Multi-Agent Communication Orchestrator
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Illustrative preview (sample data, not verified outcomes): Configure AI Communication Workflow - Project selection, communication types, target resident groups, and upcoming disruptions panel
Illustrative preview (sample data, not verified outcomes): Multi-Agent Orchestration - AI agents processing with RAG knowledge retrieval showing template and regulation matches
Illustrative preview (sample data, not verified outcomes): Human-in-the-Loop Review - Multilingual content preview with quality metrics, compliance checks, and AI generation details
Illustrative preview (sample data, not verified outcomes): Communications Generated Successfully - Multi-language translations with delivery summary for email, SMS, app push, and portal
AI Agents
Specialized autonomous agents working in coordination
Master Orchestrator
Complex multi-agent workflows require centralized coordination to ensure proper sequencing, quality control, and error handling across diverse AI agents with different capabilities.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Coordinates all agents using chain-of-thought reasoning, manages workflow execution sequencing, delegates tasks based on agent capabilities, monitors progress across phases, handles error recovery, and ensures quality output through systematic validation. Determines parallel vs. sequential execution paths and manages inter-agent data dependencies.
Predictive Intelligence Agent
Without predictive insights, communication campaigns rely on guesswork for timing, channel selection, and content tone—resulting in lower engagement rates and resident dissatisfaction.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Analyzes 1,247+ historical communications to forecast resident sentiment (84% accuracy), predict response rates (72% expected), assess engagement patterns by demographic, and identify optimal timing windows. Uses ML models to generate risk scores and provides data-driven recommendations for communication strategy optimization.
Smart Scheduler Agent
Determining the optimal time and channel for communications requires analyzing multiple factors—resident availability, construction schedules, weather conditions, and historical engagement patterns—which is impractical to do manually.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Queries external APIs (weather, construction schedule, calendar) to determine optimal delivery windows. Analyzes historical engagement data to recommend Tuesday 9:30 AM as optimal timing. Develops multi-channel strategies prioritizing SMS (94% open rate) for elderly residents and email (68% open rate) for general communications.
Risk Sentinel Agent
AI workflows can fail due to API timeouts, translation errors, or compliance violations. Without autonomous monitoring, these issues require manual intervention and delay communications.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Continuously monitors API latency (<5s threshold), translation confidence (>90%), compliance rule adherence (4/4 required), and cost budget ($0.15 limit). Implements self-healing protocols with automatic retry, fallback endpoint switching, and escalation handling. Provides autonomous approval confidence scoring (94%) for low-risk communications.
Knowledge Retriever Agent (RAG)
Generating accurate, compliant communications requires access to approved templates, Danish housing regulations, historical best practices, and current project documentation—information scattered across multiple knowledge bases.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Performs semantic search across 4 knowledge bases (1,406 total documents): Communication Templates (47 docs), Danish Housing Regulations (23 docs), Historical Communications (1,247 docs), and Project Documentation (89 docs). Retrieves top 5 chunks with relevance scores >0.85, returning results in 234ms with source citations for transparency.
Content Generator Agent
Creating personalized, empathetic communications for 325+ residents across 4 buildings with different concerns, languages, and communication preferences is prohibitively time-consuming when done manually.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Uses Claude 3 Sonnet to generate context-aware content by merging approved templates with project-specific details. Incorporates disruption information (water outage March 15), mitigation measures, and contact information. Self-validates for tone (empathetic score 0.82), readability (Grade 8), and completeness (342 words). Outputs structured content ready for translation.
Translation Specialist Agent
Diverse resident populations require communications in multiple languages (Danish, English, Arabic, Turkish, Polish, Somali). Simple translation fails to preserve cultural context, formal registers, and idiomatic expressions.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Translates content to 3-8 languages with cultural adaptation enabled. Handles idioms, adjusts greeting formats for Arabic cultural norms, maintains formal register for Turkish communications, and applies right-to-left formatting where needed. Performance requires validation on representative workloads.
Quality Reviewer Agent
Communications must meet Danish housing law requirements (48-hour notice for utility disruptions, 14-day notice for major renovations), maintain appropriate tone, and provide accessible language—verification that requires specialized knowledge.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Runs sentiment analysis (positive/empathetic score 0.84), readability assessment (Grade 8 - accessible), and compliance verification against Danish tenant notification requirements. Checks 4 mandatory rules: 48-hour notice, alternative arrangements specified, contact information provided, and accessible language level. Provides approval recommendation with improvement suggestions.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
7 technologies
Architecture Diagram
System flow visualization