Multi-Agent Coding Assignment Autograder Integration

Automating code execution in secure sandboxes and running dynamic test suites removes the single largest time sink for CS instructors. A custom workflow executes thousands of submissions in parallel, returning pass/fail results and runtime metrics instantly, freeing faculty from setting up environments, running tests, and recording scores manually.

The architecture decouples grading capacity from instructor headcount. Whether handling 50 or 5,000 submissions, the orchestrated agent system scales horizontally. This enables large introductory courses, MOOCs, and bootcamps to maintain consistent, high-quality assessment without proportional increases in teaching assistant labor or faculty overload.

Immediate, detailed feedback on test failures, code style, and efficiency closes the learning loop while the assignment is still top-of-mind. This shifts assessment from a summative audit to a formative tool, increasing student engagement, reducing repeated errors, and improving overall mastery—directly impacting retention and course success metrics.

Integrating plagiarism and AI-generation detection agents into the grading pipeline performs continuous, proactive integrity screening. By flagging potential issues with evidence at submission time, it reduces the volume of post-hoc investigations and equips instructors with auditable reports, protecting institutional standards while containing manual review efforts.

Multi-agent workflows encode rubric logic precisely, applying the same style, efficiency, and correctness checks to every submission. This eliminates grader drift, bias from fatigue, and inconsistency across teaching assistants, ensuring defensible, equitable scoring. The system logs all decision rationales for quality assurance and grade dispute resolution.

Every graded submission generates structured data on common errors, challenging concepts, and code quality trends. A custom workflow pipes this into analytics dashboards, revealing where students struggle at scale. This enables proactive curriculum adjustments, targeted TA support, and evidence-based pedagogical improvements, turning grading from a cost center into a strategic intelligence asset.

This initial agent receives student code via LMS API (Canvas, Moodle, Blackboard), validates file structure, and sanitizes inputs to prevent malicious execution. It extracts metadata (student ID, assignment ID) and prepares a secure, containerized execution package for the test runner, ensuring a clean, auditable pipeline start.

The core execution agent spins up ephemeral Docker containers or secure cloud sandboxes (e.g., AWS SageMaker, GitLab CI) to run the student's code against dynamic test suites (unit, integration, stress tests). It captures stdout/stderr, runtime, memory usage, and test results, enforcing strict resource limits and timeout policies to protect infrastructure.

A specialized static analysis agent runs linters (Pylint, ESLint), checks for adherence to style guides (PEP 8), evaluates code complexity (cyclomatic), and identifies poor practices (e.g., hard-coded secrets). It generates a structured quality score separate from functional correctness, providing formative feedback on software engineering principles.

This integrity agent performs a multi-layered check: first, a syntactic similarity scan against code repositories (e.g., GitHub, previous submissions); second, a semantic analysis for logic duplication; and third, a statistical analysis for stylistic fingerprints indicative of AI-generated code (e.g., low variance, generic patterns). It builds an evidence report for human review.

The final agent synthesizes outputs from all previous agents into a coherent, personalized report. Using LLM orchestration (via LangChain or custom prompts), it translates test failures into actionable explanations, aggregates style suggestions, and formats a final score (rubric-based or points). The report is posted back to the LMS gradebook via API.

A critical control layer monitors for edge cases: high plagiarism scores, runtime errors requiring interpretation, or submissions flagged for manual review by instructor-set policies. These are routed to a dedicated queue in a tool like Jira or a custom dashboard, where faculty can efficiently review, override, or annotate decisions, maintaining essential human oversight.

Define the pedagogical requirements, grading rubrics, and acceptable use policies. They are the ultimate owners of assessment quality and academic integrity, responsible for approving the logic of style checks, plagiarism thresholds, and feedback tone. Their sign-off is required before any automated grade is finalized.

Architect and maintain the core execution infrastructure. This includes building secure, isolated sandboxes (e.g., Docker, Kubernetes) for code execution, integrating with the Learning Management System (LMS) via LTI or APIs, and ensuring high availability during peak submission periods. They own the observability, logging, and deployment pipeline.

Design and implement the multi-agent logic using frameworks like LangGraph or CrewAI. This team builds the agents for unit test execution, static code analysis, style checking (e.g., via linters), and plagiarism detection. They are responsible for the feedback synthesis logic, exception routing, and the overall data flow between components.

Act as the human-in-the-loop for edge cases and appeals. The workflow must route submissions that fail sanity checks, have borderline plagiarism scores, or trigger exception flags to a managed queue for manual review. TAs need a clear interface to override, annotate, or supplement automated feedback, maintaining a defensible audit trail.

Govern data privacy, security, and regulatory adherence. They enforce policies for handling student code (intellectual property), securing the sandbox environment against escape exploits, and ensuring the system complies with FERPA and institutional data governance rules. Their approval is critical for production deployment.

Manage the integration point with the student and faculty experience. They configure the LMS plugin, manage user roles and permissions, and handle the rollout and training for faculty. This team ensures the autograder appears as a seamless tool within the existing academic workflow, not a disruptive external system.