testing Agent into a validation-centric, traceable, safety-bound orchestrator of scientific testing in the ConnectSoft factory:

🧪 No assumptions
📄 Only valid blueprints
🧠 All outcomes remembered

📝 Input Prompt Template¶

The Input Prompt Template defines how the A/B Testing and Experimentation Agent receives and interprets structured input from other agents or orchestrators. It transforms raw hypotheses, variant sets, and metric intents into an actionable and deterministic instruction format.

This template ensures consistency across all experiment planning interactions.

🧩 Prompt Template Structure (Semantic Kernel / OpenAI)¶

You are the A/B Testing and Experimentation Agent. The following inputs define a hypothesis that must be validated through a measurable experiment.

Your job is to:
1. Parse the hypothesis and variants
2. Validate that KPIs are defined and bindable
3. Generate a complete, reproducible experiment blueprint in YAML format
4. Apply edition and persona targeting logic
5. Ensure rollback, memory deduplication, and statistical validation logic

---

## Hypothesis ID:
{{hypothesis_id}}

## Persona:
{{persona}}

## Edition:
{{edition}}

## Hypothesis Statement:
{{hypothesis_statement}}

## Primary KPI:
{{primary_kpi}}

## Test Window (Days):
{{test_window_days}}

## Rollout Percentage:
{{rollout_percentage}}

## Variants:
- {{variant_1_name}}: {{variant_1_description}}
- {{variant_2_name}}: {{variant_2_description}}

---

Respond only with the completed YAML blueprint and no other text.
Ensure the output includes:
- Experiment ID
- Variants with control group definition
- KPI bindings
- Edition and persona filters
- Duration, exposure %, and control logic

🧠 Example Filled Input¶

## Hypothesis ID:
hyp-landing-cta-2024-q3

## Persona:
freelance_product_designer

## Edition:
startup

## Hypothesis Statement:
Using a “Get Started” button instead of “Request Demo” will increase trial sign-ups by lowering perceived commitment.

## Primary KPI:
trial_to_paid_conversion

## Test Window (Days):
14

## Rollout Percentage:
50

## Variants:
- get_started_cta: “Get Started” button and short form
- request_demo_cta: “Request Demo” button with calendar

💡 Prompt Flow Notes¶

Parsed through Semantic Kernel planner or Skill invocation
Allows chaining with memory lookups (e.g., persona test history)
Can be invoked via REST API or event-driven contract from orchestrator
Template supports YAML-in, YAML-out mode for CI/CD compatibility

✅ Summary¶

This input prompt template ensures:

🧪 Predictable test construction
📄 CI-compatible YAML exchange
🧠 Compatibility with upstream agents (Growth Strategist, Marketing Specialist)
🧭 Consistency in planning and traceability

This prompt is the blueprint behind every scientifically validated change in the ConnectSoft Factory.

📤 Output Expectations and Format¶

The A/B Testing Agent must emit outputs that are:

Machine-executable — compatible with downstream agents and runners
Scientifically valid — bound to KPIs and control logic
Edition/persona scoped — contextually targeted
Memory-traceable — able to be embedded and recalled

🧪 Primary Output: `ExperimentBlueprint.yaml`¶

This is the core product of the agent — a blueprint that fully defines the experiment for execution and analysis.

✅ YAML Format Specification¶

experiment_id: exp-202406-ab-034
hypothesis_id: hyp-cta-language-change
persona: freelance_product_designer
edition: startup
variants:
  - id: variant_a
    name: get_started_cta
    control: false
    description: CTA with "Get Started" button and minimal form
  - id: variant_b
    name: request_demo_cta
    control: true
    description: Traditional "Request Demo" button with scheduling flow
rollout:
  exposure_percentage: 50
  duration_days: 14
  use_control_group: true
kpis:
  - id: trial_to_paid_conversion
    source: metric://onboarding/trial_to_paid
telemetry:
  bindings:
    trial_to_paid_conversion: metric://onboarding/trial_to_paid
created_at: 2024-06-14T09:30:00Z

📊 Secondary Outputs¶

Output Type	Format	Purpose
`ExperimentResult.json`	JSON	Stores uplift %, winning variant, and confidence score
`MemoryUpdateRecord`	JSON	Injects results into graph memory (linked by hypothesis_id)
`AuditLogEntry.md`	Markdown	Trace log for governance, rollback, and human approval logs
`TestExecutionRequest`	JSON	Event payload to trigger rollout engine

🧠 Example Result Output¶

{
  "experiment_id": "exp-202406-ab-034",
  "winner": "get_started_cta",
  "uplift_percent": 18.7,
  "confidence_score": 0.965,
  "decision": "accept_hypothesis",
  "validated_by": "bayesian_engine",
  "kpi": "trial_to_paid_conversion",
  "test_duration_days": 14
}

🔗 Memory Insertion Structure¶

{
  "type": "experiment_result",
  "tags": ["ab_test", "startup", "freelance_product_designer"],
  "linked_hypothesis": "hyp-cta-language-change",
  "summary": "Get Started CTA increased conversions by +18.7% with 96.5% confidence",
  "variant_winner": "get_started_cta",
  "timestamp": "2024-06-28T12:00:00Z"
}

🧭 Output Validity Rules¶

Rule	Enforced Outcome
Must include at least two variants	Otherwise: error and upstream notification
Must declare KPI(s) and telemetry binding	Otherwise: abort until Observability Agent defines them
Edition and persona must be tagged	Ensures segmentation in downstream analytics
All timestamps must be in UTC ISO format	Enables alignment across pipelines

✅ Summary¶

The A/B Testing Agent produces:

📄 YAML blueprints (specifications)
🧪 JSON results (outcomes)
🧠 Memory entries (learned knowledge)
📜 Markdown audit trails (governance)

These outputs are contracts, not suggestions — designed to plug into the ConnectSoft Factory’s autonomous growth loop.

🧠 Memory – Short-Term and Long-Term¶

The A/B Testing and Experimentation Agent relies on a hybrid memory architecture to enforce test deduplication, knowledge reuse, and hypothesis lineage tracking.

It uses:

🔄 Short-term memory to persist current test context
🧬 Long-term memory to track experiment outcomes and prevent redundant ideas

🧠 Short-Term Memory (Contextual)¶

Scope	Description
Agent runtime context	Tracks the current hypothesis, KPI, persona, and variants
Prompt thread history	Maintains multi-step planning state during multi-turn experiments
Retry window	Captures recent validation failures (e.g. missing metrics)
Temporary vector store	Enables in-session similarity checks across recent hypothesis runs

🔁 Lifetime¶

Ephemeral — Reset after test is registered or discarded
Scoped per trigger — Not shared across test invocations
Attached to planner session or event correlation ID

🧠 Long-Term Memory (Persistent Graph)¶

Memory Graph Type	Purpose
🧪 `ExperimentResultGraph`	Stores outcomes, variant winners, confidence scores
🎯 `HypothesisLineage`	Links experiments to strategic hypotheses from Growth Strategist
👥 `EditionPersonaMap`	Tracks variant performance by edition and persona
📈 `KPIImpactHistory`	Keeps a record of variant performance over time

📦 Stored in:¶

Vector DB (e.g., Qdrant, Azure Cognitive Search) — for similarity and embedding
Document DB (e.g., CosmosDB) — for raw result storage and structured search
Blob Storage — for blueprint YAML archival and reproducibility

🧩 Vector Embeddings¶

Memory Item	Embedding Purpose
Hypothesis statement	Detect similarity to previous experiments
Variant configuration	Match against prior test designs
Result summary	Aid strategic recall by persona or feature

🧠 Memory Access Patterns¶

Use Case	Memory Function Called
Avoiding redundant tests	`FindSimilarTestInMemoryAsync`
Boosting test confidence via history	`GetKpiHistoryForVariantAsync`
Scoring hypotheses for viability	`ScoreHypothesisAgainstKnownResultsAsync`
Recalling winning variants by persona	`GetTopPerformingVariantsForPersonaAsync`

🔒 Data Retention and Versioning¶

All experiments are versioned by timestamp and context hash
Tests are immutable once finalized; re-runs are separate experiments
Backfill allowed from past telemetry for retroactive analysis if needed

✅ Summary¶

The agent is a scientific learner, not just a test executor:

🧠 Short-term memory keeps its thinking structured
🧬 Long-term memory makes the system cumulative, not repetitive

Every test run becomes a building block in the ConnectSoft growth brain.

✅ Validation and Verification Logic¶

The A/B Testing and Experimentation Agent enforces strict scientific and structural validation checks before any experiment is registered or executed. These checks ensure data integrity, statistical soundness, and alignment to platform principles (edition, persona, KPI observability, etc.).

🧪 Pre-Execution Validation Rules¶

Rule	Description
✅ At least 2 variants	Enforces A/B or multivariate test validity
✅ One control group must be defined	Designates baseline for performance comparison
✅ Primary KPI must be observable	Validates binding via Observability Agent
✅ Test window >= minimum duration	Prevents underpowered tests
✅ Targeting must match defined personas/editions	Avoids invalid or undefined segmentation
✅ No duplicate test exists	Checks memory for same hypothesis + persona + edition

🧠 Deduplication Check Logic¶

// Semantic Kernel plugin
var existing = await memory.CheckIfSimilarTestExists(hypothesisId, edition, persona);
if (existing != null) {
  return LinkTo(existing) && AbortCurrentTestCreation();
}

Protects factory from retesting solved ideas, reducing noise and user fatigue.

📊 Statistical Verification Post-Execution¶

Phase	Check
✅ Uplift Calculation	Compares performance vs. control with % improvement
✅ Confidence Score	Ensures >= 95% for auto-acceptance
✅ KPI Data Quality	Verifies complete telemetry signals across test set
✅ Sample Size Sufficiency	Auto-validates enough events for statistical power
✅ Result Integrity	Hash + signature check for reproducibility

⚠️ Fallbacks and Soft Errors¶

Error	Recovery Action
Missing KPIs	Wait + recheck; optionally request Observability Agent to create binding
YAML Parse Failure	Auto-correct format or return to planning agent
Metric Drift During Test	Log anomaly, reduce confidence, flag for Growth Strategist review
Memory write failure	Retry with exponential backoff or fallback to secondary persistence

🛡️ Agent is “Safety-First”¶

Never launches test unless all pre-checks pass
Automatically rejects unsafe, vague, or unmeasurable ideas
Logs test eligibility decisions for audit trail

✅ Summary¶

Validation turns this agent from a content generator into a scientific verifier:

🔍 Prevents duplicate or unsafe tests
📊 Verifies KPIs, telemetry, and outcome validity
🔁 Only allows measurable, traceable, memory-aware experiments

Without validation, there’s no science. This agent defends rigor at every step.

🔁 Retry and Correction Flow¶

The A/B Testing and Experimentation Agent is designed to self-heal in the face of missing data, misaligned inputs, or failed downstream actions. Rather than failing silently or producing invalid outputs, it follows a robust retry-correct-notify model to maintain operational integrity.

🔄 Retry Flow – Lifecycle Recovery Map¶

flowchart TD
    INIT[Start Test Planning]
    INIT --> VAL[Run Pre-Validation Checks]
    VAL -->|✅ All Pass| EXEC[Generate Blueprint]
    VAL -->|❌ Missing Data| CORR[Trigger Auto-Correction or Defer]
    CORR --> RETRY[Re-Run Validation After Fix]
    RETRY --> EXEC
    EXEC --> YAML[Emit Blueprint YAML]
    YAML --> PUB[Trigger Execution Request]
    PUB -->|❌ Delivery Failure| RETRY_PUB[Queue Retry with Backoff]
    RETRY_PUB --> PUB

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

🧪 Correction Mechanisms¶

Failure Mode	Recovery Strategy
❌ Missing Primary KPI	Pause and re-attempt after querying Observability Agent
❌ Invalid Variant Definitions	Auto-normalize input variants to enforce schema
❌ Telemetry Not Bound	Request metric binding plugin to create temporary bindings
❌ Experiment Already Exists	Link to existing result, reject new request
❌ YAML Format Invalid	Reconstruct structure with prompt correction plugin

🕓 Retry Backoff Strategies¶

Scenario	Retry Policy
Metric signal delay	Linear retry for up to 48 hours
Memory write failure	Exponential backoff up to 5 retries
Test registration dispatch error	Immediate retry, then escalate to event bus
Validation plugin timeout	Reattempt after cooling period

📣 Escalation and Notification¶

Trigger Condition	Escalation Target	Notes
Repeated YAML generation failure	Solution Architect Agent	Indicates possible model degeneration
No telemetry after 72h exposure	Growth Strategist Agent	KPI likely misaligned or misrouted
KPI mismatch with persona	Observability Agent	Needs telemetry redefinition
Control variant underperforms heavily	Marketing Specialist Agent	Signals UX/brand regression risk

🧠 Memory-Aware Corrections¶

If a similar hypothesis exists with a failed or inconclusive result → suggest rerun with revised targeting
Correction prompts may be composed automatically from memory context

✅ Summary¶

This retry/correction loop ensures:

📈 No silent failure
🔁 Tests are either run well or not at all
📣 All unresolved issues are escalated to the right agents
🧠 Memory reinforces which corrections worked previously

The A/B Agent is not just scientific — it’s resilient under ambiguity.

🤝 Collaboration Interfaces¶

The A/B Testing and Experimentation Agent is deeply integrated within the Growth, Marketing, and Customer Success cluster, acting as the validator and feedback loop provider for strategic hypotheses, marketing variations, and onboarding experiences. It uses structured APIs, events, memory references, and semantic prompt interfaces to collaborate with both upstream and downstream agents.

🔼 Upstream Interfaces (Receives From)¶

Agent	Interaction Type	Purpose
🧠 Growth Strategist	Event: `HypothesisCreated`	Supplies growth experiments tied to KPIs
📣 Marketing Specialist	Event: `VariantGroupDefined`	Sends UI/text/copy variants to test
📊 Observability Agent	Metric Lookup / Telemetry Binding	Provides KPI metrics and telemetry definitions
👥 Persona Builder	Persona Constraints	Supplies targeting boundaries for segmentation

🔽 Downstream Interfaces (Sends To)¶

Agent	Interaction Type	Purpose
📈 Observability Agent	Event: `TestTelemetryBound`	Triggers metric tracking configuration
✅ Customer Success Agent	Event: `WinningVariantIdentified`	Informs of optimal UX/flow variant for onboarding messages
🧠 Memory System	Write Operation	Stores result summaries, impact scores, and lineage
📤 Result Publisher	Event: `ExperimentCompleted`	Sends results to dashboards, orchestrators, or Git pipelines

🔗 Interface Protocols¶

Method Type	Used For	Details
🔔 Event Bus (PubSub)	Most agent-to-agent signals	Topics like `experiments/new`, `metrics/ready`, `results/finished`
🧠 Vector Search API	Memory similarity and deduplication	Plugged into shared embedding and search infrastructure
📩 REST Callback	Optional integrations (e.g. email)	Used by Product Ops or external dashboards
🧾 Semantic Prompt	Kernel-to-Kernel coordination	Used for chained plans from Planner Agent

🤖 Sample Collaboration Sequence¶

sequenceDiagram
    GrowthStrategist->>ABTestingAgent: HypothesisCreated
    MarketingSpecialist->>ABTestingAgent: VariantGroupDefined
    ABTestingAgent->>ObservabilityAgent: RequestTelemetryBinding
    ObservabilityAgent-->>ABTestingAgent: MetricBindingsReturned
    ABTestingAgent->>MemorySystem: CheckPriorExperiments
    ABTestingAgent->>ObservabilityAgent: RegisterTest
    ObservabilityAgent->>ABTestingAgent: TelemetrySignalReady
    ABTestingAgent->>MemorySystem: WriteResult
    ABTestingAgent->>CustomerSuccessAgent: WinningVariantIdentified

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

📎 Collaboration Contract Format¶

Payload Field	Description
`hypothesis_id`	Identifier from Growth Strategist
`edition`	Used to scope test and telemetry
`persona_id`	Used for audience targeting
`variant_ids`	Included in ObservabilityAgent bindings
`metric_binding_ids`	Confirmed observability metrics
`result_summary`	Emitted to downstream agents on complete

🧠 Memory-Linked Collaboration¶

Agents share memory references, not just payloads
Every test result is traceable to source agent(s)
Hypotheses are linked to variant sets, which are linked to KPIs, which are linked to results

✅ Summary¶

This agent doesn’t work alone — it collaborates with:

📈 Observability Agent (for metrics)
📣 Marketing & Growth Agents (for input)
🧠 Memory & Telemetry Graph (for reuse)
✅ Customer Success Agent (for action)

It is the critical feedback engine of the ConnectSoft Factory’s growth flywheel.

📊 Observability Hooks¶

The A/B Testing and Experimentation Agent is designed with observability-first principles, ensuring that every action, decision, and output is traceable, monitorable, and auditable. These observability hooks are essential for debugging failed tests, analyzing growth impact, and ensuring transparency across the software factory.

🔍 Observability Design Goals¶

✅ Trace end-to-end lifecycle of an experiment
✅ Validate telemetry coverage for each KPI
✅ Capture statistical confidence and exposure metrics
✅ Expose agent activity through metrics and logs
✅ Enable dashboards for test outcome visualization

📈 Metrics Emitted (via Azure Monitor / Prometheus)¶

Metric Name	Type	Labels	Description
`ab_tests_planned_total`	Counter	edition, persona, kpi_id	Number of experiments generated
`ab_test_blueprint_emission_latency_ms`	Timer	experiment_id	Time to generate and publish a complete test blueprint
`ab_test_validation_failures_total`	Counter	reason, hypothesis_id	Count of validation rejections by reason
`ab_test_result_confidence_score`	Gauge	experiment_id, variant_id	Confidence % of winning variant (0.0–1.0)
`ab_test_exposure_percentage`	Gauge	edition, persona, experiment_id	Percent of users exposed to test
`ab_test_variant_winner_rate`	Gauge	variant_id, persona, kpi_id	Win rate of a variant across experiments

📜 Logs (via Application Insights / Seq / Grafana Loki)¶

Log Event	Severity	Metadata	Notes
`TestBlueprintCreated`	Info	experiment_id, persona, edition, hypothesis_id	Blueprint created and published
`ValidationFailed`	Warning	validation_step, details	Triggered on invalid hypothesis or config
`TelemetryBindingMissing`	Error	kpi_id, metric_hint	Unable to bind KPI
`TestResultRecorded`	Info	experiment_id, uplift, winner	Captured final result
`MemoryConflictDetected`	Warning	prior_experiment_id, hypothesis_id	Similar experiment already exists

🧪 Telemetry Binding¶

The agent emits a binding request to the Observability Agent using the format:

{
  "experiment_id": "exp-202407-ab-045",
  "kpis": ["trial_to_paid_conversion", "click_rate_landing_cta"],
  "expected_signals": ["event.user.converted", "event.user.clicked_cta"],
  "source_agent": "ab_testing_agent"
}

This ensures:

Metrics are defined and routed
Logs and metrics align with real-time signal capture
Variant-specific dashboards can be rendered

📊 Dashboards (Optional)¶

Dashboard Type	Key Insights
📈 Experiment Impact	Uplift %, exposure, win rates, confidence
🧭 Agent Performance	Blueprint latency, retry counts, failure rates
🔍 KPI Drift	Metric quality and volatility
🧠 Memory Insights	Redundancy rate, historical variant trends

✅ Summary¶

Observability turns this agent into a verifiable scientific machine:

🔁 Every test has a log trail
📈 Every metric is bound to a KPI
🧠 Every decision is auditable and retraceable

No black boxes — every experiment has evidence, not guesses.

🧍‍♂️ Human Intervention Hooks¶

While the A/B Testing and Experimentation Agent operates autonomously, there are critical checkpoints where human intervention is either required or recommended. These hooks ensure safety, correctness, and strategic alignment, especially for edge cases, high-impact releases, or non-standard experiments.

🛑 Intervention Points¶

Scenario	Who Intervenes	Why
🚫 Unclear Hypothesis	Growth Strategist Agent	Validate problem statement and KPI relevance
❓ No KPI Mapped or Observability Error	Observability Engineer	Define or bind new telemetry
⚠️ High-Risk Variant (e.g., pricing)	Product Manager / Architect	Approve potential impact on user experience or revenue
🧠 Memory Conflict	Product Owner	Decide whether to rerun a similar experiment
📄 Blueprint Approval for Launch	Human Reviewer (optional)	Sign off on YAML before rollout to production users

📬 Notification & Review Channels¶

Medium	Trigger Event	Content
📨 Email or Teams Alert	`TestValidationFailed`	Sent to designated product growth lead
✅ GitHub PR	`ExperimentBlueprintCreated`	Blueprint pushed as PR to review with CI/CD pipeline
📋 Backlog Ticket	`ManualReviewRequired`	Assigned to Product Ops or designated reviewer
🧾 Markdown Log	`HumanOverrideDecisionLogged`	All decisions logged with justification and timestamp

🧠 Prompt-Level Escalation¶

If the Semantic Kernel detects an ambiguity or risky assumption, it auto-generates a prompt like:

🚨 Human input required:
The current hypothesis targets a KPI that cannot be validated by current telemetry.

Please define a KPI binding or reframe the hypothesis. Without this, the test cannot proceed.

This is routed to the orchestrator or planner interface with inline editing options.

✍️ Editable Fields for Human Review¶

Field	Editable by Human Reviewer
Hypothesis Statement	✅
Primary KPI	✅
Variant Descriptions	✅
Rollout Duration	✅
Edition/Persona Scope	✅

🔒 Override Safeguards¶

🛑 Overrides require rationale + approval timestamp
🧾 All manual changes are stored in memory graph for traceability
📣 Overrides automatically notify Customer Success Agent (if user experience is affected)

🧑‍💻 Human-AI Feedback Loop¶

After a human edits or approves a test:

✅ A new memory entry is stored as HumanReviewedExperiment
🤖 The agent learns from override patterns for future planning

✅ Summary¶

The agent supports autonomous growth, but:

Knows when to escalate
Lets humans steer critical experiments
Keeps overrides traceable and auditable

The A/B Testing Agent is autonomous by default — but never isolated from human judgment.

🧾 Summary and Conclusion¶

The A/B Testing and Experimentation Agent is a foundational intelligence unit in the ConnectSoft AI Software Factory’s Growth, Marketing, and Customer Success cluster. It serves as the scientific verification engine that transforms hypotheses, marketing variants, and strategic intents into measurable, controlled, and reproducible experiments.

This agent ensures that every product growth decision is backed by data and every test becomes part of a compounding memory of what works — and what doesn't.

🎯 Core Value Delivered¶

Area	Impact
📊 Scientific Growth	Enforces discipline around what’s tested, why, and how it’s measured
🧪 Reproducibility	Produces standard YAML blueprints with embedded KPIs and variants
🧠 Memory-Learning Loop	Stores results, confidence, and winners in long-term growth memory
🔁 Retry-Resilient	Self-healing with correction flows and agent escalation paths
🧭 Platform Integration	Collaborates across strategy, marketing, telemetry, and success teams
✅ Auditable Experiments	Full traceability from hypothesis to result to onboarding feedback

🧠 Agent Persona Summary¶

id: ab_testing_agent
role: scientific_validator
cluster: growth_marketing_success
inputs:
  - hypotheses from growth strategist
  - variants from marketing specialist
  - KPIs from observability agent
outputs:
  - YAML test blueprints
  - telemetry bindings
  - result summaries
  - winner announcements
memory:
  short_term: session-bound planning and validation
  long_term: experiment results, KPI performance history
observability:
  - metrics: latency, outcome confidence, exposure %
  - logs: result decisions, validation rejections
  - dashboards: test impact, agent coverage

🧬 Flow Position Summary¶

flowchart LR
    GSA[Growth Strategist Agent] -->|HypothesisCreated| ABA[A/B Testing Agent]
    MSA[Marketing Specialist Agent] -->|VariantsReady| ABA
    ABA -->|ValidatedExperimentBlueprint| OBS[Observability Agent]
    OBS -->|KPI Telemetry Stream| ABA
    ABA -->|ResultRecorded| Memory[Growth Memory Graph]
    ABA -->|WinnerVariantAnnounced| CSA[Customer Success Agent]

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

🧠 Final Reflection¶

In traditional factories, you build — and you guess what works. In ConnectSoft, we test, learn, and scale what works.

The A/B Testing Agent is the engine of evidence-based acceleration.

🧪 A/B Testing and Experimentation Agent¶

🎯 Purpose¶

🧠 Core Goal¶

🔍 Key Roles in the ConnectSoft AI Factory¶

📦 What This Agent Enables¶

🎯 Without It…¶

🧠 Core Role in the ConnectSoft Factory¶

🧬 Positioned at the Intersection of Strategy and Observability¶

🧭 Flow Positioning¶

🔁 Position in Agent Execution Flow¶

🧩 Lifecycle Coordination¶

🏗️ Operates Across¶

✅ Summary¶

🧩 Cluster Placement and Positioning¶

📦 Cluster and Layer Placement¶

🔁 Execution Timeline Within the Factory¶

🧱 Functional Role in Cluster Map¶

🧩 Sub-Cluster: Experimentation Layer¶

🧠 Specialized Positioning¶

✅ Summary¶

🚀 Strategic Contribution¶

📈 Why This Agent Matters¶

🔁 Impact on Factory-Wide Outcomes¶

🧬 Strategic Leverage¶

🔍 Test Everywhere, Learn Anywhere¶

🧠 Knowledge Compounding¶

✅ Summary¶

⚡ Activation Triggers¶

🔔 Trigger Sources¶

🧠 Smart Trigger Inference (Optional)¶

🎛️ Trigger Configuration Example (YAML)¶

🧭 Dependency Check Before Activation¶

🔁 Retry Logic (If Blocked)¶

✅ Summary¶

📋 Responsibilities¶

🧪 Core Responsibilities¶

📈 KPI Types Supported¶

🔄 Example Test Management Lifecycle¶

📦 Output Responsibilities¶

✅ Summary¶

🔽 Inputs¶

📥 Primary Input Channels¶

📎 Example Growth Hypothesis Input¶

🧠 Input Categories¶

🧩 Implicit Inputs (from Memory)¶

❗Input Validation Rules¶

✅ Summary¶

📤 Outputs¶

📦 Primary Output Artifacts¶

🧾 Example: ExperimentBlueprint.yaml¶

🧠 Memory Entry Output¶

🔄 Result Distribution¶

🧭 Format Characteristics¶

✅ Summary¶

🔁 Process Flow Overview¶

🧪 High-Level Lifecycle¶

🧭 Detailed Phase Flow¶

🔂 Process Flow Diagram¶

🛑 Error Branches and Loopbacks¶

🧠 Self-Regulation¶

✅ Summary¶

🧠 Skills and Kernel Functions¶

🧩 Core Skill Categories¶

🧠 Kernel Skills Used¶

🧠 Example: GenerateExperimentBlueprintYamlAsync¶

🔌 Plugin Integrations¶

🧠 Agent Prompt Planner (Optional)¶

✅ Summary¶

🛠 Technologies and Tooling¶

🧠 Core Platform Stack¶

🔌 Internal ConnectSoft Components¶

🧪 Tools for Experiment Validation¶

🖥️ Sample Technology Flow¶

☁️ Cloud-Native Design Principles¶

✅ Summary¶

🧾 System Prompt¶

🧠 System Prompt Definition¶

🔍 Key Constraints and Intent¶

🔐 Safety & Guardrails¶

📣 Embedded Identity¶

🧾 Example: `ExperimentBlueprint.yaml`¶

🧠 Example: `GenerateExperimentBlueprintYamlAsync`¶

🧪 Primary Output: `ExperimentBlueprint.yaml`¶