LLM Agents MOOC is the UC Berkeley graduate course taught by Professor Dawn Song and Google DeepMind Research Scientist Xinyun Chen in Fall 2024. Twelve guest lectures from leading researchers including Denny Zhou of Google DeepMind, Shunyu Yao of OpenAI, Jerry Liu of LlamaIndex, Jim Fan of NVIDIA, Percy Liang of Stanford, and Ben Mann of Anthropic. The full syllabus, video lectures, slides, and quizzes are publicly accessible without registration.
LLM Agents MOOC: What You’ll Learn
This is the academic foundations course in the Iqraa AI Agents series. It is broader and more theoretical than the industry courses earlier in the track. Where Hugging Face and Microsoft teach you to ship agents, Berkeley teaches you why agents work, what their known failure modes are, and what the research frontier looks like. Twelve lectures, twelve quizzes, three labs, one optional hackathon. Audit freely. Or earn one of five certificate tiers from Trailblazer to Legendary.
The course assumes Berkeley upper division computer science background. Solid machine learning and deep learning foundations, comfort reading arXiv papers, Python for the labs. If you do not have that background, do the Kaggle Gen AI Intensive and the Hugging Face course on this site first.
LLM Agents MOOC History and Origins
The LLM Agents MOOC was taught in Fall 2024 at UC Berkeley as a graduate seminar in the Department of Electrical Engineering and Computer Sciences. The principal instructor is Professor Dawn Song, who leads the Berkeley RDI Lab on Responsible, Decentralized, and Intelligent Systems. The course was co organized with researchers from Google DeepMind, OpenAI, Meta, NVIDIA, Stanford, CMU, ServiceNow, Anthropic, and Databricks, which is why the guest lecture roster reads like a who’s who of agent research.
The LLM Agents MOOC was the first Berkeley graduate course dedicated to LLM agents specifically. Earlier Berkeley ML courses covered deep learning, reinforcement learning, and systems, but the LLM agents topic had been scattered across reading groups and lab seminars until the Fall 2024 course unified it into a single curriculum. The course website at llmagents-learning.org/f24 is the canonical source for the syllabus, readings, and assignment instructions.
The Fall 2024 course was followed by a Spring 2025 sequel at llmagents-learning.org/sp25, which is a more advanced course focused on formal math, verifiable code generation, and reasoning. The Spring 2025 course is the post MOOC destination for learners who finish Fall 2024 and want more depth. The Fall 2024 LLM Agents MOOC is the broad survey, Spring 2025 is the deep dive.
Berkeley’s choice to publish the full lecture recordings, slides, and quiz materials on the open web without registration is what makes the LLM Agents MOOC the most accessible graduate level agent course. Most Berkeley graduate courses are behind the bCourses LMS wall. The LLM Agents MOOC is the exception, by design.
LLM Agents MOOC: The Twelve Lecture Syllabus
Verbatim from the official syllabus at llmagents-learning.org/f24.
- September 9, LLM Reasoning. Denny Zhou, Google DeepMind. Chain of thought reasoning without prompting, why LLMs cannot self correct reasoning yet, premise order in reasoning.
- September 16, LLM agents brief history and overview. Shunyu Yao, OpenAI. WebShop and ReAct papers, the foundational work on agents that reason and act.
- September 23, Agentic AI Frameworks and AutoGen. Chi Wang, AutoGen AI. Plus Jerry Liu, LlamaIndex, on building a multimodal knowledge assistant.
- September 30, Enterprise trends and key components. Burak Gokturk, Google. Grounding capabilities on Vertex AI, needle in a haystack retrieval.
- October 7, Compound AI Systems and DSPy. Omar Khattab, Databricks. Optimizing instructions and demonstrations for multi stage language model programs.
- October 14, Agents for Software Development. Graham Neubig, Carnegie Mellon. SWE agent, OpenHands, agent computer interfaces for software engineering.
- October 21, AI Agents for Enterprise Workflows. Nicolas Chapados, ServiceNow. WorkArena, TapeAgents, knowledge work tasks.
- October 28, Unified Neural and Symbolic Decision Making. Yuandong Tian, Meta AI FAIR. Beyond A* search with transformers, Dualformer.
- November 4, Project GR00T Generalist Robotics. Jim Fan, NVIDIA. Voyager, Eureka, DrEureka, sim to real transfer.
- November 18, Open Source and Science in the Era of Foundation Models. Percy Liang, Stanford. Cybench, evaluating cybersecurity capabilities.
- November 25, Measuring Agent Capabilities and Anthropic RSP. Ben Mann, Anthropic. Responsible Scaling Policy, computer use models.
- December 2, Building Safe and Trustworthy AI Agents. Dawn Song, UC Berkeley. Trustworthiness, evidence-based AI policy.
LLM Agents MOOC Lecture by Lecture Deep Dive Part 1
The LLM Agents MOOC lectures are dense. Below is a deeper look at lectures 1 through 4 of the LLM Agents MOOC with the supplementary readings and the practical takeaways. Treat this as a study guide for what to focus on when you watch each lecture.
Lecture 1, LLM Reasoning, by Denny Zhou of Google DeepMind, is the foundational reasoning lecture. Zhou covers chain of thought reasoning without prompting, the role of premise order in reasoning quality, and why LLMs cannot reliably self correct reasoning errors yet. The supplementary readings are the original chain of thought paper, the self consistency paper, and the self correction critique paper. The practical takeaway is that self correction patterns in agents, where the model checks its own work, are unreliable for reasoning tasks without external verification.
Lecture 2, LLM agents brief history and overview, by Shunyu Yao of OpenAI, is the foundational agents lecture. Yao is the author of the ReAct paper, which introduced the reasoning and acting loop that every modern agent framework implements. The lecture walks WebShop, ReAct, and the line of work that turned LLMs from chatbots into tool-using agents. The supplementary readings are the ReAct paper, the WebShop paper, and the Toolformer paper. The practical takeaway is that the ReAct pattern, think, act, observe, is the basic building block of every agent system you will build.
Lecture 3, Agentic AI Frameworks and AutoGen, by Chi Wang of AutoGen AI plus Jerry Liu of LlamaIndex, is the frameworks lecture. The lecture covers AutoGen in the first half and LlamaIndex in the second half. The AutoGen half walks the multi-agent conversation pattern. The LlamaIndex half walks building a multimodal knowledge assistant. The supplementary readings are the AutoGen paper and the LlamaIndex documentation. The practical takeaway is that frameworks encode assumptions about agent design, and understanding those assumptions matters more than the framework choice itself.
Lecture 4, Enterprise trends and key components, by Burak Gokturk of Google, is the production patterns lecture. Gokturk covers grounding capabilities on Vertex AI, needle in a haystack retrieval, and the architecture patterns Google sees in enterprise agent deployments. The supplementary readings are the Vertex AI grounding documentation and the retrieval-augmented generation survey. The practical takeaway is that enterprise agent deployments look very different from research prototypes, mostly because retrieval and grounding are most of the engineering work.
These four lectures together are roughly the first third of the LLM Agents MOOC and they establish the vocabulary the rest of the course builds on.
The Five Certificate Tiers
Berkeley offers five certificate tiers, each requiring more work than the previous.
Trailblazer. Complete all twelve quizzes and the written article assignment.
Mastery. Trailblazer requirements plus all three lab assignments.
Ninja. Mastery requirements plus a submitted project to the LLM Agents Hackathon.
Legendary. Ninja requirements plus prize winner or finalist at the hackathon.
Honorary. Awarded to the most helpful students in the Discord, with Ninja or Mastery coursework.
As of mid 2026 the course is archived but certificates are still issued on request via the Discord. The Fall 2024 cohort certificates have all been released. The Spring 2025 sequel, which we cover as Post 08 on this site, has its own equivalent tier structure.
LLM Agents MOOC Lecture by Lecture Deep Dive Part 2
Lectures 5 through 8 of the LLM Agents MOOC cover compound AI systems, software engineering agents, enterprise workflows, and neurosymbolic methods. Below is the same study guide format as Part 1.
Lecture 5, Compound AI Systems and DSPy, by Omar Khattab of Databricks, is the prompt optimization lecture. DSPy is the framework Khattab built for optimizing multi stage language model programs. The lecture walks the DSPy programming model, the optimizer loop, and the benchmark gains on multi hop question answering. The supplementary readings are the DSPy paper and the retrieve stage papers it builds on. The practical takeaway is that prompt engineering at scale becomes an optimization problem, not a writing problem.
Lecture 6, Agents for Software Development, by Graham Neubig of Carnegie Mellon, is the SWE agent lecture and is the most cited lecture in the LLM Agents MOOC. Neubig covers SWE agent and OpenHands. The lecture walks the agent computer interface pattern, the SWE bench evaluation harness, and the open-source OpenHands platform. The supplementary readings are the SWE agent paper and the OpenHands documentation. The practical takeaway is that the interface between the agent and its environment is a design decision, not a fixed constraint.
Lecture 7, AI Agents for Enterprise Workflows, by Nicolas Chapados of ServiceNow, is the enterprise agents lecture. Chapados covers WorkArena, TapeAgents, and the architecture patterns ServiceNow uses for agent deployments in enterprise settings. The supplementary readings are the WorkArena paper and the TapeAgents paper. The practical takeaway is that enterprise agent evaluation is dominated by benchmark design, not by model choice.
Lecture 8, Unified Neural and Symbolic Decision Making, by Yuandong Tian of Meta AI FAIR, is the neurosymbolic lecture. Tian covers search augmented transformers, Dualformer, and the line of work that combines neural networks with classical symbolic search. The supplementary readings are the Dualformer paper and the Beyond A* Search paper. The practical takeaway is that pure neural approaches plateau on tasks that require provably correct reasoning, and the neurosymbolic patterns are how the field is addressing that.
These four lectures are roughly the middle third of the LLM Agents MOOC and they cover the major research directions in production agent systems.
Prerequisites and What You Need
This is a graduate level Berkeley computer science course. The course does not teach Python or basic machine learning. It assumes both.
You need to be comfortable reading research papers. Each lecture has two to four supplementary readings from arXiv. The course does not require you to read them all but you will get more out of the lectures if you do.
You need a Python environment for the labs. The three labs are hands-on agent building assignments using the frameworks from lecture. You need an LLM API key for the labs. Berkeley does not provide one. Budget a small amount of API spend. Set a hard cap in the API console before you start, because agent labs loop model calls in a way a single homework problem never does.
You do not need a Berkeley account, an enrollment, or any registration. All lectures are on YouTube. All slides are on the Berkeley RDI website. All quizzes are Google Forms. The only registration is for the certificate declaration form, which is optional.
LLM Agents MOOC Lecture by Lecture Deep Dive Part 3
Lectures 9 through 12 of the LLM Agents MOOC cover robotics, open source and science, measuring agent capabilities, and trustworthy AI. These are the most research frontier lectures in the course.
Lecture 9, Project GR00T Generalist Robotics, by Jim Fan of NVIDIA, is the robotics lecture. Fan covers Voyager, Eureka, DrEureka, and the sim to real transfer pipeline that underlies NVIDIA’s robotics work. The supplementary readings are the Voyager paper and the Eureka paper. The practical takeaway is that the patterns that work for text agents, exploration, reward shaping, self improvement, also work for embodied agents when the action space is well defined.
Lecture 10, Open Source and Science in the Era of Foundation Models, by Percy Liang of Stanford, is the policy and science lecture. Liang covers the Center for Research on Foundation Models, the HELM evaluation framework, and Cybench, the cybersecurity capabilities benchmark. The supplementary readings are the HELM paper and the Cybench paper. The practical takeaway is that open evaluation infrastructure is what keeps the foundation model ecosystem honest, and the LLM Agents MOOC sits inside that open science tradition.
Lecture 11, Measuring Agent Capabilities and Anthropic RSP, by Ben Mann of Anthropic, is the safety and measurement lecture. Mann covers Anthropic’s Responsible Scaling Policy, the computer use model family, and the evaluation methodology Anthropic uses to decide what models can be deployed. The supplementary readings are the RSP document and the computer use system card. The practical takeaway is that frontier labs treat agent capability evaluation as a deployment gate, not just a research exercise.
Lecture 12, Building Safe and Trustworthy AI Agents, by Dawn Song of UC Berkeley, is the closing lecture and the trustworthiness synthesis. Song covers the trustworthiness taxonomy the Berkeley RDI lab uses, the evidence-based AI policy framework, and the open research problems in agent safety. The supplementary readings are the RDI lab’s trustworthiness papers. The practical takeaway is that safety and trustworthiness are first class research concerns, not afterthoughts, and the LLM Agents MOOC ends on that note deliberately.
These four lectures are the final third of the LLM Agents MOOC and they sit at the research frontier.
Why Academic Foundations Matter for Practitioners
If you are an engineer who has shipped agents, the question is whether you need the academic background. The answer is yes, for two reasons.
First, the field is moving fast and the vocabulary is unstable. When a paper says reflection or planning or tool use, the meaning depends on which research tradition the authors are coming from. Berkeley’s lectures are the canonical reference for what each term means in the literature. Knowing the canonical meaning saves you from confusing yourself and your team.
Second, the failure modes of agents are not arbitrary. They follow patterns the research community has documented. Hallucination in tool calls. Compounding errors in multi-step reasoning. brittleness in prompt variations. Knowing the patterns lets you debug faster. You can read a new agent failure and immediately say this is the same pattern as the SWE-agent paper documented.
The twelve lectures together give you the vocabulary, the patterns, and the research landmarks. That pays off across every other course in this series.
LLM Agents MOOC Three Labs in Detail
The LLM Agents MOOC has three hands-on lab assignments, each tied to a section of the course. The labs are required for the Mastery certificate tier and above. They are optional for the Trailblazer tier. Below is what each lab covers.
Lab 1, Basic Agent Building. Build a simple ReAct style agent using one of the frameworks from Lectures 2 or 3, either AutoGen, LlamaIndex, smolagents, or LangGraph. The lab tests whether you can wire an LLM to a tool, run the agent loop, and produce a structured output. Time budget is roughly five to ten hours.
Lab 2, Multi Tool Agent. Build an agent that uses at least three tools, with conditional routing between them based on the user query. This lab tests whether you can compose tools and handle the failure modes that come with composition, tool call failures, ambiguous routing, partial results. Time budget is roughly ten to fifteen hours.
Lab 3, Agentic RAG or Domain-Specific Agent. Build an agent that solves a real problem in a domain of your choice, using retrieval-augmented generation, multi-step reasoning, or both. This lab is the closest to a production agent of the three. Time budget is roughly fifteen to twenty hours.
All three labs are graded on a rubric the LLM Agents MOOC teaching team publishes. The rubric is publicly available even if you are not enrolled, which makes the labs a useful exercise for self paced learners who want a real test of their agent engineering skill. Submitting the labs for grading requires joining a cohort, but doing them on your own does not.
The three labs together are roughly thirty to forty five hours of focused work. They are the difference between watching the LLM Agents MOOC lectures and actually being able to build what the lectures describe. Most learners who stop at the Trailblazer tier skipped the labs, and most learners who finish the labs say the labs were where the course actually clicked.
How This Course Fits in the Series
The natural sequence on this site is Kaggle Gen AI Intensive for broad literacy, Hugging Face AI Agents Course for hands-on agent engineering, Microsoft AI Agents for production depth, Anthropic Academy for Claude specific certification, and then this Berkeley course for academic foundations. The order is not strict. If you are an engineer who learns by doing, do Berkeley after you have shipped an agent. If you are a researcher who learns by reading, do Berkeley first.
LLM Agents MOOC Hackathon Projects
The LLM Agents MOOC runs a hackathon alongside each cohort. The hackathon is required for the Ninja certificate tier and prize winning or finalist at the hackathon is required for the Legendary tier. Below is what the hackathon looks like in practice.
The hackathon runs for roughly four to six weeks alongside the lecture schedule. Teams of one to four people build an agent project, submit it for judging, and the top entries get prize money plus the credential bump. The hackathon is open ended, any agent project counts, but most teams pick projects that combine two or three techniques from the course.
Representative LLM Agents MOOC hackathon projects from the Fall 2024 cohort include a code review agent built on OpenHands with a custom evaluation harness, a research assistant that chained DSPy optimized prompts with LlamaIndex retrieval, and a customer support agent built on AutoGen with WorkArena style evaluation. Public project writeups from the cohort are linked from the LLM Agents Discord.
The hackathon is the highest leverage part of the LLM Agents MOOC for career outcomes. The prize winning entries are publicly listed, the judges are named researchers, and a Legendary tier certificate with a prize winning project is a meaningful signal for hiring managers in agent research roles. Most learners who treat the LLM Agents MOOC as a career investment do the hackathon.
The archived version of the LLM Agents MOOC still accepts hackathon entries, but the prize pool and the live judging are cohort specific. If you want a prize winning entry, do the LLM Agents MOOC during a live cohort. If you want the learning without the prize, do the archived version and submit a personal project to the Discord for community feedback instead.
LLM Agents MOOC Spring 2025 Sequel Preview
The Spring 2025 sequel to the LLM Agents MOOC at llmagents-learning.org/sp25 is the advanced follow up course. The Fall 2024 LLM Agents MOOC is the broad survey. The Spring 2025 course is the deep dive on reasoning, formal math, and verifiable code generation. We cover Spring 2025 in detail as Post 08 in this series, but the preview below helps you decide whether to do both.
The Spring 2025 course assumes you have done Fall 2024 or equivalent. It does not recap the basics. The lecture list for Spring 2025 includes formal mathematical reasoning, theorem proving with LLMs, program synthesis, verifiable code generation, and the line of work that combines neural networks with formal methods. The guest lecturers overlap with Fall 2024, with new additions from the formal methods community.
The Spring 2025 labs are harder than the Fall 2024 labs. Lab 1 in Spring 2025 is roughly at the level of Lab 3 in Fall 2024. The Spring 2025 hackathon, if you do it, expects you to ship a research-grade artifact. The certificate tiers are similar but the bars are higher at every tier.
The right pattern for learners doing the AI Agents series on this site is do the Fall 2024 LLM Agents MOOC first, ship a project, then do the Spring 2025 sequel only if you want research depth in formal reasoning or verifiable code generation. Most practitioners do not need Spring 2025. Most researchers do.
The Spring 2025 course is also publicly available, also free, also does not require registration. Berkeley’s commitment to publishing these courses openly is what makes the LLM Agents MOOC and its sequel the canonical graduate level agent curriculum on the open web.
Watch Lecture 1 Free
The fastest way to see what the course is like is to watch Lecture 1 on YouTube. Denny Zhou from Google DeepMind on LLM reasoning. The lecture is roughly two hours, the slides are downloadable, and the supplementary readings are linked from the syllabus page.
open "https://llmagents-learning.org/f24"
# Click Lecture 1 livestream link, or search YouTube for "Denny Zhou LLM Reasoning Berkeley"If after Lecture 1 you want more, do the rest in order. If Lecture 1 feels too theoretical, skip ahead to a more applied lecture like Graham Neubig on agents for software development or Jim Fan on robotics.
LLM Agents MOOC vs Stanford CS224N and CS324
The LLM Agents MOOC is sometimes compared to Stanford’s CS224N Natural Language Processing with Deep Learning and CS324 Large Language Models. The three courses are the canonical graduate level publicly available AI courses, and knowing the differences helps you pick the right one.
CS224N is Stanford’s long running NLP deep learning course, taught by Christopher Manning. It covers the full NLP stack from word embeddings through transformers, with a strong focus on linguistic foundations. CS224N predates the LLM era and was updated to include LLM content, but its center of gravity is NLP fundamentals. CS224N is the right course if you want the linguistic and ML foundations.
CS324 is Stanford’s newer LLM course, taught by Percy Liang and Tatsunori Hashimoto. It covers LLM capabilities, training, evaluation, and societal impact. CS324 is closer to the LLM Agents MOOC in topic and in research depth, but it focuses on LLMs as standalone systems, not on agents. CS324 is the right course if you want depth on LLMs themselves.
The LLM Agents MOOC sits beside both. It assumes you have the CS224N and CS324 background, or equivalent. Where CS224N teaches NLP and CS324 teaches LLMs, the LLM Agents MOOC teaches what you build on top of LLMs, namely tool-using agents, multi-agent systems, and agentic workflows. Percy Liang, who teaches CS324 at Stanford, is also a guest lecturer in the LLM Agents MOOC, which makes the connection explicit.
For learners doing the AI Agents series on this site, the LLM Agents MOOC is the right academic foundations course. CS224N and CS324 are the right prerequisites if your ML background is light. If you already have a graduate ML background, skip CS224N and CS324 and do the LLM Agents MOOC directly.
LLM Agents MOOC Alumni Outcomes and Career Paths
The LLM Agents MOOC does not publish alumni outcome data the way a bootcamp does, but the Discord has a dedicated careers channel where alumni post about job changes, and the patterns are consistent enough to summarize.
Alumni who complete the Mastery tier or above and ship a hackathon project most commonly move into applied research or senior engineering roles at companies building agent products. The lecture material plus a built artifact is the combination that hiring managers at frontier labs and at well funded startups reward. The LLM Agents MOOC alone, without a built artifact, is a weaker signal because the certificate does not prove engineering ability.
Alumni who complete only the Trailblazer tier most commonly use the course as preparation for other agent engineering work, either a deeper course like the Hugging Face AI Agents Course on this site or a direct move into shipping agents at their current job. Trailblazer alone is not a career changing credential, but it is the right foundation for the work that follows.
The career path the LLM Agents MOOC most directly supports is the move from general software engineering into agent engineering or applied ML research. The lectures give you the vocabulary and the failure mode catalog. The labs give you the engineering practice. The hackathon gives you a portfolio piece. Together those three are roughly what you need to interview credibly for an agent engineering role.
For learners targeting PhD programs in agent research, the LLM Agents MOOC plus the Spring 2025 sequel plus reading the foundational papers in full is closer to the preparation a first year Berkeley graduate student would have. Several alumni on the Discord report using the LLM Agents MOOC as part of their PhD application preparation, particularly international applicants who do not have access to an equivalent course at their home institution.
A Worked Example, the SWE Agent Lecture in Depth
Lecture 6 by Graham Neubig of Carnegie Mellon is the lecture most cited by engineers who have done this course. Neubig’s research focuses on agents for software engineering, and the lecture covers two of his major projects, SWE-agent and OpenHands.
SWE-agent is an agent computer interface designed for software engineering. The key insight is that the agent’s interface to the computer matters as much as the agent’s reasoning. Most agent interfaces are designed for chat, which is the wrong interface for software engineering. SWE-agent’s interface is designed for code editing, file navigation, and test execution, which makes the agent dramatically more effective on real software bugs.
OpenHands is the open-source platform Neubug’s group maintains for AI software developers as generalist agents. It is the successor to SWE-agent with a broader scope. The lecture covers the architecture, the evaluation harness, and the benchmark results on SWE-bench, the standard benchmark for software engineering agents.
What you should take from this lecture is the agent computer interface pattern. The interface between the agent and its environment is a design decision, not a fixed constraint. Most production agent failures are interface failures, not reasoning failures. The agent does the right thing but cannot communicate it through the interface. Designing the interface deliberately is the highest leverage engineering choice in agent systems.
LLM Agents MOOC Readings Roadmap
The LLM Agents MOOC lectures cite roughly thirty to forty arXiv papers across the twelve lectures. Reading all of them is roughly sixty to eighty hours of additional work. Below is the prioritized reading roadmap we recommend.
Read in full: the ReAct paper by Yao et al, the chain of thought paper by Wei et al, the SWE agent paper by Yang et al, the DSPy paper by Khattab et al, the Toolformer paper by Schick et al, and the HELM paper by Liang et al. These six papers are the foundation of every framework and every benchmark the course covers. Roughly fifteen hours of reading.
Read the abstract and introduction only: every other cited paper. The abstract and introduction of an arXiv paper give you the contribution, the method, and the result in roughly fifteen minutes per paper. Roughly ten to fifteen hours total for the remaining twenty to thirty papers.
Skip on first pass: papers cited as related work rather than as primary readings. The lecture slides flag which is which.
The LLM Agents MOOC does not test you on the readings, but the labs and the hackathon implicitly assume you have read at least the six foundational papers. Doing the readings before the labs is the highest leverage preparation you can do for the LLM Agents MOOC certificate tiers that require lab work.
LLM Agents MOOC Certificate Tier Strategies
The five certificate tiers in the LLM Agents MOOC are Trailblazer, Mastery, Ninja, Legendary, and Honorary. Each tier requires more work than the previous one. Below is the realistic strategy for each tier.
Trailblazer is the entry tier. Complete all twelve quizzes and the written article assignment. The quizzes are Google Forms with the answers in the lecture material, so Trailblazer is mostly a reading comprehension certificate. Realistic time budget is twenty to thirty hours, which is one lecture per week with readings. Most self paced learners stop at Trailblazer.
Mastery is the engineering tier. Trailblazer requirements plus all three lab assignments. The labs are the real work. Realistic time budget is fifty to seventy hours, which adds the thirty to forty five hours of lab work on top of the lecture work. Mastery is the tier most hiring managers care about because it proves you can actually build.
Ninja is the project tier. Mastery requirements plus a submitted project to the hackathon. The hackathon is open ended and the time budget depends on the project. Most Ninja tier completers spend another twenty to forty hours on the hackathon project on top of the Mastery work. Ninja is the right tier for learners who want a portfolio piece.
Legendary is the prize tier. Ninja requirements plus a prize winning or finalist entry at the hackathon. Legendary is gated by judging, not just by effort, and most Legendary completers spent at least sixty hours on the hackathon project. Legendary is rare and is the credential for agent research roles.
Honorary is the community tier. Awarded to the most helpful students on the Discord, with Mastery or Ninja coursework. Honorary cannot be earned by coursework alone, it requires sustained community contribution across a cohort.
The realistic certificate strategy for self paced learners on the LLM Agents MOOC is target Trailblazer for the first pass, then come back for Mastery in a second pass with the labs.
LLM Agents MOOC Discord and Community Norms
The LLM Agents MOOC has a Discord server that is the main community hub. The Discord is where Dawn Song’s teaching team makes announcements, where alumni answer current student questions, and where the careers channel lives. Joining the Discord is the single most useful non lecture action you can take during the course.
The Discord has channels for each lecture, channels for each lab, a hackathon channel, a careers channel, and a certificate requests channel. The lecture channels are where you ask questions about specific lecture content. The lab channels are where you ask for help when you are stuck. The certificate requests channel is how you request a certificate if you complete the work as a self paced learner.
The unwritten community norm of the LLM Agents MOOC Discord is the same as the Hugging Face Discord, search before asking. The course has run multiple cohorts and most questions have been asked before. The search bar covers everything.
The Discord is also where Berkeley announces new cohorts, new Spring sequels, and new certificate issuing windows. If you are doing the archived version, joining the Discord is how you find out when the next live cohort opens for enrollment.
LLM Agents MOOC: Common Mistakes to Avoid
The course is well regarded but it invites predictable mistakes.
- Treating it as a video only course. The lectures are dense and reference papers throughout. If you skip the readings you will miss half the content.
- Skipping the labs. The Mastery tier certificate requires the labs. Without the labs you stop at Trailblazer, which is the reading comprehension tier.
- Trying to do all twelve lectures in a weekend. Each lecture is roughly two hours of video plus two to four hours of reading. A realistic pace is one lecture per week.
- Confusing this course with the Spring 2025 sequel. The Spring 2025 course at
llmagents-learning.org/sp25is the advanced sequel, focused on formal math and reasoning. We cover it as Post 08 on this site. They are sister courses with different focus.
LLM Agents MOOC: Best Practices
- Do one lecture per week with the assigned readings. The pace matches the original cohort.
- Join the LLM Agents Discord even if you are doing the archived version. The community is active and answers questions.
- Pair the course with the earlier courses in this series for hands-on practice. Berkeley is theory heavy, the others are practice heavy.
- Submit a project to the next hackathon if you want the Ninja or Legendary tier. The hackathon runs alongside each cohort.
- Read at least the abstract and introduction of every cited paper. The full read is optional, the abstract is not.
LLM Agents MOOC: Frequently Asked Questions
Is the LLM Agents MOOC still free?
Yes. All lectures, slides, quizzes, and lab instructions are publicly accessible without registration. The course is archived but the materials remain available.
Do I need a Berkeley account?
No. The course is open. You need a Google account for the quizzes since they are Google Forms. You need an LLM API key for the labs. You do not need any Berkeley credential.
Does the course still issue certificates?
Yes on request. The Fall 2024 cohort certificates have all been issued. New completers can request a certificate via the LLM Agents Discord. The five tier structure remains in effect.
How is this different from the Spring 2025 Berkeley course?
The Fall 2024 course, this one, is the broad survey. The Spring 2025 course at llmagents-learning.org/sp25 is the advanced sequel focused on reasoning, formal math, and verifiable code generation. We cover Spring 2025 as Post 08 in this series.
How long does the course take?
Twelve lectures at two hours each plus readings and quizzes is roughly forty to sixty hours of focused work. The three labs add another twenty to thirty hours if you do them. The hackathon is open ended.
What background do I need?
Berkeley upper division computer science level. Solid ML and DL foundations, Python fluency, comfort with arXiv papers. If you do not have this background, do the earlier courses on this site first.
LLM Agents MOOC from UC Berkeley is the academic foundations course. Pair it with the earlier industry courses on this site for hands-on practice and with the Spring 2025 sequel for advanced depth.