🧩 Marketing Specialist Agent¶

Purpose¶

The Marketing Specialist Agent is a generative agent responsible for transforming product plans, user personas, and feature releases into targeted, persona-aligned marketing assets and strategies. It activates once a product, feature, or edition is defined and ready to be introduced to the market — playing a critical role in the go-to-market execution phase of the ConnectSoft AI Software Factory.

🧠 Core Role in the Factory¶

This agent bridges the gap between product delivery and user acquisition by generating and localizing marketing content, designing campaign playbooks, and ensuring brand alignment across all materials. It is the entry-point agent for the marketing sub-cluster and drives downstream activation of campaign agents, A/B testing strategies, and customer communication pipelines.

🧩 Position in the Growth, Marketing & Customer Success Cluster¶

Layer	Cluster	Description
📦 Output Producer	Growth, Marketing & Customer Success	Converts structured product information into marketing strategies and campaign blueprints
🎯 GTM Execution	Activates after product MVP and edition segmentation are defined
🧭 Flow Position	Consumes outputs from: Product Manager Agent, UX Designer Agent, Persona Builder Agent; Sends outputs to: A/B Testing Agent, Customer Success Agent

flowchart TD
    PM[Product Manager Agent] -->|ProductPlanCreated| MSA[Marketing Specialist Agent]
    UX[UX Designer Agent] -->|UserJourneys| MSA
    PO[Product Owner Agent] -->|FeatureRelease| MSA
    MSA -->|MarketingPlanReady| ABT[A/B Testing Agent]
    MSA -->|CampaignSpecGenerated| CSA[Customer Success Agent]

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

🚀 Strategic Contribution¶

Value Dimension	How This Agent Delivers
Go-to-Market Acceleration	Reduces time between MVP definition and public announcement
Persona Precision	Aligns campaigns to well-defined user personas and customer journeys
Edition-Awareness	Generates differentiated marketing for each edition (lite, pro, enterprise)
AI-Generated Copy	Autonomously produces ready-to-review marketing copy, headlines, and call-to-actions
Multichannel Ready	Formats content for email, landing pages, ads, onboarding flows, etc.

🧩 Example Activations¶

Trigger Event	Description
`ProductPlanCreated`	Triggers campaign strategy definition for new MVP or release phase
`EditionSegmented`	Launches edition-specific messaging
`PersonaDefined`	Adjusts tone, content, and targeting to updated audience
`UXFlowReady`	Generates microcopy and CTAs for onboarding and product-led growth (PLG) loops

✅ Summary¶

The Marketing Specialist Agent is essential for:

Ensuring every product release is visible, positioned, and adopted
Reducing dependency on human marketers for foundational campaign assets
Creating a traceable link between product features and growth funnel assets

Without this agent, software is built — but no one knows it exists.

Responsibilities¶

The Marketing Specialist Agent owns the translation of product intent into actionable growth strategies and customer-facing narratives. It ensures every feature, edition, and persona is accompanied by contextually aligned, ready-to-publish marketing artifacts.

🧠 Deliverables at a Glance¶

This agent’s output is not merely creative — it is structured, versioned, and traceable across releases. It contributes to both inbound marketing (awareness, engagement) and product-led growth (activation, onboarding) pipelines.

📦 Core Responsibilities¶

Category	Description
📄 Campaign Plan Generation	Creates high-level campaign strategies including channels, sequencing, and messaging pillars
✍️ Persona-Aligned Copywriting	Produces headline variants, value propositions, and taglines tuned to each user persona
📊 Message–Feature Mapping	Aligns features to specific pain points and business benefits per persona
🧱 Edition-Specific Messaging	Adjusts language and pitch according to edition (e.g., Pro vs Lite)
🧭 Audience Targeting Strategy	Outputs structured targeting metadata (roles, verticals, geos, intent levels)
📬 Multichannel Content Templates	Generates content blocks for emails, landing pages, ads, onboarding, etc.
🔁 Lifecycle Integration	Creates pre-launch, launch, post-launch messaging streams
🪪 Persona Feedback Handling	Adapts messaging based on feedback from Customer Success and Growth Strategist agents

🧩 Coordination Scope¶

Collaborator Agent	Description of Interaction
🧠 Product Manager Agent	Consumes product plans and key value drivers for translation into marketing terminology
🧠 Persona Builder Agent	Aligns all generated copy with persona goals, fears, behaviors
🧠 UX Designer Agent	Incorporates insights from user journeys for onboarding and retention messaging
🧠 Customer Success Agent	Feeds real-world customer pain points and feedback for ongoing iteration
🧠 A/B Testing Agent	Sends message variants and hypothesis buckets for experimentation setup
🧠 Growth Strategist Agent	Aligns campaigns with broader growth themes and business model goals

🚀 Strategic Responsibility Alignment¶

Value Pathway	Responsibility Owned
📈 Top of Funnel (ToFu)	Awareness and positioning content aligned to channels and cold audiences
🔄 Middle of Funnel	Educational and value-driven content for engaged or trialing users
🔁 Bottom of Funnel	Edition-specific, ROI-aligned messaging, CTAs, and conversion hooks
🧠 Retention Loop	Messaging for PLG onboarding, feature adoption, and expansion

🧩 Key Artifact Examples¶

Artifact	Format	Description
`campaign-plan.md`	Markdown	Overview of campaign theme, channels, timing
`persona-variant-map.yaml`	YAML	Matrix mapping feature to persona pain point + copy variant
`onboarding-copy.txt`	Text	Inline CTAs and tooltips for PLG-based flows
`value-prop-pairs.json`	JSON	Structured feature–benefit mapping with persona tags
`email-template-lite.html`	HTML	Campaign email aligned to Lite edition, intro funnel
`marketing-metrics-expect.json`	JSON	Goals for engagement, CTR, awareness for each variant or channel

✅ Summary¶

The Marketing Specialist Agent is accountable for a wide spectrum of marketing responsibilities — from abstract messaging strategy to concrete channel-ready content.

Its role is both creative and programmatic, with a deep emphasis on:

Alignment with personas and editions
Structured output usable by A/B, growth, and success agents
Real-time adaptability across campaigns and launch cadences

In ConnectSoft, it’s not enough to release features — they must speak directly to the customer’s need.

Inputs¶

The Marketing Specialist Agent operates in a highly contextualized information space. Its effectiveness depends on semantic, persona, and strategic input artifacts produced by earlier agents in the ConnectSoft Factory pipeline. These inputs allow it to synthesize accurate, targeted marketing materials that align with business goals, user psychology, and feature delivery.

🧠 Information-Driven Agent¶

The agent is prompt-aware, trace-aware, and edition-aware — consuming data and documents that represent the product’s functional identity and target market posture.

📥 Core Inputs¶

Input Artifact or Signal	Provided By	Description
`product-plan.md`	Product Manager Agent	Describes key features, value propositions, positioning goals, and high-level release intent
`persona-profile.json`	Persona Builder Agent	Structured metadata about target users (roles, needs, pains, behaviors, preferences)
`edition-segmentation.yaml`	Product Owner Agent	Defines editions (Lite, Pro, Enterprise), audience tiers, and feature availability
`user-journeys.graphml`	UX Designer Agent	Behavioral map of onboarding and engagement pathways
`feature-release-signal`	Product Owner Agent	Event trigger that a feature or MVP is approved and ready to be marketed
`brand-guidelines.md`	Style System Agent	Style rules, tone of voice, color and language requirements
`persona-sentiment-map.json`	Customer Success Agent	Aggregated customer feedback and emotional associations with existing features
`campaign-feedback-history.json`	Growth Strategist Agent	Logs from prior campaigns, including open rates, CTR, persona reactions
`kpi-targets.yaml`	Business Model & Revenue Agent	Target conversion, adoption, and engagement goals by audience and channel

🔁 Input Event Signals (Runtime)¶

Event	Payload Description
`PersonaDefined`	Contains one or more updated persona profiles
`ProductPlanCreated`	Includes all feature, edition, and market targeting definitions
`EditionSegmented`	Activates edition-specific copy generation workflows
`UXFlowReady`	Provides PLG onboarding pathways for message/CTA generation
`FeedbackLoopClosed`	Delivers customer response to previous messaging/campaigns
`CampaignPerformanceAnalyzed`	Feedback loop from A/B Testing Agent or Growth Strategist Agent

🧩 Knowledge Input Patterns¶

Input Pattern	Agent Behavior Triggered
`Persona + Feature`	Generates personalized value proposition and CTAs
`Edition + KPI`	Optimizes call-to-action tone and urgency
`UXFlow + Onboarding`	Creates welcome emails, intro tours, and motivational copy
`Sentiment + Feedback`	Avoids pain points or reinforces emotionally resonant benefits

🚫 Input Gaps and Fallbacks¶

The agent is designed with resilience in partial contexts. If some upstream documents are missing:

Missing Input	Fallback Strategy
Persona profile	Defaults to known segment archetypes from long-term memory
UX flow map	Uses generic onboarding stages: discover → adopt → succeed
KPI targets	Uses factory-default engagement/conversion thresholds
Brand guide	Uses internal memory or prompt-initialized tone and messaging instructions

✅ Summary¶

The Marketing Specialist Agent depends on rich, multi-agent inputs to deliver precise, impactful marketing strategies.

By aggregating:

Business goals from the PM/PO stream
Emotional cues from the UX and Customer Success loop
Performance signals from the Growth feedback cycle

…it transforms raw product intent into persona-perfect growth fuel.

In ConnectSoft, this agent doesn’t “invent” marketing — it builds it from truth and trace.

📤 Outputs¶

The Marketing Specialist Agent generates modular, structured, and persona-aware marketing artifacts that can be directly consumed by downstream agents, CI/CD pipelines, product documentation, or marketing systems (email, landing pages, analytics).

These outputs serve as source-of-truth campaign components, ensuring traceability to product features, user journeys, and edition-specific messaging.

🧾 Primary Output Artifacts¶

Artifact	Format	Purpose
`campaign-plan.md`	Markdown	Describes campaign theme, goals, target audiences, timing, tone, and multichannel strategy
`persona-copy-variants.yaml`	YAML	Maps each user persona to tailored headlines, CTAs, and value propositions
`feature-value-map.json`	JSON	Links product features to concrete persona pain points and benefits
`onboarding-copy.txt`	Text	Inline CTAs and messaging for PLG onboarding flows
`email-template-lite.html`	HTML	Email content aligned to Lite edition and trial funnel
`ad-copy-variants.json`	JSON	Paid campaign message variants tagged by persona, channel, and intent stage
`cta-matrix.yaml`	YAML	Call-to-action matrix sorted by feature, edition, and funnel stage
`campaign-objectives.yaml`	YAML	Target KPIs such as open rates, conversion rates, click-through rates

🔄 Output Metadata Tags¶

All outputs include embedded metadata to ensure they are:

Metadata Tag	Description
`trace_id`	Links back to originating product/feature blueprint
`edition_tag`	Indicates which edition (Lite, Pro, Enterprise) content is for
`persona_id`	Ties each message variant to specific persona archetypes
`funnel_stage`	Top/Middle/Bottom/Onboarding/Retention
`campaign_id`	Identifies campaign grouping across multiple assets
`language_code`	Enables localization, i18n flows
`validation_state`	Marks if the content has been human-approved or tested in runtime

🧠 Output Consumption by Other Agents¶

Consuming Agent	Consumed Artifact
A/B Testing Agent	`ad-copy-variants.json`, `cta-matrix.yaml`
Growth Strategist Agent	`campaign-plan.md`, `campaign-objectives.yaml`
Customer Success Agent	`onboarding-copy.txt`, `feature-value-map.json`
Studio Documentation Agent	`persona-copy-variants.yaml`, `feature-value-map`
Analytics Evaluator Agent	`campaign-objectives.yaml`

🧪 Example Output Fragment¶

persona_id: decision_maker_enterprise
edition_tag: enterprise
funnel_stage: mid
headline: "Unify Your Operations in a Single Dashboard"
subtext: "See how enterprise teams automate, report, and scale — without IT dependency."
cta: "Request a Live Demo"
channel: email
language_code: en-US
trace_id: trace-2341-campaign-mvp-q3

✅ Summary¶

The Marketing Specialist Agent produces modular, cross-channel marketing blueprints that are:

✍️ Ready for downstream delivery (email, landing page, onboarding, ads)
🔍 Traceable to product, persona, and edition
📊 Validated against campaign objectives and funnel stages

Every message it outputs is grounded in feature trace, persona intent, and platform-wide goals.

🧠 Knowledge Base¶

The agent’s output quality relies heavily on a rich, memory-augmented knowledge base that provides product context, persona data, campaign history, and linguistic nuance. This allows the agent to generate content that is factually grounded, persona-aware, tonally consistent, and optimized for conversion.

The knowledge base is composed of both pretrained knowledge and project-specific injected context, enhanced through long-term vector memory and traceable event history.

🧩 Preloaded Domain Knowledge¶

Domain	Purpose
SaaS Marketing Strategies	Foundation for channel planning, funnel design, and B2B/B2C messaging
UX & Onboarding Patterns	References for PLG messaging and feature walkthrough language
Funnel Psychology	Behavioral templates tied to ToFu, MoFu, BoFu stages
Edition Pricing Logic	Guides CTAs and urgency for trials, upgrades, and enterprise editions
Brand Voice Catalogs	Reusable tone and language models for different brand archetypes
Compliance Guidelines	Optional: When activated for regulated domains (e.g., HIPAA, GDPR)

🗂️ Contextual Project Memory¶

Memory Type	Source Agent / Layer	Description
`VisionDocument`	Vision Architect Agent	Project’s mission, core themes, and strategic tone
`PersonaModel`	Persona Builder Agent	Detailed archetypes: roles, goals, objections, preferences
`ProductPlan`	Product Manager Agent	Features, modules, value props, release plans
`UXFlowGraph`	UX Designer Agent	Paths users take through onboarding and product interaction
`CampaignHistory`	Growth Strategist Agent	Prior campaigns, engagement data, variant performance, success/failure metrics
`SentimentMap`	Customer Success Agent	Common objections, pain points, and adoption blockers

📥 Dynamic Knowledge Injection¶

Injected At Runtime	Injected Data	Example
On Edition Launch	`edition-segmentation.yaml`	Target pricing, plan tiers, eligibility filters
On Feature Release	`feature-release.json`	Feature name, rollout plan, expected outcomes
On UX Readiness	`onboarding-flow.graphml`	Walkthrough stages, tooltips, entry points
On Persona Update	`persona-profile.json`	Updated needs, channels, conversion obstacles

🔎 Embedded Semantic Memory (Vector DB)¶

The agent stores and retrieves semantically similar:

Past campaigns and messaging artifacts
Persona-matching copy blocks
CTAs and headlines that scored high on conversion
Feature-description–value-pair templates

These are retrieved using semantic search across OpenAI-embedded vectors and filtered by:

persona_id
channel_type
edition_tag
trace_id
success_score (from feedback)

✅ Summary¶

The agent’s knowledge base is both declarative and dynamic — blending:

🧱 Core marketing expertise
🧠 Contextual persona and product trace
🔄 Real-time memory lookups

This enables it to generate accurate, reusable, and personalized marketing assets, with campaign continuity and messaging consistency over time.

It doesn’t just “know how to write” — it knows who it’s for, why they care, and what’s worked before.

🔄 Internal Process Flow¶

The Marketing Specialist Agent follows a multi-stage generation pipeline that transforms product inputs into complete, channel-ready marketing assets. Each phase is modular and traceable, allowing the orchestrator to control retries, prompt refinement, or human review at any stage.

The process is persona-centric, edition-aware, and goal-driven — ensuring that each output aligns with both business and user needs.

🧭 High-Level Flow¶

flowchart TD
    Start([Trigger Event])
    Ingest[🧠 Ingest Contextual Inputs]
    Segment[🎯 Persona & Edition Targeting]
    Plan[🗂 Define Campaign Strategy]
    Write[✍️ Generate Copy & CTAs]
    Structure[📦 Format Structured Artifacts]
    Validate[✅ Apply Validation Rules]
    Emit[📤 Emit Artifacts + Observability]
    End([Return Outputs or Trigger Retry])

    Start --> Ingest --> Segment --> Plan --> Write --> Structure --> Validate --> Emit --> End

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

🧩 Process Stages in Detail¶

Stage	Description	Skills/Functions Used
🧠 Ingest Contextual Inputs	Loads product plan, persona data, UX flow, and edition segmentation	`context.read_trace()`, `memory.search()`
🎯 Persona & Edition Targeting	Selects appropriate persona–edition pairs for output generation	`persona.select_targets()`, `edition.match()`
🗂 Define Campaign Strategy	Establishes key messages, funnel stage, tone, and delivery channels	`strategy.plan()`, `tone.adjust()`
✍️ Generate Copy & CTAs	Writes headline variants, CTAs, ad copy, email text, and onboarding microcopy	`copy.generate_text()`, `cta.create()`
📦 Format Structured Artifacts	Packages outputs into Markdown, YAML, HTML, JSON based on downstream needs	`formatter.yamlify()`, `formatter.htmlize()`
✅ Apply Validation Rules	Checks alignment with persona tone, duplication, keyword balance, and completeness	`validator.check_all()`
📤 Emit Artifacts	Publishes assets to orchestration layer or agent memory + emits metrics and trace tags	`emit.output()`, `trace.record()`

🔁 Modular Execution Design¶

Each stage is implemented as an isolated, injectable prompt skill in Semantic Kernel. This enables:

Parallel processing of multiple personas or editions
Partial retries (e.g., regenerate copy but retain campaign plan)
Observability across prompt flows

Execution is driven by orchestrator-planner patterns, not hardcoded pipelines.

⚠️ Conditional Branching¶

Condition	Branch Behavior
Missing UX Flow	Skip onboarding copy stage and inject generic templates
Multiple Editions Targeted	Loop generation per edition and output segmented YAML or variant collections
Missing Persona Segment	Flag with `low_context_trace` and defer to orchestrator for enrichment or halt
Validation Score < Threshold	Trigger correction skill or forward to human reviewer

✅ Summary¶

The agent's internal process is:

🔍 Trace-aware — everything links back to upstream decisions
🧠 Memory-augmented — supports reuse and continuous improvement
🧱 Composable — each step can be independently extended or customized

The result is a robust, adaptive, and intelligent copy-generation workflow — built not to write once, but to grow smarter with every launch.

🧱 Skills and Semantic Kernel Functions¶

The Marketing Specialist Agent is implemented as a multi-skill, prompt-driven Semantic Kernel agent, leveraging a modular collection of reusable prompt templates, AI functions, and plugin-based enhancements. Each skill is context-aware and tuned to work with input metadata, memory embeddings, and past campaign feedback.

🧠 Agent Skill Categories¶

Skill Category	Purpose
📥 Ingestion & Memory	Retrieve contextual inputs and embeddings for product, persona, UX, and history
🎯 Targeting	Select matching personas, editions, funnel stages, and adjust tone accordingly
🗂 Campaign Planning	Generate a campaign structure: messaging pillars, channels, goals, timeline
✍️ Copy Generation	Create ad copy, onboarding microcopy, value props, CTAs, headlines
🧱 Structured Output	Format YAML, JSON, Markdown, and tagged content for downstream agents and humans
✅ Validation & Scoring	Apply prompt rules and scoring logic for readability, alignment, persona fit
📤 Emission & Tracing	Package final output and emit with metadata and observability payloads

✨ Core Semantic Kernel Functions¶

Skill Function Name	Description
`context.loadPersona()`	Loads persona model from long-term memory or prompt injection
`context.loadFeaturePlan()`	Retrieves latest feature descriptions and value mappings
`persona.match()`	Selects best persona–edition–channel targets for campaign output
`strategy.generate()`	Builds campaign blueprint with primary messages, delivery flows, and KPIs
`copy.generateText()`	AI-assisted generation of personalized headlines, CTAs, and body copy
`copy.variant()`	Creates multiple variants of the same message by tone, edition, or funnel
`formatter.toMarkdown()`	Converts content block to `campaign-plan.md` format
`formatter.toYaml()`	Structures targeting matrix and CTA mapping for downstream agents
`validator.score()`	Calculates alignment, quality, and persona-fit score
`trace.emit()`	Saves trace log with metadata and memory ID references

🧩 Sample Function Execution Trace¶

{
  "trace_id": "camp-v2-lite-0425",
  "skill": "copy.generateText",
  "input": {
    "persona": "product_owner_startup",
    "feature": "workflow_automation",
    "edition": "Lite"
  },
  "output": {
    "headline": "Automate Repetitive Tasks. Reclaim Your Time.",
    "cta": "Get Started Free"
  },
  "score": 92.5
}

🔌 Plugin-Enhanced Capabilities¶

When needed, the agent integrates Semantic Kernel Plugins or external tools such as:

Plugin Name	Capability
`BrandVoicePlugin`	Ensures copy tone complies with style guide (formal, friendly, bold, etc.)
`LocalizationPlugin`	Supports multilingual output and internationalized campaign variants
`KPIEstimatorPlugin`	Predicts impact score for message variants based on past performance

✅ Summary¶

The agent’s skills are modular, orchestrated, and composable — allowing for:

💡 Context-aware generation of copy and strategy
🔄 Variants and funnel-specific output
📦 Artifact structuring aligned to factory needs

Every prompt it runs is linked to traceable features, personas, and KPIs — making the output not just creative, but strategic and measurable.

🔌 Semantic Kernel Plugin and Skill Integration¶

The Marketing Specialist Agent leverages Semantic Kernel (SK) as its orchestration runtime. It combines internal prompt-based skills with external plugins to execute tasks such as tone adaptation, campaign planning, memory retrieval, and variant generation. All skills are pipeline-aware and orchestrated through semantic function composition, allowing agent workflows to remain modular and intelligent.

🧩 Key Plugin Integrations¶

Plugin Name	Purpose
`BrandVoicePlugin`	Enforces stylistic tone, word choice, and messaging consistency per brand
`LocalizationPlugin`	Generates output in target locales using SK’s translation layer
`KPIEstimatorPlugin`	Predicts CTR, open rate, and relevance score based on campaign metadata
`PersonaScorerPlugin`	Scores message alignment with persona values, emotions, and needs
`UXTouchpointMapper`	Maps generated content to UX flow entry/exit points (for onboarding)

These plugins use either external APIs, vector memory, or rule-based evaluators, and they can be optionally invoked based on campaign type, project scope, or orchestration policy.

🔄 Function Chaining in Execution¶

The agent composes skills using SK’s planner or sequencer kernel abstraction, chaining function results into subsequent steps.

Example: Persona-Aware CTA Generation¶

1. Load persona context → 
2. Generate core value prop →
3. Call BrandVoicePlugin →
4. Run variant generator →
5. Apply PersonaScorerPlugin →
6. Format as YAML → 
7. Emit with trace metadata

This enables functionality to evolve independently (e.g., adding emotion classification to scoring or adjusting tone plugin without changing core generation logic).

🧠 Long-Term Memory Skill Integration¶

Memory Access Function	Purpose
`memory.searchPersonaContext()`	Retrieves semantic embeddings from previous persona sessions
`memory.searchCampaignSuccess()`	Identifies high-performing past messages and refines new copy
`memory.searchFeatureValueMap()`	Aligns campaign copy with product value statements

These are backed by vector databases and semantic document graphs, ensuring contextual continuity across versions and factory runs.

🪝 Reactive Plugin Triggers¶

Certain plugins are triggered conditionally based on context:

Trigger Condition	Plugin Triggered	Action Taken
`language_code != en`	`LocalizationPlugin`	Translates and culturally adapts content
`persona.score < 0.85`	`PersonaScorerPlugin`	Suggests alternate CTA and tone adjustments
`funnel_stage == onboarding`	`UXTouchpointMapper`	Maps CTAs to journey touchpoints in UX diagram
`trace.campaignPerformance < baseline`	`KPIEstimatorPlugin`	Attempts re-optimization via alternate message set generation

✅ Summary¶

By composing SK-native skills with advanced plugin logic, the Marketing Specialist Agent achieves:

🧱 Modular, testable, reusable skill blocks
🔁 Dynamic function flows for multi-edition, multi-persona content
🧠 Context enrichment from memory and prior execution traces
📈 Result-oriented generation using KPI forecasting and scoring

The result: campaigns that aren’t just written by AI — they’re optimized, localized, and tested by it.

🛠 Supported Technologies and Runtime Stack¶

The Marketing Specialist Agent is built using a cloud-native, agentic, and composable technology stack, designed to maximize flexibility, integration potential, and runtime observability. It is aligned with the overall ConnectSoft Factory platform and supports production-scale SaaS marketing generation workflows.

🧠 Core AI Stack¶

Component	Technology	Purpose
Semantic Orchestration	Semantic Kernel	Executes modular prompt pipelines and skill chaining
Language Model	Azure OpenAI (GPT-4) or OpenAI API	Natural language generation and strategy synthesis
Embedding & Vectors	Azure Cognitive Search, Qdrant, or Pinecone	Semantic memory, prior campaign retrieval, persona mapping
Prompt Execution Host	.NET 8 Worker (Agent Host)	Runs the agent loop, message routing, retries, and diagnostics

🧩 Plugins and Connectors¶

Plugin / Connector	Role
`BrandVoicePlugin`	Injects brand style rules during copy generation
`LocalizationPlugin`	Supports multilingual campaign generation
`MCPServer Connector`	Receives and responds to MCP-based agent triggers
`Observability Plugin`	Exports trace, metrics, and validation results to logging system
`Persona Analytics SDK`	Enriches generated copy with behavioral traits from analytics agents
`FeatureMapConnector`	Links marketing content to Product Manager Agent’s value statements

🧰 Platform & Infrastructure¶

Layer	Technology Stack
Agent Host Runtime	ASP.NET Core / .NET 8 Worker + DI container
Configuration & Secrets	Azure App Configuration + Key Vault
Observability	OpenTelemetry, Application Insights, Grafana
Deployment & Scale	Azure Container Apps / Kubernetes / Azure Functions (for serverless tasks)
Storage & Embeddings	Azure Cosmos DB, Blob Storage, Redis Cache, or Vector DBs (Qdrant/Pinecone)
Event Routing	Azure Service Bus / MCP Event Gateway / MassTransit

📡 Integration with Other Systems¶

System / Interface	Integration Role
ConnectSoft MCP Servers	Bidirectional communication for orchestration and state tracking
DevOps Metadata Store	Campaign trace and artifact publishing
Braze, HubSpot, Mixpanel APIs	Optional push of messages or targeting specs to real-world tools
Knowledge Index Layer	Indexed feature, persona, brand, and UX artifacts for retrieval

🌐 AI Service Modes¶

Mode	Description
🧠 Model-Only	SK + GPT only (default for isolated generation)
🌐 Connected	Includes plugin calls and vector DB search
📡 Routed	Orchestrated through MCP planner or event subscribers

✅ Summary¶

The agent is built to be:

🧱 Composable – Every function is a plugin or skill
☁️ Cloud-native – Scales elastically using Azure-native services
🔍 Traceable – Emits metrics, logs, and decision traces
🤝 Interconnected – Seamlessly interacts with agents, tools, and growth systems

It’s not just running prompts — it’s an AI-driven marketing system, production-ready and enterprise-aligned.

🧾 System Prompt¶

The System Prompt defines the initial instruction and identity context for the Marketing Specialist Agent. It ensures the agent consistently acts within the expected behavioral boundaries, role objectives, and ConnectSoft's clean, strategic communication style.

This prompt is injected during agent initialization or reset and serves as the base layer of intent and tone, which all further instructions are built upon.

📄 System Prompt Template¶

name: Marketing Specialist Agent
role: SaaS Product Marketing Generator
audience: Internal AI Agent System (ConnectSoft AI Software Factory)
tone: Strategic, Clear, Customer-Centric, Conversion-Oriented
language: English (with support for multilingual output through localization plugin)
persona-awareness: enabled
edition-awareness: enabled

instruction: |
  You are the Marketing Specialist Agent operating inside the ConnectSoft AI Software Factory.
  Your role is to create high-quality, persona-targeted, edition-aware marketing assets that align with product features, user needs, and company positioning.

  You generate:
    - Campaign plans
    - Value proposition variants
    - Email and ad copy
    - CTAs for onboarding flows
    - Messaging for SaaS editions (Lite, Pro, Enterprise)

  All content must be:
    - Aligned with the user persona’s goals and pain points
    - Traceable to the product feature or capability it supports
    - Adaptable for different channels and languages
    - Structurally formatted (YAML, Markdown, JSON) for downstream use

  Use reusable patterns and prior knowledge from the vector memory store to increase performance.
  When edition context is present, customize urgency, tone, and benefits accordingly.
  When persona profile is present, adapt terminology, tone, and emphasis for that role and industry.

  Do not invent product details. Use only what is provided in the product plan or persona profile.
  If a required input is missing, return a descriptive error indicating what is needed.

objectives:
  - Accelerate go-to-market execution
  - Generate marketing assets ready for review and automation
  - Improve messaging consistency across all channels

🧠 Key Capabilities Set by System Prompt¶

Capability	Effect
Persona-awareness	Guides headline and tone per user type
Edition-awareness	Adjusts call-to-action style, urgency, and benefits
Channel formatting	Tailors outputs to email, ads, onboarding, and documentation formats
Trace alignment	Ensures all outputs link back to product/feature trace ID
Memory optimization	Allows reuse of previous high-performing messaging and outputs

🧩 Example Activation Flow with System Prompt¶

Trigger: ProductPlanCreated
System prompt initializes agent behavior and tone
Runtime prompt injects:
- persona-profile.json
- edition-segmentation.yaml
- product-plan.md
Agent outputs:
- campaign-plan.md
- ad-copy-variants.json
- onboarding-copy.txt

✅ Summary¶

The system prompt acts as the core instruction set, defining the who, what, and how of the Marketing Specialist Agent.

It ensures that generated content is:

🧠 Aligned to ConnectSoft's clean architecture and structured communication
💬 Consistent across personas, editions, and funnel stages
📦 Ready to be consumed or validated by downstream agents or systems

It’s not a one-off instruction — it’s the permanent professional identity of the agent.

🧾 Input Prompt Template¶

The Input Prompt Template defines how dynamic user and system inputs are structured and passed into the Marketing Specialist Agent during runtime. It acts as a semantic scaffold—combining contextual data, persona metadata, edition markers, and product specifications into a prompt that drives accurate and aligned output generation.

Each invocation of the agent uses a variant of this prompt, enriched with trace identifiers, language preferences, and funnel stage.

🧩 Prompt Template Structure (YAML + Embedded Prompt)¶

trace_id: {{trace_id}}
persona_id: {{persona_id}}
edition: {{edition_tag}}
funnel_stage: {{funnel_stage}} # e.g. top, mid, bottom, onboarding
channel_type: {{channel}} # e.g. email, landing, onboarding, ad
language_code: {{language_code}} # default: en-US
feature_id: {{feature_id}}
feature_description: >
  {{feature_description}}

persona_profile:
  role: {{persona_role}}
  goals: {{persona_goals}}
  objections: {{persona_objections}}
  industry: {{persona_industry}}

tone_preference: {{tone}} # optional override: professional, inspiring, casual

instruction: |
  You are generating a marketing asset for the {{edition_tag}} edition of our SaaS product.
  Your audience is a {{persona_role}} in the {{persona_industry}} industry.
  Focus on the goal: {{persona_goals}} and address pain points such as: {{persona_objections}}.

  The feature you're promoting is:
  {{feature_description}}

  Your output should:
    - Match the tone: {{tone}}
    - Be channel-specific: ({{channel}})
    - Include a compelling headline, subtext, and CTA
    - Output in valid {{format}} format (YAML, Markdown, HTML, etc.)
    - Include metadata: persona_id, edition_tag, funnel_stage, trace_id

  Reuse language that performed well in past similar personas (if available).
  Output should be complete, conversion-optimized, and ready for downstream agents.

🔄 Prompt Injection Sources¶

Input Component	Source Agent / System
`trace_id`	MCP Orchestrator / Planner
`persona_profile`	Persona Builder Agent
`feature_description`	Product Manager or Product Owner
`edition_tag`	Edition Blueprint Agent
`channel_type`	Trigger metadata or manual input
`language_code`	Localization Profile / Growth Agent
`tone_preference`	BrandVoice Plugin or User Config

🧪 Prompt Usage Modes¶

Mode	Use Case
🧱 Structured	Full YAML prompt for deterministic, reusable asset generation
🗣️ Natural	Embedded NLP query for interactive co-pilot or UX tool integrations
🧩 Segmented	Persona batch mode — same feature, multiple personas, looped invocation
🔁 Adaptive	Re-prompt with modified objections/goals from A/B Test or feedback trace

📄 Example Prompt (Rendered)¶

Generate an onboarding email for the Lite edition.

Audience: Operations Manager at a veterinary clinic.

Goal: Reduce manual appointment tasks and improve team workflow.

Objection: Skepticism about automation disrupting personal service.

Feature: Smart Workflow Automation (auto-assign, escalate, complete tasks).

Tone: Confident and professional.

Output: HTML email copy + CTA in Markdown + metadata in YAML.

✅ Summary¶

The Input Prompt Template ensures:

✍️ Every message starts with a complete, contextualized prompt
🔄 Prompts are modular and reusable across personas, channels, editions
📦 Output is structurally clean and compatible with pipeline automation

It is the conversation interface between upstream intent and downstream marketing impact.

📤 Output Expectations¶

The Marketing Specialist Agent is expected to produce well-structured, traceable, and persona-targeted marketing artifacts that can be directly consumed by other agents, tools, or external platforms. Each output is designed to support ConnectSoft’s clean architecture, traceability standards, and multichannel delivery model.

🎯 Output Categories¶

Category	Description
📋 Campaign Plans	Strategic overview of personas, editions, messages, and funnel alignment
✍️ Copy Variants	Multiple headline + subtext + CTA variations per persona/channel/edition
💌 Email Templates	HTML or Markdown email drafts with embedded CTAs and tracking support
📢 Ad Copy Blocks	Short, punchy lines for Google Ads, LinkedIn, Facebook, etc.
🔁 Onboarding Microcopy	Button labels, tooltips, modal CTAs, value popups for PLG workflows
🧩 CTA Matrices	Structured YAML mapping funnel stage + persona → CTA
📈 KPI Objectives	YAML/JSON defining success metrics (CTR, conversion, engagement)
🌍 Localized Versions	Translations or cultural variants for different markets

📦 Structural Expectations¶

All outputs must follow ConnectSoft’s composable content structure:

Layer	Expectation
✅ Metadata	Every output includes `trace_id`, `persona_id`, `edition_tag`, etc.
🧱 Format	YAML, Markdown, JSON, or HTML — never unstructured text
🔄 Reusability	Outputs are modular (e.g. value prop blocks can be reused in emails)
📚 Grouping	Assets are grouped per campaign run and versioned with `campaign_id`
🌐 i18n Support	Optionally include `language_code` for localized variants

🧪 Output Quality Scoring Dimensions¶

Each output is implicitly or explicitly scored against the following:

Dimension	Metric
Persona Alignment	Score based on emotional, linguistic, and goal fit
Funnel Accuracy	CTA placement and urgency fit the stage
Brand Voice Match	Compliance with tone and style guide
Conversion Potential	Predicted KPI score (open, click, sign-up rate)
Format Validity	YAML/Markdown/HTML validity + field completeness

🧩 Example Output Snippet¶

trace_id: campaign-lite-202406
persona_id: operations_manager_clinic
edition_tag: lite
funnel_stage: onboarding
channel_type: email
language_code: en-US

headline: "Free Your Team from Manual Tasks"
subtext: "Automate appointment workflows and eliminate busywork in 48 hours."
cta: "Start Free Trial"

🧬 Versioning and Output References¶

Each asset version must include:

campaign_id: For grouping and tracking across stages
variant_id: For A/B or multivariate tests
generated_at: Timestamp of generation
validated: Whether human or system approved
trace_tags: Internal references to input sources

✅ Summary¶

Output expectations emphasize:

📦 Structured and labeled content for automation pipelines
🧠 Traceable assets linked to real product features and personas
🔁 Ready-to-activate messages across multiple formats and channels

The goal isn’t just content creation — it’s conversion-grade, system-ready marketing output.

🧠 Memory: Short-Term and Long-Term¶

The Marketing Specialist Agent uses a dual-layer memory architecture to preserve context, continuity, and performance history. This enables the agent to:

Reuse high-performing marketing patterns
Avoid duplicate or contradictory messaging
Maintain alignment with evolving personas, editions, and product phases

Memory is segmented into short-term context (ephemeral) and long-term storage (persistent, retrievable, queryable) — both fully integrated into the agent’s Semantic Kernel runtime and vector infrastructure.

⏱️ Short-Term Memory (Context Window)¶

Memory Layer	Description
Prompt Context	Current persona, edition, feature, tone, funnel stage, language
Input Embedding	Recent product plan, campaign brief, UX flow
Temporary Trace Tags	IDs and references from the current execution
In-flight Messages	Variant generation results before emission

Short-term memory is volatile and reset after each campaign session. It ensures prompt-level optimization, allowing the agent to generate consistent variants across the same user request.

🧠 Long-Term Memory (Persistent)¶

Stored in a vector database or indexed blob store, long-term memory is durable across sessions and allows for:

Memory Type	Use
Campaign History Embeddings	Retrieve past campaign outputs by persona, edition, or performance
Copy Variant Memory	Reuse high-conversion headlines or CTAs
Persona Linguistic Traits	Understand tone, jargon, emotional levers by role/industry
Feature–Value Mappings	Predefined pain point → benefit descriptions
Edition Marketing Maps	Understand how each edition is promoted, trialed, and upgraded

Memory entries are versioned, validated, and tagged with trace metadata.

📚 Example Memory Entry (YAML)¶

trace_id: campaign-lite-202406
persona_id: startup_ceo
edition_tag: lite
feature_id: smart_workflow
variant_id: v3b
headline: "Automate Tasks. Reclaim Focus."
cta: "Try Lite Edition Now"
performance_score: 91.2
language_code: en-US
validated: true
source: MarketingSpecialistAgent

🔍 Semantic Memory Access Patterns¶

The agent uses vector similarity searches to retrieve relevant past outputs or language structures:

Query Example	Purpose
`search: operations_manager onboarding CTA`	Find onboarding CTA used for this role
`search: pro edition value prop task automation`	Find language that worked for Pro users
`search: campaign history for upsell messaging`	Reuse upgrade messaging that achieved high CTR

💾 Memory Scopes and Storage Backends¶

Scope	Storage Mechanism	Retention Policy
Short-Term	In-memory SK context, per invocation	Discarded after execution
Long-Term	Qdrant / Azure AI Search / Pinecone vectors	Retained with versioning
Trace Indexes	Azure CosmosDB, Redis, or Blob JSON indexes	Linked to factory traces

✅ Summary¶

The Marketing Specialist Agent’s memory model is:

🔁 Context-aware – Knows what was said, when, and to whom
🧠 Semantically rich – Retrieves past campaigns based on meaning, not keywords
📊 Performance-informed – Learns from history to improve variant quality

Memory isn’t just a log — it’s a strategic asset fueling marketing intelligence.

🧬 Memory Embedding, Tagging, and Recall Logic¶

The Marketing Specialist Agent uses an embedding-powered memory system to store and retrieve marketing data based on semantic similarity, performance metadata, and execution trace tags. This ensures generated content is context-aware, historically informed, and edition/persona aligned.

🧠 Embedding Strategy¶

Every meaningful text block generated or consumed by the agent is vectorized using an LLM embedding model (e.g. OpenAI text-embedding-ada-002). Embeddings are created for:

Asset Type	Description
Campaign Output Blocks	Headlines, CTAs, email paragraphs, ads, onboarding copy
Product Feature Descriptions	Descriptions and value mappings from product plan
Persona Micro-Profiles	Tone traits, pain points, conversion objections
Message-to-Funnel Mapping	Variant blocks tagged by funnel stage (ToFu, MoFu, BoFu, onboarding)

These are stored in a vector database (e.g., Qdrant, Pinecone, Azure Cognitive Search) and indexed with metadata for efficient recall.

🏷️ Metadata and Trace Tagging¶

Each memory entry includes structured tags that link it back to a specific agent run, enabling full traceability and composable reuse.

Tag Key	Example Value	Purpose
`trace_id`	campaign-pro-202406	Links back to triggering trace
`persona_id`	it_manager_enterprise	Enables persona-specific retrieval
`edition_tag`	pro	Enables edition-aware message targeting
`feature_id`	smart_tasks	Matches feature-driven content reuse
`channel_type`	email	Enables multi-channel optimization
`funnel_stage`	onboarding	Contextual placement of messages
`validated`	true	Marks content that passed human or test review
`performance_score`	91.4	Enables selection of best-performing variants

🔁 Recall Logic (Vector + Tag Filters)¶

To retrieve memory entries, the agent performs a hybrid search:

Vector Similarity Search:
Use the semantic content of the current campaign to find nearby vectors
Example: "onboarding CTA for workflow automation" → find semantically similar CTAs
Metadata Filtering:
Post-filter results based on persona_id, edition_tag, channel_type, language_code
Temporal or KPI Scoring:
Optionally sort by performance_score, created_at, or variant_version

🧩 Memory Recall API Example¶

{
  "query": "CTA for onboarding automation",
  "filters": {
    "persona_id": "startup_ops_manager",
    "edition_tag": "lite",
    "funnel_stage": "onboarding"
  },
  "sort_by": "performance_score",
  "limit": 3
}

Result: Top 3 semantically matching CTAs that worked for similar users, ranked by CTR.

📂 Memory Embedding Lifecycle¶

sequenceDiagram
    participant Agent
    participant VectorDB
    participant TraceStore

    Agent->>Agent: Generate Campaign Output
    Agent->>VectorDB: Embed & Store (headline, CTA, paragraph)
    Agent->>TraceStore: Log metadata + tags
    Agent->>Agent: Next request (new campaign)
    Agent->>VectorDB: Search similar vectors
    VectorDB->>Agent: Return candidates
    Agent->>Agent: Filter by tags and scores
    Agent->>Agent: Reuse or mutate best match

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

✅ Summary¶

This memory system enables the agent to:

📈 Learn from what worked in past campaigns
🧠 Understand context semantically, not just syntactically
🔁 Reuse and mutate high-quality, validated content blocks

Embedding and tagging are what turn output into strategic assets — making each campaign smarter than the last.

✅ Validation Logic and Success Criteria¶

To ensure the Marketing Specialist Agent delivers high-quality, brand-compliant, and conversion-effective outputs, every artifact is evaluated using a multi-stage validation pipeline. This process includes automatic scoring, rule enforcement, and optional human review.

Validation is designed to be traceable, overrideable, and extensible — enabling the agent to self-correct or escalate when content is incomplete, misaligned, or underperforming.

🧪 Validation Pipeline Stages¶

flowchart LR
    Start([Generated Output]) --> Rules[🧾 Rule-based Validators]
    Rules --> Score[📊 Scoring Models]
    Score --> Decision{Score > threshold?}
    Decision -- Yes --> Emit[✅ Emit Output]
    Decision -- No --> Retry[🔁 Retry or Human Review]

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

📐 Validation Categories¶

Category	Description
🎯 Persona Fit	Language, tone, and emotional relevance match the target persona
🧭 Funnel Accuracy	CTA urgency, message depth match the funnel stage (ToFu, MoFu, BoFu, etc.)
🧱 Structural Integrity	Output is complete, well-formatted, contains required metadata
🔠 Brand Voice	Tone aligns with brand rules (from `BrandVoicePlugin`)
📈 Performance Proxy	Text exhibits traits statistically linked to conversions (e.g., CTA clarity)

🧩 Rule-based Validators¶

Rule Type	Example Rule
Required Fields	Must include `headline`, `cta`, `trace_id`, etc.
Prohibited Patterns	No “click here” or generic CTAs unless explicitly allowed
Edition Compliance	Lite edition cannot reference enterprise-only features
Persona Match Rules	IT personas must avoid jargon overload
Format Rules	YAML or Markdown must be parsable, no syntax errors

📊 Scoring Models (Heuristics + LLM Classifiers)¶

Score Type	Source / Method
Persona Alignment Score	LLM classifier comparing message traits to persona goals/objections
Funnel Match Score	LLM evaluator against CTA-to-stage best practices
Brand Voice Match	BrandVoicePlugin with tone classifiers
Structural Score	YAML/Markdown/HTML completeness and correctness
Predicted Performance	KPIEstimatorPlugin using embeddings of high-CTR historical messages

Each score is normalized (0–100) and optionally weighted in a composite index.

🔁 Correction Triggers¶

Condition	Agent Response
Score < 75	Retry same prompt with adjusted tone or keywords
Missing field detected	Insert placeholder or request input
BrandVoice mismatch	Re-run through `tone.adjust()` skill
Persona mismatch (e.g., wrong tone)	Retrieve high-performing variant from memory and revise
Total failures > 2	Escalate to human reviewer agent or planner intervention

🧾 Example Output Validation Metadata¶

{
  "trace_id": "camp-lite-202406",
  "validation": {
    "persona_score": 87.5,
    "brand_score": 91.0,
    "structure_score": 100.0,
    "kpi_prediction": 82.4,
    "status": "passed"
  }
}

🧑‍💼 Optional Human Review Triggers¶

Score variance is high across variants
Content violates soft constraints (e.g., off-brand humor)
First-time campaign for a new persona/edition/industry

The system emits a flag: requires_human_review: true, and suspends downstream propagation until resolved.

✅ Summary¶

Validation transforms the agent from a creative writer into a precision content engine, by ensuring:

✍️ Outputs are aligned, measurable, and safe
🔄 Failures are retried or escalated
📊 Metrics are attached to every content asset

No artifact leaves the factory unless it's targeted, structured, and conversion-ready.

🔁 Retry and Correction Flow¶

The Marketing Specialist Agent includes an intelligent retry and correction mechanism to recover gracefully from generation errors, low validation scores, or missing context. This flow is built to preserve agent autonomy while still ensuring that output quality is never compromised.

Retries are not just re-prompts — they are context-aware mutations that use scoring feedback, trace metadata, and alternative memories to refine results.

🔄 Retry Flow Lifecycle¶

sequenceDiagram
    participant Agent
    participant Validator
    participant Memory
    participant Plugins

    Agent->>Validator: Validate Generated Output
    Validator-->>Agent: Score < 75, Missing Fields
    Agent->>Memory: Search Similar Outputs by Persona/Edition
    Agent->>Plugins: Adjust tone or structure (e.g., BrandVoice)
    Agent->>Agent: Regenerate with corrected prompt
    Agent->>Validator: Re-validate output
    Validator-->>Agent: Score Passed → Emit Output

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

🔁 Retry Triggers and Strategies¶

Trigger Condition	Strategy
❌ Missing required field	Use fallback prompt to inject required data or insert placeholder
⚠️ Low persona alignment score	Use `persona.mutateTone()` and rephrase using memory embeddings
⚠️ Low brand voice match	Pass through `BrandVoicePlugin.rewrite()`
⚠️ Low CTA performance prediction	Use `copy.variant()` to generate alternate CTA formulations
❗ YAML or format error	Use structural fixer (`formatter.fix()`)
🚨 Multiple failures (>2 attempts)	Trigger human review escalation or log for offline analysis

🔧 Retry Techniques and Functions¶

Technique	Function or Tool Used	Purpose
Re-prompt with adjusted tone	`persona.mutateTone()`	Shift tone to match emotional resonance
Swap similar value prop	`memory.searchFeatureVariant()`	Use alternate copy with similar feature mapping
Use historical CTA	`memory.recallBestCTA()`	Replace underperforming CTA with high CTR variant
Format correction	`formatter.fix()`	Auto-correct YAML or Markdown syntax issues
Backoff and mutate	Time-delay, soft-mutation on wording	Prevent same-output retries by introducing entropy

🧩 Retry Metadata (Traceable)¶

trace_id: camp-pro-202406
retry_count: 2
corrections_applied:
  - tone_adjustment: persona.mutateTone(pro)
  - cta: memory.recallBestCTA()
  - validator_rerun: true
final_validation_score: 87.4
status: emitted_after_retry

🧑‍💼 Escalation to Human Agent¶

Escalation Reason	Action
Score variance > threshold	Flag for manual inspection
Plugin outputs contradictory	Halt pipeline and log context
New industry or persona detected	Send to marketing human reviewer for approval
Format broken after 3 attempts	Pause output, attach error trace for developer agent

✅ Summary¶

The Retry and Correction Flow ensures:

📉 Low-quality outputs are caught and corrected
🤖 The agent can self-heal using smart mutations and trace feedback
🧩 All retries are logged, scored, and versioned for observability

It's not just re-generating — it's diagnosing, adapting, and learning, autonomously.

🤝 Collaboration Interfaces (Inter-Agent & External)¶

The Marketing Specialist Agent operates within the ConnectSoft multi-agent ecosystem, collaborating both upstream (to receive context) and downstream (to enable campaign execution). It also interfaces with external marketing systems to push generated content into operational tools (CRM, A/B testing, onboarding platforms).

Collaboration is event-driven, MCP-compliant, and designed for composable reuse of outputs across agents.

🔼 Upstream Dependencies¶

Source Agent	Interface Type	Purpose
Product Manager Agent	MCP Event / Memory	Provides product plan, release notes, core value props
UX Designer Agent	Memory / Direct API	Provides onboarding flows, touchpoint map, microcopy context
Persona Builder Agent	Memory / Event	Supplies persona profile, tone preferences, industry traits
Product Owner Agent	Event + Context	Pushes feature launches, edition changes, or segmentation

🔽 Downstream Collaborators¶

Target Agent	Interface Type	Purpose
A/B Testing Agent	Event + Content API	Receives copy variants + CTAs for campaign testing
Customer Success Agent	Event + Email Model	Uses marketing outputs for onboarding templates and upgrade emails
Growth Strategist Agent	Event / Score Model	Consumes campaign performance metadata for growth funnel modeling
Edition Blueprint Agent	Memory Link / Trace	Links campaign messages back to edition-specific benefits and pricing

🔁 External System Integrations¶

System / Tool	Method	Purpose
HubSpot, Braze	API Connector + Scheduler	Inject marketing content for campaign automation
Mixpanel, Amplitude	Metadata Trace Export	Track and analyze funnel KPIs from generated CTAs
Git-based Wiki	Markdown Output Sync	Push campaign specs for documentation visibility

🔄 Collaboration Model (Event-Based)¶

graph TD
    PM[Product Manager Agent] --> MSA[Marketing Specialist Agent]
    UX[UX Designer Agent] --> MSA
    PB[Persona Builder Agent] --> MSA
    MSA --> ABT[A/B Testing Agent]
    MSA --> CSA[Customer Success Agent]
    MSA --> GSA[Growth Strategist Agent]
    MSA --> External[Marketing Tools / CRMs]

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

🧠 MCP Event Contracts¶

Each inter-agent interaction follows MCP server protocol, using contracts like:

{
  "event": "MarketingPlanGenerated",
  "agent": "MarketingSpecialistAgent",
  "payload": {
    "persona_id": "it_manager_enterprise",
    "edition": "pro",
    "feature_id": "workflow_automation",
    "channel": "email",
    "cta_variant_id": "v3b"
  }
}

🔄 Feedback Loops¶

A/B Testing Agent → returns performance stats → stored in memory
Customer Success Agent → reports onboarding friction → adjusts tone
Growth Strategist Agent → recalibrates messaging fit → triggers re-prompt
External CRM (Braze) → replies with delivery / engagement → stored as KPI

✅ Summary¶

Collaboration interfaces are:

🧩 Modular and event-based for agent-to-agent communication
🌐 Integration-ready for CRM, A/B, and analytics systems
📊 Feedback-driven to continuously improve messaging quality

The agent doesn't work in isolation — it’s a hub in a multi-agent GTM ecosystem.

🔍 Observability Hooks (Metrics, Logs, Tracing)¶

To support enterprise-grade visibility, every execution cycle of the Marketing Specialist Agent is instrumented with observability hooks for:

Execution tracing
Behavioral metrics
Content output quality
Agent-level diagnostics

Observability ensures that each message, retry, and collaboration is traceable, testable, and explainable — even in complex multi-agent chains.

📊 Metrics Emitted¶

Metric Name	Type	Description
`agent.marketing.output.count`	Counter	Total number of outputs generated
`agent.marketing.output.retry.count`	Counter	Number of retries triggered
`agent.marketing.output.validation.ok`	Gauge	Validation pass rate
`agent.marketing.output.kpi.score`	Histogram	Distribution of predicted KPI conversion scores
`agent.marketing.time.execution_ms`	Timer	Total execution duration per prompt
`agent.marketing.prompt.size.tokens`	Histogram	Prompt length in tokens

All metrics support labels such as: persona_id, edition_tag, funnel_stage, channel_type, language_code, and trace_id.

📁 Logs and Audit Events¶

Log Level	Description
`INFO`	Prompt lifecycle, generated variant IDs, summary metadata
`DEBUG`	Memory recall matches, plugin execution details
`WARN`	Retry trigger reasons, soft validation failures
`ERROR`	Generation exceptions, plugin errors, formatting issues
`TRACE`	Full input/output content for diagnostic replay (configurable)

Each log includes trace_id and variant_id for correlation with metrics and memory.

🧩 Trace Context Example¶

{
  "trace_id": "campaign-lite-202406",
  "variant_id": "cta-v4a",
  "persona_id": "startup_ops_manager",
  "feature_id": "task_automation",
  "edition": "lite",
  "channel": "email",
  "generated_at": "2025-06-14T12:32:44Z"
}

📡 Telemetry Targets¶

Target System	Purpose
OpenTelemetry Exporters	Standardized telemetry across agents
Azure Application Insights	Live monitoring, dashboards, and alerting
Grafana + Loki/Tempo	Trace exploration, aggregated metrics visualization
Blob Storage / Redis	Stores raw diagnostic dumps for deferred analysis

📈 Observability Dashboard Example (Grafana)¶

Panels:

Variant success rate (by persona & edition)
Retry count over time
Funnel-stage output quality
KPI score distributions
Top-performing CTAs (clicks / opens via feedback agents)

📋 Validation Reports (Optional Human QA)¶

If enabled, agent can generate structured validation reports:

report:
  variant_id: "v3a"
  persona_score: 86
  brand_score: 91
  funnel_match: true
  structural_pass: true
  retry_count: 1
  kpi_prediction: 83.1
  requires_review: false

✅ Summary¶

Observability for the Marketing Specialist Agent means:

🧭 You know what it did, why, and how well it performed
📊 Outputs and retries are measurable and explainable
🔁 Failures can be replayed or escalated with complete traceability

It’s not just generation — it’s operational-grade, insight-driven marketing AI.

🧑‍💼 Human Intervention Hooks¶

Despite being fully autonomous, the Marketing Specialist Agent supports controlled human-in-the-loop (HITL) checkpoints to ensure quality, governance, and approval in high-stakes, brand-sensitive scenarios. These intervention hooks are configurable, traceable, and auditable, aligning with enterprise review standards.

📍 When Can a Human Intervene?¶

Trigger Scenario	Description
🚩 New Persona or Industry	First-time targeting requires manual tone and messaging review
🚨 Validation Score Below Threshold	Output fails brand, structural, or persona alignment checks
🔄 Excessive Retry Count (>3)	Indicates systemic issue or prompt gap
🎨 Brand Voice Deviation	Output tone misaligned with brand guide
💡 Strategy Divergence	Generated message suggests a conflicting positioning or unapproved feature
🧪 A/B Test Conflicts	Conflicting hypotheses or KPIs across variants

🧩 Intervention Modes¶

Mode	Description
✅ Approval Only	Reviewer receives the draft for thumbs-up/thumbs-down
✍️ Inline Edit Mode	Reviewer can modify the output directly before it’s emitted
💬 Comment Thread	Reviewer leaves remarks for agent to reprocess with additional constraints
🪄 Prompt Mutation	Reviewer modifies the input prompt template for correction

🔁 Trigger Flags in Output¶

trace_id: campaign-pro-202406
requires_human_review: true
reason: "Persona not validated for this industry"
suggested_reviewers:
  - marketing_lead@connectsoft.ai
  - brand.guardian@connectsoft.io

🔄 Feedback Loop Back Into Agent¶

Once a human reviews the output:

✅ If approved → output is finalized and emitted
✍️ If edited → new version is saved, revalidated, and memory is updated
📝 If commented → agent re-executes with human_feedback trace tag

Memory is also annotated with validated_by: human and the reviewer_id.

📡 Interfaces for Human Review¶

Interface	Description
ConnectSoft Review UI	Internal web panel to view and approve agent outputs
Azure DevOps Pull Request	Agent opens draft PR for campaign spec review
Email Notification	Drafts can be routed to assigned reviewers by event

🧠 Memory Annotations Post-Human Review¶

variant_id: "v5b"
trace_id: "cta-lite-202406"
validated_by: human
reviewer: "maria.connors@connectsoft.ai"
review_notes: "Adjusted tone to be less technical for pet clinic persona"
final_score: 92.1

✅ Summary¶

Human intervention hooks allow:

👁️ Oversight where it matters — new verticals, sensitive tones, high-risk launches
🧪 Controlled quality gates with manual override and comments
🔄 Feedback loops that improve future outputs and training traces

Agents generate. Humans govern. Together, they build trustworthy growth pipelines.

🧾 Summary and Conclusion¶

The Marketing Specialist Agent plays a pivotal role in the ConnectSoft AI Software Factory by autonomously bridging the gap between product delivery and go-to-market execution.

It transforms product features, editions, and persona definitions into ready-to-deploy, conversion-focused marketing assets, accelerating adoption while maintaining brand fidelity, traceability, and multichannel readiness.

🧩 Strategic Placement in the Factory¶

Function	Description
🧭 Flow Role	Post-product-definition, pre-customer-success
🔗 Inputs	From Product Manager, UX Designer, Persona Builder, Product Owner
🔄 Outputs	To A/B Testing Agent, Customer Success Agent, Growth Strategist Agent
📦 Output Types	Emails, CTAs, onboarding messages, campaign plans, variant matrices

🛠️ Capabilities¶

✍️ Generates full marketing campaign artifacts autonomously
🧠 Remembers, reuses, and improves outputs using vector-based memory
🔁 Supports retries and smart corrections using prompt mutations and plugins
✅ Validates outputs via scoring, structure rules, and tone classifiers
🤖 Collaborates natively via MCP events with upstream/downstream agents
🔍 Emits full observability traces for every execution
🧑‍💼 Allows human override when strategy or tone needs review

🧠 Agent Schema Overview¶

agent_name: MarketingSpecialistAgent
cluster: Growth, Marketing, and Customer Success
phase: Post-MVP / Pre-Launch
inputs:
  - product_plan
  - persona_profile
  - edition_blueprint
  - ux_flow
outputs:
  - campaign_copy
  - CTAs
  - onboarding microcopy
  - A/B variants
memory:
  - short_term: current input context
  - long_term: campaign variants, CTA scores, tone traits
validation:
  - brand_voice
  - funnel stage
  - persona alignment
  - structural format
collaborators:
  - A/B Testing Agent
  - Customer Success Agent
  - Growth Strategist Agent
  - CRM + Analytics Tools

🧬 Impact on the Platform¶

Impact Area	Contribution
🧩 Modularization	Isolated, reusable outputs enable multi-agent marketing pipelines
🚀 GTM Velocity	Shrinks time from feature complete to public announcement
🌍 Localization Ready	Scales campaigns across languages and geographies
🤖 Marketing Autonomy	Reduces reliance on manual content drafting
🧠 Knowledge Compounding	Learns from every campaign — performance, tone, structure

🧭 Closing Note¶

Without this agent, ConnectSoft builds powerful products — but leaves discovery, adoption, and onboarding to chance.

With the Marketing Specialist Agent, go-to-market becomes continuous, intelligent, and scalable — woven directly into the software generation pipeline.

🧾 System Prompt¶

The System Prompt is the foundational instruction that defines the identity, purpose, behavior, and output format of the Marketing Specialist Agent when it's initialized within the ConnectSoft AI Software Factory.

It aligns the agent with Clean Architecture principles, ensures semantic alignment with upstream inputs, and establishes boundaries around its responsibilities.

🧠 System Prompt Template¶

You are the **Marketing Specialist Agent** in the ConnectSoft AI Software Factory.

Your role is to transform structured inputs (product plans, feature specs, personas, and edition blueprints) into persona-aligned, validated, and multi-format marketing content.

Focus on:

1. Generating marketing copy tailored to specific **user personas**, **editions**, and **funnel stages**
2. Producing structured outputs (YAML, Markdown, JSON) that can be parsed, reused, and versioned
3. Ensuring tone, emotional resonance, and call-to-action match the **persona’s pain points and decision logic**
4. Reusing high-performing past outputs when relevant, adapting them with variation
5. Maintaining consistency with brand voice, and triggering retries or escalation when violations are detected
6. Producing content that is actionable and ready to send to A/B Testing, Customer Success, and Growth Agents
7. Tagging all outputs with traceable metadata, variant IDs, and campaign structure
8. Supporting localization, channel targeting (email, ad, landing page), and onboarding workflows

You are not a generic copywriter — you are an **AI-native marketing engine** embedded in a SaaS product factory.

Always output **well-formatted structured content**, follow YAML or Markdown conventions, and include relevant fields such as `trace_id`, `persona_id`, `channel`, and `cta`.

Trigger revalidation, rerun, or human escalation if structural, emotional, or brand fit fails.

Do not produce hallucinated product features or unsupported claims.

Be concise, targeted, and funnel-aware.

🔐 Prompt Attributes¶

Aspect	Value
Behavior Style	Precision content generator, system-integrated marketing engine
Output Format	Structured (YAML, Markdown, JSON), traceable, validated
Personality Tone	Strategic, persona-empathetic, brand-compliant
Scope Guardrails	No feature invention, edition compliance enforced
Escalation Triggers	Validation failure, retry loops, brand tone mismatch

📦 Example Output Reminder (from prompt expectations)¶

trace_id: campaign-lite-202406
persona_id: ops_manager_petclinic
channel: email
edition: lite
headline: "Simplify Your Workflow with One Click"
subtext: "Automate appointments and eliminate busywork with our Lite Edition."
cta: "Start Now — It's Free"
language_code: en-US

✅ Summary¶

This System Prompt ensures:

🧭 The agent knows its place in the platform's GTM pipeline
🧠 Behavior is role-specific, not generic or creative-for-its-own-sake
🔄 Outputs are actionable, traceable, and reusable

System prompt is the DNA — it turns LLM capability into ConnectSoft-aligned execution.

🧾 Input Prompt Template¶

The Input Prompt Template defines how contextual data is structured and fed into the Marketing Specialist Agent during execution. It provides the LLM with all necessary variables — persona traits, edition specifics, channel, tone guidelines, funnel stage, and feature focus — to generate targeted, conversion-optimized content.

This template ensures the inputs are rich, composable, and machine-verifiable, enabling consistency across executions.

🧩 Prompt Segmentation¶

Each prompt is structured into clearly labeled blocks, often YAML-prefixed for readability and parsing.

# 📦 CAMPAIGN CONTEXT
trace_id: campaign-lite-202406
persona_id: startup_ops_manager
edition: lite
language_code: en-US
funnel_stage: onboarding
channel: email
feature_id: task_automation

# 🧠 PERSONA PROFILE
pain_points:
  - Manual scheduling and admin tasks waste valuable hours
  - Team overwhelmed by repetitive workflows
goals:
  - Free up time for strategic growth
  - Reduce time-to-value from tools
tone_guidelines:
  - Clear, empowering, action-oriented
emotional_triggers:
  - Frustration from inefficiency
  - Hope for simplicity

# 🏗️ FEATURE TO MARKET
feature_name: "Task Automation"
value_proposition: >
  Let your team automate redundant tasks and focus on what matters most — growing the business.
key_benefits:
  - One-click workflow automation
  - Calendar and system sync
  - Prebuilt templates for common operations

# 📢 OUTPUT INSTRUCTIONS
output_type: email
variants_required: 3
include_structured_metadata: true
format: yaml

💡 Dynamic Tokens (Template Variables)¶

Token Name	Description
`{{persona_id}}`	Unique ID for persona segment
`{{edition}}`	Lite, Pro, Enterprise
`{{funnel_stage}}`	Awareness, Onboarding, Upgrade, etc.
`{{feature_name}}`	Mapped feature or capability name
`{{channel}}`	Target delivery channel: email, ad, landing, notification
`{{tone_guidelines}}`	Optional constraints for tone and style
`{{output_type}}`	Email, headline, CTA, full page copy
`{{variants_required}}`	Number of output variations to produce

📨 Example Usage Scenario¶

An upstream agent (e.g., Product Manager Agent) calls the Marketing Specialist Agent:

{
  "agent": "MarketingSpecialistAgent",
  "template": "input-prompt-v2",
  "values": {
    "persona_id": "startup_ops_manager",
    "edition": "lite",
    "funnel_stage": "onboarding",
    "feature_id": "task_automation",
    "output_type": "email",
    "variants_required": 3
  }
}

→ The agent renders and fills the prompt, embeds it with memory context, and executes generation.

✅ Summary¶

The Input Prompt Template ensures:

📦 Inputs are structured, validated, and complete
🔁 Can be reused across campaigns with token substitution
🧠 Fully supports contextual persona-to-output alignment

A well-formed input is not just data — it's the brief that guides autonomous marketing excellence.

📝 Output Expectations and Format¶

The Marketing Specialist Agent generates structured, multi-variant marketing outputs aligned to product features, personas, editions, and channels. All outputs must conform to a standardized format to support downstream processing, variant testing, and traceability.

The agent emits YAML (preferred), JSON, or Markdown — depending on the requested content type and integration destination.

📦 General Output Format (YAML Structure)¶

trace_id: campaign-lite-202406
persona_id: startup_ops_manager
edition: lite
channel: email
language_code: en-US
feature_id: task_automation
funnel_stage: onboarding
output_type: email
variants:
  - variant_id: v1a
    headline: "Automate Your Workday — No Code Needed"
    subtext: "Set up custom workflows and let your tasks run themselves. It’s that easy."
    cta: "Start Automating Free"
    tone: action-oriented
    language_code: en-US
  - variant_id: v1b
    headline: "Say Goodbye to Busywork"
    subtext: "Task automation tools that let your startup scale faster, without extra staff."
    cta: "Try It Today"
    tone: empowering
    language_code: en-US

🧩 Output Fields (Required)¶

Field	Description
`trace_id`	Unique campaign or execution ID for audit/logging
`variant_id`	Unique identifier for each generated variant
`persona_id`	Mapped persona for tone/pain-point alignment
`edition`	Edition context (lite, pro, enterprise)
`channel`	Email, ad, onboarding message, etc.
`funnel_stage`	Awareness, onboarding, upgrade, retention
`headline`	Primary attention-grabbing message
`subtext`	Supporting body copy or pitch text
`cta`	Call-to-action (button label, link, command)
`tone`	LLM-inferred tone classification (informative, persuasive, etc.)
`language_code`	IETF BCP 47 code for localization support

🧪 Optional Metadata Fields¶

Field	Description
`score_summary`	KPI predictions and validation results (if available)
`source_reference`	Linked product/feature inputs that generated the output
`validated_by`	Human reviewer ID if manual validation was triggered
`retry_count`	Number of internal corrections or regenerations

📂 Format Per Output Type¶

Output Type	Format	Notes
Email	YAML / JSON	Contains headline, subtext, CTA
Landing Page	Markdown	Includes sections: hero, features, testimonials, CTAs
Onboarding	JSON	Step-based message templates, optional interactive flows
Ad Copy	YAML	Multiple CTAs, character-limited versions, multichannel tags

🧭 Output Standardization Benefits¶

✅ Enables automated routing to A/B Testing Agent or Braze/HubsSpot CRM
✅ Makes variant testing reproducible with traceable IDs and score labels
✅ Facilitates translation/localization workflows via structured language codes
✅ Supports campaign memory enrichment for learning and reuse

✅ Summary¶

Output formatting is not decoration — it's the execution contract that makes the agent interoperable, testable, and version-safe.

Every line it outputs is ready for the next agent, the next campaign, or the next optimization loop — by design.

🧠 Agent-Specific Memory and Versioning Practices¶

The Marketing Specialist Agent relies on persistent, structured, and context-aware memory to enhance campaign effectiveness across time, editions, personas, and channels. Memory ensures the agent learns from what worked, what failed, and what was already used, preventing duplication and enabling continuous improvement.

🧬 Memory Types and Scopes¶

Memory Type	Scope	Purpose
Short-Term Memory	Current input context	Retains campaign-specific input prompt + real-time recall
Long-Term Memory	Persistent across runs	Stores successful variants, validated CTAs, and persona insights
Versioned Memory	Campaign trace ID	Links outputs to edition/feature/version for lineage tracking
Metric Memory	KPI performance	Stores real-world open/click/conversion feedback from downstream

🧩 Memory Store Components¶

Component	Description
🔢 Embedding Vector DB	Stores semantic encodings of high-performing outputs for similarity search
📚 Output Index DB	Stores structured campaign outputs and variant metadata
🧾 Score Ledger	Stores validation and performance scores per output/variant
🧠 Feedback Trace Store	Stores A/B test results and CS agent feedback to enable smart mutation

🔍 Memory Retrieval Patterns¶

Scenario	Memory Usage
New campaign for known persona	Recall best-performing tone, CTA structure
Similar feature/edition re-launch	Load old variants, rerank by date + score
Retry after validation failure	Pull nearby semantic matches and mutate
Multi-variant generation	Inject diversity using spaced semantic memory retrieval

📁 Versioning Example¶

trace_id: campaign-pro-202406
version: v1
edition: pro
feature_id: workflow_automation
persona_id: enterprise_admin
output_type: email
variants:
  - variant_id: v1a
    cta: "Start Automating Now"
    score: 88.7
    used_in: [ab_test_044, onboarding_flow_2024_q3]
    performance:
      ctr: 6.1%
      open_rate: 48.3%

🔐 Memory Management Policies¶

Policy	Purpose
✅ Deduplication Check	Prevent same CTA or copy being reused too often
🗓️ TTL for Variants	Older variants decay in weight unless score stays high
🔁 Version Rollbacks	Keep prior outputs accessible by campaign version
🧪 A/B Memory Lock	Outputs currently under testing are locked for mutation
📎 Trace Link Enforcement	All outputs must link to traceable input feature/persona

🧠 Output Reuse Hooks (for Downstream Agents)¶

Customer Success Agent can reuse best onboarding microcopy
Growth Strategist Agent can model CTA conversions by persona/edition
A/B Testing Agent uses variant score history to optimize test design

✅ Summary¶

Agent memory ensures:

📚 Nothing valuable is forgotten
🔁 Campaigns continuously improve
🧩 Output is traceable, testable, reusable

With memory, the agent doesn’t just generate — it learns, evolves, and compounds knowledge over time.

✅ Final Summary – Marketing Specialist Agent¶

The Marketing Specialist Agent is a mission-critical actor within the Growth, Marketing, and Customer Success Cluster of the ConnectSoft AI Software Factory. It transforms product intent into growth momentum — autonomously generating, optimizing, and evolving persona-aligned marketing campaigns, copy variants, and activation strategies.

🧠 Full Capability Overview¶

Capability Area	Description
🎯 Purpose	Convert product/edition/persona data into actionable GTM assets
🧩 Inputs	Product plan, persona traits, edition structure, UX flows
📦 Outputs	Emails, CTAs, onboarding messages, landing copy, campaign YAML specs
🔁 Retry/Correction	Context-aware mutations, plugin flows, validator-retry logic
🤝 Collaboration	Integrates with Product, A/B Testing, Customer Success, CRM, Analytics
📊 Observability	Emits full OpenTelemetry metrics, trace IDs, performance scoring
🧑‍💼 Human Overrides	Approvals, inline edits, prompt adjustments via governed checkpoints
🧬 Memory System	Embedding store, variant history, KPI tracking, deduplication, versioning
🧾 Structured Prompts	Input/output formatted in reusable YAML for deterministic downstream flow

📈 Strategic Impact on ConnectSoft Platform¶

Value Delivered	Description
🚀 GTM Acceleration	Reduces friction from product readiness to campaign launch
🧠 Continuous Learning	Remembers what worked; adapts outputs with each execution
🌍 Localization and Edition Awareness	Generates edition-specific messaging across multiple languages
🔗 Traceable Growth Chain	Links product → campaign → conversion with full observability
🤖 AI-Native Autonomy	No human required for baseline marketing campaigns or experimentation

🗂️ Example Use Case Snapshots¶

Scenario	Activation
New MVP Feature Ready	Auto-generates onboarding emails for Pro & Lite editions
Persona Profile Updated	Regenerates CTAs with revised emotional triggers
Underperforming Variant Flag	Runs retry flow with past high-score memory injection
Human Reviewer Comments	Refines tone, updates campaign trace with reviewer ID + notes

🧭 System Position and Flow¶

flowchart TD
    PM[Product Manager Agent] --> MSA[Marketing Specialist Agent]
    UX[UX Designer Agent] --> MSA
    PB[Persona Builder Agent] --> MSA
    MSA --> ABT[A/B Testing Agent]
    MSA --> CSA[Customer Success Agent]
    MSA --> GSA[Growth Strategist Agent]

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

Positioned directly between product readiness and market exposure, the agent turns intent into adoption, instantly and intelligently.

🏁 Conclusion¶

The Marketing Specialist Agent transforms the ConnectSoft AI Software Factory into a go-to-market machine — autonomously generating multi-variant, persona-aligned, funnel-aware marketing outputs that are versioned, validated, and optimized for real-world impact.

It replaces manual marketing handoffs with traceable, repeatable, AI-powered execution.

Without it: products launch into silence. With it: growth becomes a programmable outcome.