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If you are learning how to build AI agents, knowing how to call an LLM or API is only the beginning. The harder problem is building agents that can evaluate their results, use feedback, recover from failures, and improve their performance.
Stanford's CS329A - Self Improving AI Agents is a graduate seminar focused on exactly this area. Stanford describes the course as covering AI agents that can continuously improve through interaction with themselves and their environment.
🎯 The core idea is simple:
A useful AI agent should not only produce an answer. It should be able to reason, act, observe results, verify what happened, and use that information to improve future attempts.
An agentic system can be much more involved:
The difference is important.
An agent can use tools, retrieve information, interact with an environment, execute code, evaluate its own output, and use feedback to guide another attempt.
This does not necessarily mean the model is retraining its neural network after every task. In practice, "self-improvement" can involve techniques such as better inference-time search, verification, feedback, memory, tool use, and other mechanisms that improve how the system performs a task. Stanford's course covers several of these approaches.
For example, an agent might generate code that looks reasonable but fails when executed.
A verification step can provide a much stronger signal:
For coding agents, this can be especially useful because code can often be compiled, executed, and tested. Stanford's related agentic AI material highlights execution feedback and verification as important mechanisms for improving agent performance.
The same general idea can be applied to other tasks when there is a reliable way to evaluate the result.
Instead of immediately accepting the first response generated by a model, a system can spend additional computation exploring multiple possibilities, reasoning further, or using a verifier to select a better result.
This creates a different approach to improving AI systems:
Stanford's CS329A materials discuss test-time scaling and combining search with LLMs as part of the course's self-improvement techniques.
This is where tool use, retrieval, and memory become important.
An agent might:
These techniques are part of the broader CS329A curriculum, which covers tool use, retrieval, memory, and multimodal web interaction.
More difficult tasks can require a sequence of decisions:
This makes reasoning and planning important parts of agent design.
Instead of asking an LLM to solve everything in one response, an agent can break a problem into smaller steps and adapt its plan based on what happens during execution.
CS329A specifically covers multi-step reasoning and planning problems for agentic workflows. (Stanford University Bulletin)
A more complete development process is:
Build → Test → Measure → Find Failure → Improve → Test Again
Without evaluation, it is difficult to know whether a change actually made the agent better.
Useful questions include:
An agent can generate code, run it, inspect errors, modify the implementation, and test it again.
For example:
The execution result becomes a feedback signal.
This is one reason software engineering is an important application area for agent research. Stanford's course specifically includes coding agents among the industry applications discussed.
Also ask:
🎓 Course: CS329A - Self Improving AI Agents
📺 Lecture playlist: Stanford CS329A Self-Improving AI Agents
The course is especially relevant if you already understand basic LLM APIs and want to go deeper into agent reasoning, verification, feedback, evaluation, memory, tool use, and self-improvement.
Stanford's CS329A - Self Improving AI Agents is a graduate seminar focused on exactly this area. Stanford describes the course as covering AI agents that can continuously improve through interaction with themselves and their environment.
🎯 The core idea is simple:
A useful AI agent should not only produce an answer. It should be able to reason, act, observe results, verify what happened, and use that information to improve future attempts.
What Is Self-Improving AI?
A basic LLM workflow might look like this:Prompt → LLM → AnswerAn agentic system can be much more involved:
Reason → Act → Observe → Verify → ImproveThe difference is important.
An agent can use tools, retrieve information, interact with an environment, execute code, evaluate its own output, and use feedback to guide another attempt.
This does not necessarily mean the model is retraining its neural network after every task. In practice, "self-improvement" can involve techniques such as better inference-time search, verification, feedback, memory, tool use, and other mechanisms that improve how the system performs a task. Stanford's course covers several of these approaches.
What Does Stanford CS329A Cover?
CS329A explores several important areas of modern agentic AI:- 🧠 Self-improvement techniques for LLMs
- ✅ Verifiers and rewards
- ⚡ Test-time compute
- 🔎 Search combined with LLMs
- 🛠️ Tool use
- 📚 Retrieval and memory
- 🧩 Reasoning and planning
- 📊 Agent evaluation and orchestration
- 💻 Coding agents
- 🔬 Research assistants and other agent applications
Why Verification Matters for AI Agents
One of the biggest challenges with AI agents is knowing whether an answer or action is actually correct.For example, an agent might generate code that looks reasonable but fails when executed.
A verification step can provide a much stronger signal:
Generate → Execute → Test → Check Result → ReviseFor coding agents, this can be especially useful because code can often be compiled, executed, and tested. Stanford's related agentic AI material highlights execution feedback and verification as important mechanisms for improving agent performance.
The same general idea can be applied to other tasks when there is a reliable way to evaluate the result.
Test-Time Compute and Search
Another important topic is test-time compute.Instead of immediately accepting the first response generated by a model, a system can spend additional computation exploring multiple possibilities, reasoning further, or using a verifier to select a better result.
This creates a different approach to improving AI systems:
More inference-time work → More candidate solutions → Verification → Better resultStanford's CS329A materials discuss test-time scaling and combining search with LLMs as part of the course's self-improvement techniques.
Tools, Retrieval, and Memory
AI agents also need access to information and capabilities outside the base model.This is where tool use, retrieval, and memory become important.
An agent might:
- 🧠 Reason about the task.
- 🔎 Retrieve relevant information.
- 🛠️ Call a tool or API.
- 👀 Observe the result.
- ✅ Verify the result.
- 🔄 Adjust its next action.
These techniques are part of the broader CS329A curriculum, which covers tool use, retrieval, memory, and multimodal web interaction.
Reasoning and Planning
Simple tasks may only require one or two actions.More difficult tasks can require a sequence of decisions:
Goal → Plan → Action → Observation → New Decision → Action → ResultThis makes reasoning and planning important parts of agent design.
Instead of asking an LLM to solve everything in one response, an agent can break a problem into smaller steps and adapt its plan based on what happens during execution.
CS329A specifically covers multi-step reasoning and planning problems for agentic workflows. (Stanford University Bulletin)
Agent Evaluation Is Just as Important as Agent Building
A common mistake when learning AI agents is focusing only on how to make an agent work.A more complete development process is:
Build → Test → Measure → Find Failure → Improve → Test Again
Without evaluation, it is difficult to know whether a change actually made the agent better.
Useful questions include:
- Does the agent complete the task correctly?
- How often does it fail?
- Can it recover from errors?
- Does additional reasoning improve the result?
- Which tools does it use successfully?
- Does it waste unnecessary tool calls?
- Can the system handle longer tasks reliably?
Coding Agents Are a Good Example
Coding agents provide a useful example of self-improvement.An agent can generate code, run it, inspect errors, modify the implementation, and test it again.
For example:
Write Code → Run Tests → Read Failure → Fix Code → Run Tests AgainThe execution result becomes a feedback signal.
This is one reason software engineering is an important application area for agent research. Stanford's course specifically includes coding agents among the industry applications discussed.
What Should You Learn From CS329A?
If you are learning AI agents, don't stop at: "How do I build an agent?"Also ask:
- 🎯 How do I measure its performance?
- ✅ How do I verify its output?
- 🔄 How does it recover from failure?
- 🧠 What information should it remember?
- 🛠️ Which tools should it use?
- 🔎 When should it search or retrieve information?
- 📊 How can I evaluate different agent designs?
- ⚡ When is additional test-time computation worth the cost?
Where to Watch Stanford CS329A
Stanford has published CS329A lecture material through Stanford Online, including a course overview and lectures covering topics such as feedback-driven improvement and agent workflows.🎓 Course: CS329A - Self Improving AI Agents
📺 Lecture playlist: Stanford CS329A Self-Improving AI Agents
The course is especially relevant if you already understand basic LLM APIs and want to go deeper into agent reasoning, verification, feedback, evaluation, memory, tool use, and self-improvement.