AI Agent Guidelines for CS336 at Stanford
Meta Description: Discover the official AI Agent Guidelines for CS336 at Stanford — what they cover, why they matter, and how students can navigate them effectively in 2026.
TL;DR
Stanford's CS336 (Language Models from Scratch) has specific guidelines governing the use of AI agents in coursework. These rules define what's permissible, what's prohibited, and how students should document AI assistance. Whether you're enrolled, curious, or building a similar policy framework, this article breaks down everything you need to know — with practical advice on staying compliant while still learning effectively.
Introduction: Why AI Agent Guidelines Matter in Graduate CS Courses
Artificial intelligence is no longer just the subject of computer science courses — it's actively reshaping how those courses are taught and completed. Stanford's CS336, one of the most rigorous language model courses in the world, sits at a fascinating crossroads: it teaches students to build large language models from scratch, while simultaneously having to govern how AI tools can be used during that learning process.
The AI Agent Guidelines for CS336 at Stanford represent one of the first serious, detailed attempts by a top-tier institution to define the boundaries of AI-assisted work in an advanced ML course. For students, researchers, and educators alike, understanding these guidelines offers a window into the broader conversation about academic integrity in the age of generative AI.
What Is CS336 at Stanford?
CS336, officially titled Language Models from Scratch, is a graduate-level course offered through Stanford's Computer Science department. It's designed for students who want to go beyond using pre-trained models and actually understand — and implement — the full stack of modern language model development.
Core Topics Covered in CS336
- Transformer architecture and attention mechanisms
- Tokenization and vocabulary design
- Pre-training data pipelines and curation
- Distributed training and systems optimization
- Alignment and safety fundamentals
- Evaluation and benchmarking
The course is hands-on by design. Students write real training code, run actual experiments on GPU clusters, and submit assignments that require deep technical understanding. This context is critical to understanding why the AI agent guidelines are structured the way they are.
[INTERNAL_LINK: Stanford CS graduate program overview]
The AI Agent Guidelines for CS336 at Stanford: A Detailed Breakdown
The AI Agent Guidelines for CS336 at Stanford are part of the course's broader academic honesty policy, but they go significantly further than a standard "don't use ChatGPT" blanket ban. Instead, they attempt to define nuanced, context-specific rules that reflect the reality of working in AI research environments.
What the Guidelines Actually Cover
The CS336 AI agent guidelines address several distinct categories:
1. Permitted Uses of AI Assistance
Students in CS336 are generally allowed to use AI tools for:
- Conceptual clarification — asking an AI to explain a concept already covered in lecture or readings
- Debugging assistance — using AI to help identify syntax errors or understand error messages
- Literature search — using AI to surface relevant papers or summarize abstracts
- Writing polish — light grammar and clarity improvements on written reports (not substantive content generation)
This is a notably more permissive stance than many peer institutions, and it reflects Stanford's acknowledgment that professional ML engineers routinely use AI coding assistants in their work.
2. Prohibited Uses of AI Assistance
The guidelines draw clear lines around what constitutes a violation:
- Generating core assignment solutions — submitting AI-generated code or derivations as your own work
- Using AI agents to run experiments autonomously — particularly relevant given the rise of agentic coding tools like Devin, Cursor's agent mode, and similar products
- Automating the training pipeline with AI orchestration without disclosure
- Using AI to write substantive sections of research reports or analysis
- Sharing course materials with AI systems in ways that could expose proprietary datasets or unpublished research
The prohibition on autonomous AI agents is particularly notable and forward-looking. As agentic AI systems become more capable of end-to-end task completion, courses like CS336 are grappling with a genuine philosophical question: if an AI agent completes your assignment, what did you learn?
3. Disclosure Requirements
This is where the CS336 guidelines become genuinely innovative. Students are required to:
- Log all significant AI interactions related to assignments in a dedicated section of their submission
- Describe the nature of AI assistance (e.g., "Used GitHub Copilot for boilerplate data loading code; all model architecture code written independently")
- Reflect on what they learned from AI-assisted portions versus independently completed work
This disclosure framework is borrowed partly from emerging norms in academic publishing, where journals like Nature now require authors to declare AI assistance. Applying it to coursework is a meaningful step forward.
[INTERNAL_LINK: Academic integrity policies in AI research]
Why These Guidelines Are Designed This Way
Understanding the reasoning behind the AI Agent Guidelines for CS336 at Stanford helps students comply more thoughtfully — and helps educators at other institutions learn from Stanford's approach.
The Core Tension: Learning vs. Efficiency
CS336 is fundamentally about building deep intuition for how language models work. The instructors' concern isn't primarily about cheating in the traditional sense — it's about learning loss. If a student uses an AI agent to write their training loop, they may submit a working assignment but fail to develop the mental models that make a great ML researcher.
The guidelines are calibrated to permit AI use that augments understanding while prohibiting use that bypasses the struggle that produces genuine learning.
Agentic AI as a Specific Risk Category
The explicit mention of AI agents — not just AI assistants — in the CS336 guidelines reflects a 2025-2026 reality: tools like Cursor, GitHub Copilot, and emerging research agents can now autonomously plan, execute, and iterate on complex coding tasks with minimal human input.
This is genuinely different from using ChatGPT to explain backpropagation. An AI agent could, in theory, receive a CS336 problem set, design experiments, write training code, run evaluations, and produce a report — with the student doing little more than reviewing the output. The guidelines specifically target this scenario.
Fairness Across the Cohort
Graduate students in CS336 come from varied backgrounds. Some have access to premium AI coding tools through institutional licenses; others may not. Clear guidelines help level the playing field by defining what tools and use cases are in-bounds for everyone.
Practical Advice for CS336 Students
If you're currently enrolled in CS336 or a similar course with AI agent guidelines, here's how to navigate them effectively:
Do This
- Read the guidelines before starting each assignment — not just at the beginning of the semester. AI policy nuances can vary by assignment type.
- Keep a running log of every AI tool interaction as you work, not after the fact. Reconstructing this retroactively is both difficult and less accurate.
- Use AI for the "why," not the "what" — asking an AI to explain why a particular optimizer works in a specific regime is valuable learning. Asking it to write your optimizer is not.
- Treat AI suggestions as starting points — if Copilot autocompletes a function, take the time to understand it before accepting it.
- Ask your TA or instructor when in doubt — the CS336 teaching team has consistently emphasized that asking for clarification is never penalized.
Avoid This
- Running agentic coding sessions (multi-step, autonomous AI execution) on assignment code
- Submitting AI-generated analysis or discussion sections without disclosure
- Using AI to interpret or summarize proprietary course data
- Sharing assignment specifications with external AI systems in ways that violate course data policies
Recommended Tools (With Honest Assessments)
| Tool | Best For | CS336 Compliance Notes |
|---|---|---|
| GitHub Copilot | Code completion, boilerplate | Permitted for non-core code; disclose use |
| Cursor | IDE-integrated AI assistance | Agent mode likely prohibited; standard completion OK with disclosure |
| Perplexity AI | Research and literature search | Generally permitted; verify citations independently |
| Grammarly | Grammar and clarity editing | Permitted for light editing; not for substantive writing |
| ChatGPT / Claude | Concept explanation, debugging | Permitted for clarification; disclose; don't submit outputs |
Note: Always verify current course policy directly with CS336 instructors. These assessments are based on publicly available policy information as of June 2026 and may not reflect mid-semester updates.
[INTERNAL_LINK: Best AI coding tools for graduate students]
How CS336's Approach Compares to Other Top Programs
The AI Agent Guidelines for CS336 at Stanford are among the most detailed in academic ML education. Here's a brief comparison:
| Institution | Course Type | AI Policy Approach |
|---|---|---|
| Stanford CS336 | LM from Scratch | Nuanced: permitted with disclosure; agents prohibited |
| MIT 6.S965 | Deep Learning Systems | Restrictive: minimal AI tool use permitted |
| CMU 11-868 | LLM Alignment | Moderate: case-by-case instructor approval |
| Berkeley CS294 | Foundation Models | Permissive: AI use encouraged with full transparency |
Stanford's approach sits in a thoughtful middle ground — neither reflexively banning AI tools (which would be increasingly impractical) nor permitting unrestricted use (which would undermine learning outcomes).
Implications for Educators Building Similar Policies
The CS336 framework offers a useful template for any educator grappling with AI policy design. Key principles worth adopting:
- Distinguish between AI assistants and AI agents — these represent meaningfully different levels of automation and learning displacement
- Require disclosure rather than prohibition — this builds professional norms while maintaining accountability
- Tie policy to learning objectives — make explicit why certain uses are prohibited, not just that they are
- Update policies each semester — AI capabilities are evolving faster than academic policy cycles
[INTERNAL_LINK: AI policy frameworks for university courses]
Key Takeaways
- The AI Agent Guidelines for CS336 at Stanford are among the most sophisticated AI use policies in graduate CS education
- The guidelines permit AI assistance for clarification, debugging, and research — but require full disclosure
- Autonomous AI agents are specifically prohibited due to the risk of bypassing core learning outcomes
- Students must log and disclose all significant AI interactions as part of their submissions
- The policy reflects a broader philosophy: AI should augment learning, not replace the productive struggle that builds expertise
- Educators at other institutions can use CS336's framework as a practical template for their own AI policies
Frequently Asked Questions
Q1: Can CS336 students use GitHub Copilot for assignments?
Yes, with important caveats. GitHub Copilot is generally permitted for boilerplate and utility code, but students must disclose its use and should not use it for core model architecture or training logic. Using Copilot's agent features for autonomous multi-step coding tasks is likely prohibited. When in doubt, ask your TA.
Q2: What counts as an "AI agent" under the CS336 guidelines?
An AI agent, in this context, refers to any AI system that autonomously plans and executes multiple steps toward a goal with minimal human intervention. This includes tools like Cursor's agent mode, Devin, and similar agentic coding assistants — as opposed to single-turn AI assistants that respond to individual queries.
Q3: What happens if a student violates the AI agent guidelines?
Violations are handled through Stanford's standard academic integrity process, which can result in consequences ranging from a zero on the assignment to course failure or referral to the University's Committee on Student Conduct. CS336 instructors have emphasized that disclosure and honest mistakes are treated very differently from intentional violations.
Q4: Are the AI guidelines the same for all CS336 assignments?
Not necessarily. The guidelines may specify different rules for different assignment types — for example, more restrictive policies on core implementation assignments versus more flexibility on literature review components. Students should read the guidelines section of each individual assignment carefully.
Q5: Do the CS336 AI guidelines apply to group projects?
Yes, and with additional complexity. For group work, all group members are responsible for understanding and complying with the guidelines. AI use must be disclosed collectively, and the group is jointly accountable for any violations — even if only one member used a prohibited tool.
Final Thoughts and CTA
The AI Agent Guidelines for CS336 at Stanford represent a genuine attempt to solve one of education's hardest current problems: how do you teach people to build the most powerful technology in the world, when that same technology can increasingly do the homework for them?
The answer Stanford has landed on — nuanced, disclosure-based, learning-outcome-focused — is worth studying whether you're a student, an educator, or a policy maker.
If you're a CS336 student: bookmark the official course policy page, start your AI use log on day one, and remember that the struggle is the point. The intuitions you build by wrestling with transformer training will serve you for a decade. An AI agent's shortcut will serve you until the next assignment.
If you're an educator: consider adapting CS336's framework for your own courses. The principles are sound, the disclosure model is practical, and your students will respect a policy that treats them as future professionals rather than potential cheaters.
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Last updated: June 2026. Policy details are based on publicly available CS336 course information. Always verify current guidelines directly with Stanford course staff, as policies may be updated each academic term.
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