Enterprise Agentic AI-Powered Automated Grading & Feedback System

Core Logic

The Orchestrator Agent serves as the central coordinator implementing a DAG-based execution model. It initializes agent teams, manages task delegation based on submission requirements, aggregates results from specialist agents, handles error recovery with exponential backoff, maintains session state throughout the grading pipeline, and ensures all agents complete their assigned tasks before proceeding to the next phase. Uses Claude 3 Opus for complex reasoning and decision-making.

Core Logic

The Content Analyzer Agent performs comprehensive analysis of each submission including paragraph structure mapping, sentence-level parsing, theme extraction using semantic analysis, citation counting and verification, unique vocabulary assessment, readability scoring (Flesch-Kincaid, SMOG), and structural analysis detecting presence of introduction, thesis, and conclusion. Outputs metadata that informs subsequent scoring agents. Implements caching for repeated content patterns.

Core Logic

The Scorer Agent evaluates each submission against every rubric criterion, providing scores with Chain of Thought reasoning chains that document the decision process. For each criterion, it identifies specific evidence in the submission text (with character offsets for highlighting), maps evidence to performance level descriptors, generates confidence scores with uncertainty ranges, and produces reasoning summaries. Implements inter-rater reliability checks against exemplar calibration data and flags borderline scores for human review.

Core Logic

The Feedback Generator Agent synthesizes scoring results into comprehensive, student-facing feedback. It generates summaries that acknowledge strengths before addressing improvements, creates prioritized lists of actionable improvement suggestions, links suggestions to specific submission passages with inline annotations, recommends relevant learning resources (videos, articles, tutorials) matched to identified gaps, and adapts feedback tone based on configuration (professional, encouraging, direct, Socratic). Supports multiple languages and accessibility requirements.

Core Logic

The QA Agent validates all grading results through multiple checks: statistical outlier detection comparing scores to historical distributions, confidence threshold verification, calibration drift analysis comparing current session to baseline, inter-agent agreement checking when multiple agents assess the same criterion, and flag aggregation for human review prioritization. Produces quality metrics dashboards and triggers re-grading when anomalies exceed thresholds. Maintains audit trails for compliance.

Core Logic

The Pattern Recognition Agent analyzes all submissions collectively to detect emergent patterns including common misconceptions (same wrong answers), skill gaps affecting multiple students, grade clustering indicating possible assessment issues, citation pattern anomalies, structural issues in writing organization, and exceptional performance warranting recognition. Produces visualizations (histograms, heatmaps, scatter plots) and generates instructor-facing insights with suggested pedagogical interventions.

Core Logic

The Plagiarism Detector Agent implements multi-layered originality checking including semantic similarity comparison against submission corpus, citation verification against claimed sources, AI-generated content detection using statistical patterns and perplexity analysis, and unusual pattern identification (e.g., writing style inconsistencies within a submission). Integrates with external plagiarism services when available and produces severity-rated flags with evidence for human review. Respects student privacy while protecting academic integrity.

Core Logic

The Calibrator Agent maintains grading consistency through continuous calibration against exemplar submissions with known scores. It computes accuracy metrics (mean absolute error, correlation, Cohen's Kappa), detects calibration drift direction (stricter or more lenient), generates reliability metrics (inter-rater and intra-rater reliability), and produces calibration recommendations (retrain model, adjust thresholds, add exemplars, require human review). Implements Bayesian calibration to adjust confidence intervals based on historical accuracy.