Agents

Administration

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• Review of Class Goals
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Course Evaluations

• 10 minutes for course evaluations

Agents

An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.

• Russel & Norvig 1995

Agents: Core Concepts (Huyen, 2025)

• An agent is characterized by:
  • The environment it operates in
  • The set of actions it can perform
• AI is the "brain" that:
  • Processes tasks
  • Plans sequences of actions
  • Determines task completion

The terminology of 'agents' has been muddled in AI community - there are many things that people call agents

We'll focus on large language models that can plan the necessarily actions to complete a task, have access to one or more tools to execute those actions, and summarize or use the outputs of those actions

Exercise: Planning a Task

Agent Components

• Environment: Defines where the agent operates (web, code editor, game, etc.)
• Tools: Extend the agent's capabilities
  • Knowledge augmentation - add access to source documents, rather than relying on model's learned nowledge (e.g. retrieval augmented execution)
  • Capability extension - the things that Language Models can't do themselves (e.g. calculator, code execution, image generation)
  • Write actions (file editing, sending emails)
• Planning: The agent's ability to reason about how to accomplish tasks

Huyen, 2025

We've Seen Agents in Class Already

• Retrieval-Augmented Generation -> Giving an LLM access to web search, or search over your own database
• DALL-E -> Currently, GPT doesn't generate images itself - If you ask for an image, it generates a text-to-image prompt and sends that prompt to a separate image generation tool
• Code Interpreter -> In our class about classification, we saw that you can visual your data at the end of a (e.g. our 'sentiment classification' exercise). There, the LLM is writing the code to visualize the data, then sending that code to a separate tool to run, then returning and interpreting the result.

The Future of Agents

• Autonomous assistants for complex tasks
• Potential to save human time despite computational costs
• Continuous improvement through:
  • Better planning capabilities
  • More sophisticated tools
  • Enhanced security measures

The risks of agents

• Real-world harm
• Unauthorized and highly-sensitive actions (financial transactions, data deletion)
• Privacy violations through excessive data access
• Security vulnerabilities from tool misuse
• Resource depletion (lots of read/write)
• Accountability gaps (who's responsible when agents make mistakes?)
• Amplification of existing biases in decision-making
• Unpredictable behaviors in complex environments

Agents Need More Powerful Models

• Compound mistakes: Errors multiply across multiple steps, so agents need
  • 95% accuracy per step → 60% accuracy over 10 steps
• Higher stakes: With tools, agents can perform more impactful tasks
  • You likely don't want to give access to your email or bank to any machine, but especially not to a poor, error-prone model

Agent Failure Modes

• Planning failures: Incorrect reasoning, missing steps
• Tool failures: Incorrect tool selection, misuse of tools
• Efficiency issues: Too many steps, redundant actions

Limits of red teaming

Since the model has access to the internet, the external red teamers were advised to avoid prompting the model to complete tasks that could cause real-world harm. In certain cases, they created test environments—such as mock websites, databases, or emails—to safely demonstrate possible exploits. Given this constraint, their findings may not fully capture the worst-case real-world risks, but still identified key vulnerabilities that informed additional mitigations

• OpenAI - Operator System Card

Agents-lite

Code-using LLMs are a basic - but powerful - form of tool-use agent

• see 'code interpreter' in ChatGPT, Javascript artifacts in Claude

Langchain

Problem: Limits to bespoke tools

Some tools are very valuable, like web search or being able to run Python code. Other tools are easy to implement, like a calculator.

But there's a whole lot of actions you may want to do, and building a tool for each one can get challenging.

Solutions?

Computer Use

• Claude Computer Use
• OpenAI Operator
• Google Mariner

Deep Research

Claude Code

Aider

Browser Use

Code Interpreter / Claude Artifacts

The Big Quiz

See Canvas. We'll do the quiz in pairs. Remember - it's just review, unless you include in your Portfolio.

Updates

Updates since I last taught the class are here. They are not formatted as slides, so refer to the 'Notes' version of the deck https://ai.porg.dev

MCP

Model Context Protocol is a standard for providing context to tools to LLMs.

https://modelcontextprotocol.io/

e.g. You can make a database or a filesystem available to a coding app, or a chat app, etc., without writing a custom tool for each

(Mar 2025 - OpenAI is introducing MCP support: https://openai.github.io/openai-agents-python/mcp)

Playwright-MCP

• Playwright is a browser-testing tool, it's what Browser Use uses
• with this server, you can give a command to an MCP LLM (e.g. Claude on your computer) and it can use a browser (in image-based-mode or all text-based)

(Simon Willison summarizes the tool and how to use it in Claude: https://simonwillison.net/2025/Mar/25/playwright-mcp/)