{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_agnetic","slug":"agnetic","name":"Agnetic","type":"product","url":"https://www.agnetic.ai","page_url":"https://unfragile.ai/agnetic","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_agnetic__cap_0","uri":"capability://text.generation.language.behavior.driven.message.personalization.engine","name":"behavior-driven message personalization engine","description":"Agnetic analyzes customer interaction history, engagement patterns, and preference signals to dynamically generate and adapt marketing message copy in real-time. Rather than static template variables, the system uses behavioral data (email open rates, click patterns, support ticket sentiment, product usage) to select messaging tone, content focus, and call-to-action variants that match individual customer context. This operates across email, SMS, and web channels with unified customer profiles.","intents":["I need to send the same campaign to 10,000 customers but have each message feel personally relevant to their specific situation","I want to automatically adjust my outreach tone based on whether a customer is actively engaged or at risk of churning","I need to test different messaging approaches without manually creating dozens of campaign variants"],"best_for":["B2B marketing teams managing large customer bases with diverse segments","Customer success teams needing to personalize retention outreach at scale","Product-led growth companies with rich behavioral data to leverage"],"limitations":["Personalization quality depends on data completeness — sparse customer interaction history limits adaptation effectiveness","No A/B testing framework built-in; requires external analytics to measure personalization impact","Real-time personalization adds processing latency (~500ms-2s per message generation depending on data volume)"],"requires":["Customer data platform or CRM with historical interaction logs (email, support, product usage)","API connectivity to customer data sources for real-time behavior ingestion","Minimum 50-100 customer interactions per segment to establish reliable behavior patterns"],"input_types":["structured customer profiles (JSON/CSV with demographics, lifecycle stage)","interaction event streams (email opens, clicks, support tickets, product events)","campaign templates with personalization placeholders"],"output_types":["personalized message text (email body, SMS content, web copy)","structured campaign payloads ready for multi-channel delivery","personalization metadata (confidence scores, selected variant reasoning)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_agnetic__cap_1","uri":"capability://automation.workflow.multi.channel.campaign.orchestration.with.intelligent.scheduling","name":"multi-channel campaign orchestration with intelligent scheduling","description":"Agnetic coordinates campaign delivery across email, SMS, and web channels with AI-driven timing optimization that accounts for individual customer timezone, engagement history, and channel preference. The system learns which channels and send times yield highest engagement per customer segment and automatically sequences messages to avoid fatigue while maintaining campaign momentum. Orchestration rules can be conditional based on customer actions (e.g., if email not opened in 24h, send SMS reminder).","intents":["I want to run a multi-step nurture campaign that automatically adjusts timing and channel based on how each customer responds","I need to send campaigns at optimal times for each customer without manually calculating timezones and preferences","I want to prevent campaign fatigue by limiting message frequency while still hitting engagement targets"],"best_for":["Marketing operations teams managing complex multi-touch campaigns","Global companies with distributed customer bases across timezones","Teams transitioning from manual campaign scheduling to automated orchestration"],"limitations":["Requires historical engagement data to train timing models — new customer segments may use default timing until sufficient data accumulates","Channel preference learning is reactive; cannot predict channel preference for customers with no prior interaction history","Conditional logic limited to simple if/then rules; no support for complex branching workflows with multiple decision points"],"requires":["Integration with email service provider (SMTP, SendGrid, Mailgun) and SMS gateway (Twilio, Nexmo)","Customer timezone and channel preference data in CRM or customer data platform","Minimum 2-4 weeks of historical engagement data per segment for timing optimization"],"input_types":["campaign definition (message templates, channel selection, sequence steps)","customer segment data with timezone and channel preferences","engagement history (open rates, click rates, conversion rates by channel and time)"],"output_types":["scheduled message queue with optimized send times per customer","delivery status reports (sent, bounced, opened, clicked by channel)","engagement metrics and channel performance analytics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_agnetic__cap_10","uri":"capability://automation.workflow.customer.lifecycle.stage.automation.with.stage.specific.workflows","name":"customer lifecycle stage automation with stage-specific workflows","description":"Agnetic automatically classifies customers into lifecycle stages (prospect, trial, customer, at-risk, churned) based on behavioral signals and engagement patterns, then triggers stage-specific automation workflows. Each stage has predefined campaign sequences, messaging tone, and channel preferences. When a customer's behavior indicates stage transition (e.g., trial signup to paid customer, active customer to at-risk), the system automatically moves them to the new stage and initiates the corresponding workflow. Workflows can include email sequences, SMS alerts, support escalations, or sales outreach.","intents":["I want to automatically send different campaigns to prospects vs. customers vs. at-risk customers without manually managing segments","I need to trigger specific actions when a customer moves from trial to paid (e.g., send onboarding sequence, assign success manager)","I want to ensure every customer gets appropriate engagement based on their lifecycle stage without manual intervention"],"best_for":["SaaS companies with distinct customer lifecycle stages and stage-specific engagement strategies","Organizations with complex onboarding and retention workflows that require automation","Teams managing large customer bases where manual lifecycle management is not scalable"],"limitations":["Lifecycle stage classification depends on behavioral signal quality — sparse data results in misclassification","Stage transition detection has latency — customers may be in wrong stage for 24-48 hours after behavior change","Workflows are linear sequences; cannot express complex conditional logic based on multiple customer attributes","Stage definitions are organization-specific; requires careful configuration to match actual customer journey"],"requires":["Clear definition of lifecycle stages and behavioral signals that indicate each stage","Engagement tracking (login frequency, feature usage, support interactions, purchase history)","Predefined campaign sequences and workflows for each stage","CRM integration to track stage transitions and trigger workflows"],"input_types":["customer engagement history (logins, feature usage, support interactions, purchases)","lifecycle stage definitions (behavioral criteria for each stage)","workflow definitions (email sequences, SMS alerts, support escalations per stage)"],"output_types":["customer lifecycle stage classification (prospect, trial, customer, at-risk, churned)","stage transition events (customer moved from trial to customer)","workflow execution logs (which campaigns were sent, when, to which customers)","lifecycle analytics (distribution of customers across stages, stage transition rates)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_agnetic__cap_11","uri":"capability://automation.workflow.email.deliverability.monitoring.with.bounce.and.complaint.handling","name":"email deliverability monitoring with bounce and complaint handling","description":"Agnetic monitors email delivery metrics (bounce rate, complaint rate, spam folder placement) and automatically handles bounces and complaints to maintain sender reputation. Hard bounces (invalid email addresses) are flagged and removed from future campaigns. Soft bounces (temporary delivery failures) are retried with exponential backoff. Complaints (spam reports) trigger automatic list suppression and may trigger customer support outreach. The system tracks sender reputation metrics (SPF, DKIM, DMARC alignment) and provides recommendations to improve deliverability.","intents":["I need to ensure my emails reach the inbox and don't get marked as spam","I want to automatically remove invalid email addresses so I don't waste sends on bounces","I need to understand why my emails are being marked as spam and how to improve deliverability"],"best_for":["Marketing teams sending high-volume email campaigns who need to maintain sender reputation","Organizations with large email lists that accumulate invalid addresses over time","Teams concerned about email deliverability and inbox placement"],"limitations":["Bounce and complaint handling is reactive; cannot prevent bounces before they occur","Spam folder placement depends on ISP algorithms that are not fully transparent; recommendations are based on best practices","Complaint handling may be too aggressive (removing customers from all future campaigns) or too lenient (allowing repeat complainers)","Requires integration with email service provider to access bounce and complaint data"],"requires":["Integration with email service provider (SendGrid, Mailgun, Amazon SES) with bounce and complaint webhooks","SPF, DKIM, and DMARC records configured for sending domain","Email list with valid email addresses (initial validation recommended)"],"input_types":["email send events (recipient, timestamp, email address)","bounce notifications (hard bounce, soft bounce, bounce reason)","complaint notifications (spam report, unsubscribe)"],"output_types":["deliverability metrics dashboard (bounce rate, complaint rate, inbox placement rate)","bounce and complaint handling logs (actions taken per event)","sender reputation report (SPF/DKIM/DMARC alignment, authentication status)","deliverability recommendations (list cleaning, authentication setup, sending practices)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_agnetic__cap_2","uri":"capability://memory.knowledge.unified.customer.data.integration.with.support.ticket.context","name":"unified customer data integration with support ticket context","description":"Agnetic ingests customer data from CRM, support ticketing systems, and product analytics into a unified customer profile that marketing and support teams access through a shared interface. The system normalizes customer records across sources (deduplicating on email, phone, company domain) and enriches profiles with support ticket sentiment, resolution history, and support agent notes. This enables marketing campaigns to reference recent support interactions and support teams to see active marketing campaigns affecting the same customer.","intents":["I need my marketing team to see what support issues a customer has had before sending them a sales campaign","I want to automatically pause marketing to customers who just had a negative support experience","I need a single source of truth for customer data instead of managing separate systems for marketing and support"],"best_for":["Companies with separate marketing and support teams that need unified customer visibility","B2B SaaS companies where support interactions directly impact customer lifetime value","Organizations struggling with data silos between marketing and customer success functions"],"limitations":["Data synchronization latency — support ticket updates may take 5-15 minutes to reflect in marketing profiles","Requires manual configuration of field mappings between CRM and support system; no automatic schema detection","Limited to 2-3 integrated data sources; adding additional systems requires custom API development","No built-in conflict resolution for duplicate customer records — requires manual review for edge cases"],"requires":["API access to CRM system (Salesforce, HubSpot, Pipedrive) with read/write permissions","API access to support ticketing system (Zendesk, Intercom, Freshdesk) with ticket and customer data endpoints","OAuth or API key authentication for both systems","Data mapping configuration (CRM field names to Agnetic profile schema)"],"input_types":["customer records from CRM (name, email, company, lifecycle stage, custom fields)","support tickets with metadata (subject, resolution time, sentiment, agent notes)","product usage events (optional, for engagement scoring)"],"output_types":["unified customer profile (merged identity, enriched attributes, support history summary)","customer timeline (chronological view of marketing interactions and support tickets)","segmentation rules based on support metrics (e.g., customers with unresolved tickets)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_agnetic__cap_3","uri":"capability://planning.reasoning.ai.driven.customer.churn.risk.scoring.and.intervention.automation","name":"ai-driven customer churn risk scoring and intervention automation","description":"Agnetic analyzes customer engagement trends, support ticket frequency, product usage decline, and renewal date proximity to calculate a churn risk score for each customer. When risk exceeds a threshold, the system automatically triggers targeted retention campaigns with messaging tailored to the likely churn reason (e.g., feature request not addressed, competitor comparison, pricing concerns). Intervention campaigns can include special offers, feature education, or direct outreach from customer success managers.","intents":["I want to automatically identify customers at risk of churning before they cancel","I need to send different retention messages to customers churning for different reasons (price vs. feature gap vs. poor support)","I want to trigger manual intervention (customer success call) for high-value customers showing churn signals"],"best_for":["SaaS companies with subscription revenue models where churn prediction directly impacts ARR","Customer success teams managing large customer bases and needing to prioritize at-risk accounts","Companies with sufficient historical churn data (100+ churned customers) to train predictive models"],"limitations":["Churn prediction accuracy depends on data quality and historical churn patterns — models may not generalize to new customer segments","Requires 6-12 months of historical data to establish reliable trend baselines for engagement decline detection","Cannot predict churn caused by external factors (company acquisition, market shift) that don't manifest in behavioral signals","Intervention campaigns may inadvertently trigger churn if poorly targeted (e.g., aggressive discounting to price-sensitive customers)"],"requires":["Historical customer data including churn events with dates","Engagement metrics (login frequency, feature usage, support ticket count) tracked over time","Product usage data or event stream showing feature adoption and activity trends","Customer attributes (plan tier, contract value, tenure, industry) for segmentation"],"input_types":["customer engagement history (login dates, feature usage counts, session duration)","support interaction data (ticket count, resolution time, sentiment)","product usage events (feature adoption, activity decline)","customer attributes (plan tier, contract value, renewal date, industry)"],"output_types":["churn risk score per customer (0-100 scale with confidence interval)","churn reason classification (price sensitivity, feature gap, support quality, competitor threat)","automated intervention campaign trigger (email sequence, SMS alert, support escalation)","churn prediction report with cohort analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_agnetic__cap_4","uri":"capability://text.generation.language.dynamic.content.generation.for.email.and.sms.templates","name":"dynamic content generation for email and sms templates","description":"Agnetic provides a template editor that supports dynamic variable insertion, conditional blocks, and AI-assisted copy suggestions. Templates can reference customer profile data (name, company, plan tier), behavioral data (recent product features used, support ticket topics), and campaign context (offer amount, expiration date). The system generates template preview variations showing how different customer segments will see the final message, enabling marketers to validate personalization before sending.","intents":["I want to create email templates that automatically fill in customer-specific details without manually creating dozens of variants","I need to show different message sections to different customer segments (e.g., show pricing to prospects, show ROI to customers)","I want to preview how my template will render for different customer types before sending to the full list"],"best_for":["Marketing teams without coding skills who need to create personalized campaigns","Agencies managing campaigns for multiple clients with different messaging needs","Teams transitioning from static email templates to dynamic, data-driven messaging"],"limitations":["Template preview only shows static variations; cannot simulate real-time behavioral personalization","Conditional logic limited to simple if/then rules based on customer attributes; no complex nested conditions","No built-in A/B testing framework — requires external analytics to measure template performance","Dynamic variable insertion requires exact field name matching; no fuzzy matching or fallback values for missing data"],"requires":["Customer data with standardized field names (first_name, company_name, plan_tier, etc.)","Email service provider integration for template rendering and delivery","Basic understanding of template syntax (e.g., {{variable_name}}, {{#if condition}}...{{/if}})"],"input_types":["template markup with variable placeholders and conditional blocks","customer profile data (structured JSON or CSV)","campaign metadata (offer details, expiration dates, call-to-action URLs)"],"output_types":["rendered email HTML with personalized content","SMS text with dynamic variable substitution","template preview variations for different customer segments","validation report (missing variables, broken conditionals)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_agnetic__cap_5","uri":"capability://data.processing.analysis.campaign.performance.analytics.with.attribution.modeling","name":"campaign performance analytics with attribution modeling","description":"Agnetic tracks campaign metrics (open rate, click rate, conversion rate, revenue attributed) across all channels and provides dashboards showing performance by segment, channel, and campaign variant. The system supports multi-touch attribution modeling that credits multiple touchpoints in a customer journey rather than last-click attribution, enabling marketers to understand which campaigns and channels drive actual revenue impact. Attribution models can be configured as first-touch, last-touch, linear, or time-decay.","intents":["I need to understand which campaigns and channels are actually driving revenue, not just engagement metrics","I want to see how different touchpoints in a customer journey contribute to conversion","I need to compare performance across different segments and campaign types to optimize budget allocation"],"best_for":["Marketing teams with multi-touch customer journeys who need to understand true campaign ROI","B2B companies with long sales cycles where attribution to a single touchpoint is misleading","Data-driven marketing organizations that need detailed performance analytics to justify marketing spend"],"limitations":["Attribution accuracy depends on complete customer journey tracking — gaps in data (offline interactions, competitor research) create blind spots","Multi-touch attribution models are probabilistic and may not reflect actual customer decision-making","Requires integration with CRM and revenue system to track conversions; without this, attribution is limited to engagement metrics","Attribution latency — revenue data may take days to sync, delaying performance insights"],"requires":["Integration with CRM system to track customer interactions and conversion events","Integration with revenue system (Stripe, Salesforce, custom billing) to track actual revenue","Customer journey tracking enabled (cookies, UTM parameters, or event API)","Minimum 30-60 days of campaign data to establish reliable attribution patterns"],"input_types":["campaign interaction events (email sent, opened, clicked, SMS delivered)","customer conversion events (demo booked, trial started, deal closed)","revenue data (subscription amount, renewal amount, churn)","customer attributes (segment, plan tier, industry)"],"output_types":["campaign performance dashboard (open rate, click rate, conversion rate, revenue attributed)","attribution report showing revenue credit across touchpoints","channel performance comparison (email vs. SMS vs. web)","segment performance analysis (which segments convert best)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_agnetic__cap_6","uri":"capability://data.processing.analysis.customer.segment.builder.with.behavioral.rule.engine","name":"customer segment builder with behavioral rule engine","description":"Agnetic provides a visual segment builder that enables marketers to create customer segments using behavioral rules (e.g., 'customers who opened email in last 7 days AND clicked link AND have plan tier = Enterprise'). The rule engine supports AND/OR logic, date ranges, numeric comparisons, and text matching. Segments are evaluated dynamically against the customer database, updating in real-time as new engagement data arrives. Segments can be saved as reusable filters for campaign targeting or audience export.","intents":["I want to create a segment of high-value customers who are actively engaged so I can send them a premium offer","I need to target customers who haven't engaged in 30 days with a re-engagement campaign","I want to exclude customers who recently churned from my new customer onboarding campaign"],"best_for":["Marketing teams without SQL skills who need to create complex audience segments","Teams managing multiple campaigns with overlapping audience requirements","Organizations with rich customer data who want to leverage behavioral signals for targeting"],"limitations":["Rule evaluation latency — segments with complex rules may take 30-60 seconds to evaluate against large customer databases","Limited to AND/OR logic; cannot express complex conditional logic (e.g., 'A AND (B OR C) AND NOT D')","No support for cohort analysis or statistical significance testing; segments are deterministic based on rules","Segment size estimates may be inaccurate if customer data is not fully synchronized"],"requires":["Customer data with behavioral attributes (engagement dates, click counts, feature usage)","Customer profile data (plan tier, company size, industry, lifecycle stage)","Real-time or near-real-time data synchronization from CRM and analytics systems"],"input_types":["customer attributes (profile data, engagement history, product usage)","rule definitions (attribute, operator, value combinations)","date ranges and time-based filters"],"output_types":["segment definition (rule set with AND/OR logic)","segment membership list (customer IDs matching rules)","segment size estimate and composition breakdown","segment export (CSV, JSON for use in external systems)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_agnetic__cap_7","uri":"capability://text.generation.language.ai.assisted.copywriting.with.brand.voice.consistency","name":"ai-assisted copywriting with brand voice consistency","description":"Agnetic includes an AI writing assistant that generates email subject lines, body copy, and SMS messages based on campaign context (product, offer, target segment). The assistant learns brand voice and messaging guidelines from historical campaigns and applies them to generated copy, ensuring consistency across campaigns. Marketers can provide feedback on generated copy (thumbs up/down, edit suggestions) which fine-tunes the model for future generations. The system supports multiple tone options (formal, casual, urgent, educational) and can generate multiple variants for A/B testing.","intents":["I need to generate email subject lines that match our brand voice and are likely to get high open rates","I want to create multiple copy variants for A/B testing without manually writing each one","I need to ensure all my campaign messaging maintains consistent tone and brand guidelines"],"best_for":["Marketing teams without dedicated copywriters who need to generate campaign copy quickly","Agencies managing campaigns for multiple clients with different brand voices","Teams looking to scale campaign production without proportionally increasing headcount"],"limitations":["Generated copy quality depends on training data — limited historical campaigns result in generic, inconsistent output","AI-generated copy may not capture nuanced brand positioning or competitive differentiation","Requires human review and editing; generated copy should not be used without validation","No built-in fact-checking — AI may generate claims that are inaccurate or unsupported"],"requires":["Historical campaign data (at least 20-50 previous campaigns) to train brand voice model","Campaign context (product name, offer details, target segment, tone preference)","Human review process to validate and edit generated copy before sending"],"input_types":["campaign brief (product, offer, target segment, tone, key messages)","historical campaign examples for brand voice training","customer segment data (for personalization context)"],"output_types":["generated email subject lines (multiple variants)","generated email body copy (multiple variants)","generated SMS copy (character-limited variants)","confidence scores for generated copy quality"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_agnetic__cap_8","uri":"capability://data.processing.analysis.lead.scoring.with.engagement.and.firmographic.signals","name":"lead scoring with engagement and firmographic signals","description":"Agnetic calculates lead scores combining engagement signals (email opens, clicks, website visits, content downloads) with firmographic data (company size, industry, location, technology stack) and behavioral signals (product feature interest, support ticket topics). Scoring models can be configured with custom weights for different signals, enabling organizations to prioritize leads based on their specific sales criteria. Scores update in real-time as new engagement data arrives, and leads can be automatically routed to sales when they exceed a threshold.","intents":["I need to identify which leads are most likely to convert so my sales team can prioritize outreach","I want to automatically notify sales when a lead reaches a certain engagement level","I need to understand which engagement activities are most predictive of conversion for my business"],"best_for":["B2B SaaS companies with sales teams who need to prioritize high-potential leads","Organizations with long sales cycles where lead quality varies significantly","Teams with sufficient historical conversion data to train predictive models"],"limitations":["Lead scoring accuracy depends on historical conversion data — organizations with limited sales history may have unreliable models","Engagement signals may be biased toward high-touch accounts; low-engagement leads may be underscored despite high conversion potential","Firmographic data quality issues (incomplete company information, misclassified industries) reduce scoring accuracy","Lead scoring may create self-fulfilling prophecies if sales team only pursues high-scored leads"],"requires":["Historical lead data with conversion outcomes (converted vs. not converted)","Engagement tracking (email opens, clicks, website visits, content downloads)","Firmographic data (company size, industry, location, technology stack)","CRM integration to track lead status and conversion events"],"input_types":["lead engagement history (email opens, clicks, website visits, content downloads)","firmographic data (company size, industry, location, technology stack)","behavioral signals (product feature interest, support ticket topics)","historical conversion data (lead ID, conversion status, conversion value)"],"output_types":["lead score (0-100 scale with confidence interval)","score breakdown (contribution of engagement, firmographic, and behavioral signals)","lead grade (A, B, C, D based on score distribution)","sales routing recommendation (which sales rep or team should contact)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_agnetic__cap_9","uri":"capability://data.processing.analysis.campaign.a.b.testing.framework.with.statistical.significance.calculation","name":"campaign a/b testing framework with statistical significance calculation","description":"Agnetic enables marketers to set up A/B tests comparing different email subject lines, body copy, send times, or channels. The system randomly assigns customers to test variants, tracks engagement metrics (open rate, click rate, conversion rate) for each variant, and calculates statistical significance to determine if observed differences are real or due to chance. Tests can be configured with minimum sample size requirements and confidence level thresholds (e.g., 95% confidence). Results are displayed with confidence intervals and recommendations for winning variants.","intents":["I want to test different subject lines to see which gets higher open rates before sending to my full list","I need to understand if the difference in performance between two email variants is statistically significant or just random variation","I want to automatically declare a winning variant and apply it to future campaigns once statistical significance is reached"],"best_for":["Data-driven marketing teams who want to optimize campaigns based on empirical results","Organizations with large enough customer bases to achieve statistical significance in reasonable timeframes","Teams managing multiple campaigns who want to systematically improve performance over time"],"limitations":["Requires sufficient sample size to achieve statistical significance — small customer bases may need to run tests for weeks to reach conclusions","A/B testing assumes random assignment; if customer assignment is biased, results may be invalid","Multiple testing problem — running many A/B tests increases false positive rate; requires correction for multiple comparisons","Cannot test changes that require long observation periods (e.g., impact on customer lifetime value)"],"requires":["Minimum 1,000-5,000 customers per test variant to achieve statistical significance in reasonable timeframe","Engagement tracking for all test variants (opens, clicks, conversions)","Random assignment mechanism to ensure unbiased variant distribution"],"input_types":["test definition (variant A, variant B, metric to optimize, sample size, confidence level)","customer list for test assignment","engagement data for each variant (opens, clicks, conversions)"],"output_types":["test results dashboard (open rate, click rate, conversion rate for each variant)","statistical significance calculation (p-value, confidence interval)","winning variant recommendation with confidence level","test report with methodology and results"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Customer data platform or CRM with historical interaction logs (email, support, product usage)","API connectivity to customer data sources for real-time behavior ingestion","Minimum 50-100 customer interactions per segment to establish reliable behavior patterns","Integration with email service provider (SMTP, SendGrid, Mailgun) and SMS gateway (Twilio, Nexmo)","Customer timezone and channel preference data in CRM or customer data platform","Minimum 2-4 weeks of historical engagement data per segment for timing optimization","Clear definition of lifecycle stages and behavioral signals that indicate each stage","Engagement tracking (login frequency, feature usage, support interactions, purchase history)","Predefined campaign sequences and workflows for each stage","CRM integration to track stage transitions and trigger workflows"],"failure_modes":["Personalization quality depends on data completeness — sparse customer interaction history limits adaptation effectiveness","No A/B testing framework built-in; requires external analytics to measure personalization impact","Real-time personalization adds processing latency (~500ms-2s per message generation depending on data volume)","Requires historical engagement data to train timing models — new customer segments may use default timing until sufficient data accumulates","Channel preference learning is reactive; cannot predict channel preference for customers with no prior interaction history","Conditional logic limited to simple if/then rules; no support for complex branching workflows with multiple decision points","Lifecycle stage classification depends on behavioral signal quality — sparse data results in misclassification","Stage transition detection has latency — customers may be in wrong stage for 24-48 hours after behavior change","Workflows are linear sequences; cannot express complex conditional logic based on multiple customer attributes","Stage definitions are organization-specific; requires careful configuration to match actual customer journey","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:28.696Z","last_scraped_at":"2026-04-05T13:23:42.562Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=agnetic","compare_url":"https://unfragile.ai/compare?artifact=agnetic"}},"signature":"S9HpBYUuXhj+KTIgaJisw4yOJA8Mcd/aHrpkV+KCZmx9aM/rPfa7O9XWJIgOCm9ua6godf+pKPiQzYJ+wGY4Aw==","signedAt":"2026-06-21T20:53:01.800Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agnetic","artifact":"https://unfragile.ai/agnetic","verify":"https://unfragile.ai/api/v1/verify?slug=agnetic","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}