AI-Powered IEP Generation System
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): Multi-Agent IEP Orchestration - Student profile selection with STU program participants, assessment document upload, and FERPA-certified system status
Illustrative preview (sample data, not verified outcomes): Agent Orchestration Pipeline - Workflow execution with active agents (Document Analyzer, Goal Generator) showing real-time tokens, latency, and model inference metrics
Illustrative preview (sample data, not verified outcomes): AI-Generated IEP Results - Executive summary showing SMART goals, interventions, student demographics, extracted assessments, and IDEA compliance validation
Illustrative preview (sample data, not verified outcomes): AI Insights Dashboard - Quality score analysis with strengths and challenges breakdown, emerging behavioral patterns, and progress monitoring recommendations
AI Agents
Specialized autonomous agents working in coordination
IEP Workflow Orchestrator
IEP generation involves complex dependencies between document analysis, goal creation, intervention matching, and compliance validation. Sequential processing without optimization creates bottlenecks and extended generation times.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Implements DAG-optimized workflow planning coordinating 7 downstream agents through 8 pipeline stages. Powered by Claude 3.5 Sonnet for reasoning. Manages agent-to-agent handoffs with structured message passing, broadcast notifications, and correlation tracking. Aggregates metrics: totalTokensUsed, totalLatencyMs, toolCallCount, llmCallCount, successRate across all agents.
Document Analysis Agent
IEP creation requires extracting structured information from 50-100+ page assessment documents containing psychological evaluations, academic assessments, behavioral observations, and medical records in varied formats.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Performs OCR text extraction, named entity recognition, and NLP classification to build structured student profiles. Tools: extract_text, extract_entities, classify_section. Chain-of-thought example: 'Extracted 156 entities from 78-page assessment. Key findings: ASD Level 2, GAD comorbidity, visual-spatial strength (95th percentile).' Outputs profiles with 8+ assessment scores, strengths, challenges.
SMART Goal Generation Agent
Writing measurable, achievable IEP goals requires expertise in SMART criteria, evidence-based practices, and alignment with individual student profiles. Inconsistent goal quality affects student outcomes and compliance.
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. Synthesizes 12 SMART goals across personal (4), social (4), and vocational (4) development domains. Tools: search_evidence, generate_goal, validate_smart. Integrates UDL profile (engagement, representation, action/expression), MTSS tier recommendations, and SEL competency targets. Provides baselines, targets, measurement methods, and confidence scores (avg 70-72%).
Evidence-Based Intervention Matching Agent
Matching students to effective interventions requires analyzing extensive research databases, considering individual profiles, and optimizing across multiple criteria including effectiveness, feasibility, and resource requirements.
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. Executes multi-criteria optimization using intervention database queries and match score calculations. Tools: query_interventions, calculate_match, optimize_selection. Outputs ranked interventions with effectiveness scores (e.g., visual learning: 87%, extended time: 92%), implementation notes, evidence ratings, and contraindication flags. Performance requires validation on representative workloads.
IDEA Compliance Validation Agent
IEPs must meet IDEA 2004 federal requirements plus state-specific regulations. Manual compliance review misses violations, creating legal exposure and delaying services.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Checks IEP documents against configured requirements for components, timelines, safeguards and transitions. Reports missing items and suggested remediation for educator and compliance review; automated checks do not establish legal compliance.
Predictive Progress Monitoring Agent
Tracking IEP goal progress requires continuous data analysis to identify students falling behind before annual reviews. Traditional quarterly reviews miss early warning signs.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Implements trend line analysis with slope calculation, direction classification, and R² goodness-of-fit metrics. Tools: analyze_trends, predict_outcomes, generate_alerts, recommend_adjustments. Predicts goal achievement with confidence intervals. Alert system: on_track/monitor/warning/critical. Generates data-driven adjustment recommendations.
Transition Planning Agent
IDEA mandates transition planning for students 16+ covering post-secondary education, employment, and independent living. Creating comprehensive transition plans requires career assessment, agency coordination, and pathway analysis.
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. Generates post-secondary goals across education, employment, and independent living domains. Tools: match_careers, plan_pathway, assess_self_determination, coordinate_agencies. Provides career recommendations with match scores (e.g., Graphic Designer: 94%), self-determination assessment (7 skills scored 0-100), and agency coordination plans (VR, developmental services, community college).
Family Engagement Agent
Parent engagement significantly improves student outcomes, but generating personalized home strategies, preparing meeting materials, and maintaining communication requires substantial staff time.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Automates family engagement deliverables including home strategy guides (Zones check-in routines, visual schedules, social story practice), meeting preparation with agendas and documentation, and weekly progress summaries. Tools: generate_home_strategies, prepare_meeting, match_resources, generate_summary. Curates 12+ family resources matched to student needs.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization