Comprehensive Agentic Intelligence Platform
Deploys 5 production-grade AI agents with advanced agentic capabilities: self-healing error recovery, adaptive learning optimization, autonomous decision-making, and multi-agent collaboration. Processes 12+ students and 96+ IEP goals to generate comprehensive municipality reports with strategic recommendations across multiple output formats.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Mission Control - Municipality report configuration with data source selection, output formats (PDF, Excel, JSON), target audience settings, and agent workflow overview
Municipality Report Generation - Real-time execution phases with agent status tracking, live metrics, and multi-agent data processing pipeline
Quality Assessment Dashboard - Report quality dimensions (accuracy, completeness, readability, compliance) with agent performance metrics and self-healing events
AI Insights & Agentic Intelligence - Goal achievement trends, transition-related incident patterns, family engagement impact, and AI-suggested intervention actions
AI Agents
Specialized autonomous agents working in coordination
Autonomous Workflow Orchestrator
Complex multi-phase workflows require intelligent coordination that can adapt to runtime conditions, prioritize urgent cases, and make autonomous decisions when human approval would introduce unacceptable delays.
Core Logic
Powered by GPT-4-Turbo for workflow planning, task delegation, and conflict resolution. Implements autonomous decision-making with confidence-based auto-approval (94% threshold). Example: Detects high-risk student (score 78/100), evaluates options (standard workflow: -45sec delay vs. prioritize: +2% resources), selects prioritization with 94% confidence—auto-approved per policy. Manages 6-phase pipeline with parallel execution.
Data Engineering Agent
Aggregating data from IEP systems, behavioral databases, and assessment platforms involves connection management, query optimization, and data quality assurance across heterogeneous sources.
Core Logic
Executes data extraction, validation, and transformation workflows with self-healing capabilities. Tools: connect_database (1000ms latency, 98% success), query_iep_data, query_behavioral_data, validate_data_quality. Implements self-healing: recovers from API rate limits via exponential backoff with jitter, reduces batch size 50→25 records, recovers in 1500ms with context preserved. Achieves 96.8/100 data quality scores.
Predictive Analytics Agent
Generating actionable insights from student data requires statistical analysis, risk scoring, trend detection, and predictive modeling—capabilities beyond traditional reporting tools.
Core Logic
Powered by Claude 3 Opus for statistical analysis and predictive modeling. Loads ML models (student_risk_logistic v2.4.1, 94.3% accuracy), calculates risk scores, predicts goal achievement (84 on-track, 9 at-risk, 3 behind from 96 goals), and detects behavioral patterns (e.g., 78% incidents during transitions, p<0.01). Implements adaptive learning: discovers parallel risk scoring improves throughput 40% for cohorts >10 students.
Narrative Content Generation Agent
Transforming analytics into compelling narratives for diverse stakeholders (executives, parents, municipalities) requires adapting tone, highlighting relevant metrics, and maintaining engagement while ensuring accuracy.
Core Logic
Powered by Claude 3 Opus for narrative generation and tone adaptation. Tools: generate_executive_summary (3000ms, 99% success), generate_success_story, generate_recommendations. Outputs: Executive summaries (342 words, readability 68, quality 94), success stories with emotional resonance, 5 actionable evidence-based recommendations. Adaptive learning: discovers 3-4 key metrics in summaries yield 23% higher engagement.
Multi-Framework Compliance Agent
Municipality reports must meet multiple regulatory frameworks simultaneously—IDEA, GDPR, FERPA, WCAG 2.1—while maintaining person-first language and quality standards.
Core Logic
Validates outputs against comprehensive compliance matrix: IDEA 2004 (8/8 checks), GDPR (5/5 checks), FERPA, WCAG 2.1, ISO 27001, SOC 2 Type II. Verifies person-first language (47/47 instances compliant). Generates quality assessment: 96.2/100 overall (accuracy 99.8%, completeness 100%, readability 93%, compliance 100%). Maintains complete audit trail with 23 checks, data lineage verification, and 92nd percentile national benchmark.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization