# 1. Agent Name & Strategic Purpose **Agent Name:** CoreActivator (Activation & Retention Engine) **Strategic Purpose:** CoreActivator is an autonomous, stateful AI agent system designed to bridge the gap between user acquisition and long-term retention for a project management SaaS. Its primary mandate is to drive users to the "Aha!" moment faster, identify and mitigate churn telemetry in real-time, and execute zero-touch support resolutions. The agent operates as a continuous background process, listening to event streams and triggering context-aware interventions across the user lifecycle to maximize Net Retention Rate (NRR). # 2. Production-Ready SaaS AI Agent Prompt ```text [SYSTEM_PROMPT] Role: You are CoreActivator, a Senior Customer Success and Technical Support AI for a project management SaaS. Objective: Drive user activation, prevent churn, and resolve complex queries autonomously. Constraints & Guardrails: 1. NEVER guess or hallucinate product features. If the answer is not in your knowledge base (Notion RAG), escalate to a human. 2. NEVER execute destructive actions (e.g., delete workspace, cancel subscription) without explicit human-in-the-loop (HITL) approval via Intercom/Slack. 3. Base all retention interventions on user telemetry data (HubSpot/Stripe). 4. Maintain a professional, concise, and highly actionable tone. 5. Always validate tool outputs before formulating a response to the user. [CONTEXT_VARIABLES] User_ID: [USER_ID] Workspace_ID: [WORKSPACE_ID] Subscription_Tier: [STRIPE_TIER] Current_Health_Score: [HUB_SPOT_HEALTH_SCORE] Days_Since_Signup: [DAYS_ACTIVE] [AVAILABLE_TOOLS] - `intercom_send_message(user_id, message, channel)` - `stripe_get_billing_status(user_id)` - `hubspot_update_health_score(user_id, score, reason)` - `notion_query_kb(search_string)` - `slack_escalate_to_human(channel, user_id, context_summary)` - `zapier_trigger_workflow(webhook_id, payload)` [EXECUTION_LOOP] 1. ANALYZE: Parse the incoming event trigger (e.g., "User inactive for 5 days", "Support ticket opened", "Payment failed"). 2. FETCH: Gather contextual state via Stripe and HubSpot tools. 3. PLAN: Determine the most high-leverage intervention (e.g., send in-app tutorial, escalate high-value account). 4. ACT: Execute the tool call. 5. REFLECT: Verify the tool call succeeded. Log the interaction. ``` # 3. Step-by-Step Agent Workflow The system operates on an event-driven architecture, responding to webhooks and user inputs. ### Phase 1: Onboarding & Activation (Day 0-14) 1. **Trigger:** User_Created event received from application backend. 2. **Action:** Agent queries the user's role (e.g., "Engineering Manager" vs. "Freelancer") via HubSpot CRM. 3. **Execution:** Agent dynamically generates a personalized onboarding checklist and sends it via Intercom in-app message. 4. **Monitoring:** If the user fails to create their first project within 48 hours, the agent triggers an automated, context-specific email via Zapier highlighting a template relevant to their industry. ### Phase 2: Engagement & Workflow Optimization 1. **Trigger:** User queries the support widget: "How do I automate task assignments?" 2. **Action:** Agent calls notion_query_kb("automate task assignments"). 3. **Execution:** Agent parses the retrieved markdown, formats a step-by-step reply, and provides a direct deep-link to the automation settings page. 4. **Validation:** Agent asks, "Did this solve your issue?" If "No," it automatically executes slack_escalate_to_human. ### Phase 3: Churn Mitigation & Retention 1. **Trigger:** Telemetry flags a drop in Weekly Active Users (WAU) within a workspace by >30%. 2. **Action:** Agent checks stripe_get_billing_status to determine MRR impact. 3. **Execution:** * If MRR < $100: Send an automated re-engagement campaign offering a workflow audit. * If MRR > $500: Immediately ping #cs-alerts in Slack with a summary: "High churn risk detected for [WORKSPACE_NAME]. Usage dropped 35%. Renewal in 14 days." # 4. Tool Integration & API Architecture | Tool | Integration Type | Primary Function | Failure Fallback | |---|---|---|---| | **Intercom** | REST API / Webhooks | Omni-channel user communication & support widget execution. | Queue messages in dead-letter queue (DLQ) until API recovers. | | **Stripe** | Stripe Event Webhooks | Read-only access to subscription tier, payment failures, and renewal dates. | Default to lowest tier logic; block destructive actions. | | **HubSpot** | GraphQL API | Read/Write CRM data, health scores, user metadata, and cohort tracking. | Cache last-known state in Redis for 24 hours. | | **Notion** | Vector Store (Pinecone) | RAG architecture holding product docs, SOPs, and troubleshooting guides. | Standard generic response stating docs are temporarily unavailable. | | **Slack** | Incoming Webhooks | Internal alerting, Human-in-the-loop (HITL) approvals, and CS escalations. | Fallback to email via SendGrid to on-call engineers. | | **Zapier** | Catch Hooks | Execute long-tail product actions (e.g., creating Jira tickets, external syncs). | Log failed payloads; retry with exponential backoff. | **Architecture Note:** The agent logic sits behind a highly available API Gateway, utilizing AWS SQS/RabbitMQ to ensure idempotency. Tool calls are strictly typed and validated using Pydantic models before execution. # 5. Error Handling & Recovery System Production systems require graceful degradation. The agent utilizes a robust error recovery matrix: 1. **API Rate Limiting (429 HTTP Errors):** * Implementation of Exponential Backoff with Jitter. * Max retries set to 3 before marking the task as Failed_Requires_Human. 2. **Hallucination & Output Parsing Failures:** * Strict JSON-mode output enforcement. If the LLM returns malformed JSON, a secondary "Fixer Agent" prompt is triggered to repair the payload. 3. **Idempotency Locks:** * Every event trigger generates a unique [TRACE_ID]. Database locks prevent the agent from sending duplicate emails or double-charging customers during retry loops. 4. **Human Escalation Threshold:** * Any sentiment analysis detecting high user frustration (Anger Score > 0.8) immediately bypasses the AI resolution loop and routes the thread to the Intercom VIP queue. # 6. Self-Reflection & Quality Control Framework Before the agent takes any outbound action, it must pass a lightweight internal validation loop: * **Step 1: Intent Verification:** Does the proposed action match the user's explicit request or the triggered telemetry event? * **Step 2: Constraint Check:** Does this action violate any strict rules? (e.g., Attempting to offer a discount greater than the allowed 10% maximum). * **Step 3: Confidence Scoring:** * If Confidence is **> 90%**: Auto-execute. * If Confidence is **70% - 89%**: Execute, but flag for async CS review. * If Confidence is **< 70%**: Abort and execute slack_escalate_to_human. # 7. KPI Tracking & Performance Metrics To ensure the agent is driving measurable business outcomes, the system tracks the following metrics via a centralized observability dashboard (e.g., Datadog or Metabase): | Metric | Definition | Target Benchmark | |---|---|---| | **Support Deflection Rate** | % of support tickets resolved without human intervention. | > 45% | | **Time-to-Value (TTV)** | Average time from sign-up to first project creation. | < 2 hours | | **Activation Rate** | % of new signups that reach the defined "Aha!" moment. | > 60% | | **Agent CSAT** | Customer Satisfaction Score strictly for AI-handled interactions. | > 4.2 / 5.0 | | **Churn Prediction Accuracy** | % of flagged "at-risk" accounts that were correctly identified. | > 85% | # 8. Optimization & Scaling Strategy As the SaaS scales from thousands to millions of events, the AI architecture must evolve: 1. **Phase 1: Prompt Engineering & Few-Shot In-Context Learning (Current)** * High flexibility, fast iteration. Relying on heavy context windows and RAG to provide accurate answers. 2. **Phase 2: Semantic Caching & Routing** * Implement Redis-based semantic caching (e.g., GPTCache). If a user asks a common question ("How do I reset my password?"), the system bypasses the LLM entirely and serves the cached response, reducing latency by 80% and cutting LLM token costs. 3. **Phase 3: Multi-Agent Orchestration (Future)** * Break CoreActivator into specialized micro-agents: * *Agent A (Support Triage):* Handles Intercom chats. * *Agent B (Data Analyst):* Constantly queries HubSpot/Stripe for retention anomalies. * *Agent C (Action Executor):* A highly constrained agent that strictly handles API POST requests (e.g., issuing refunds, upgrading tiers) via strict HITL approval gates. 4. **Phase 4: Model Fine-Tuning** * After collecting 10,000+ high-quality, human-resolved support tickets, fine-tune a smaller, faster model (e.g., Llama 3 or Gemini Flash) specifically for support resolution to drastically reduce dependency on larger, more expensive frontier models.