How To Build Better Agents- Agentic Design Principles

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AI Summary

Summary of Video Transcript: Improving Agent-Based Systems

Introduction

• The video discusses a research paper on improving agent-based systems for web automation.
• The paper is titled “Agent E: From Autonomous Web Navigation to Foundational Design Principles in Agenting Systems.”
• The focus is on the capabilities of web agents to sense and act on web pages, specifically for browser automation.

Research Paper Insights

• Web agents typically have two basic capabilities: sensing (understanding the state of a web page) and acting (performing actions on the web page).
• Sensing involves encoding the document object model (DOM) or using screenshots.
• Acting includes simple actions like navigating, clicking, and entering text, or composite actions like filling out forms.
• Challenges for web agents include handling noisy HTML documents, dealing with web designs optimized for humans, and executing complex multi-step tasks.

Proposed Improvements

• The research suggests assessing agent frameworks using additional parameters beyond success rates, such as task completion times, cost, and error awareness.
• Agent E operates in two modes: autonomous and human-in-the-loop, with the paper focusing on autonomous functionality.
• The paper introduces a hierarchical architecture for web agents, separating roles between a planner agent and a browser navigation agent.
• A flexible DOM distillation approach allows the agent to choose the most suitable DOM representation for a given task.
• Change observation monitors the outcome of each action, providing feedback to the agent for better state awareness and performance.
• Agent E achieved a 20% improvement over the top player in benchmarks.

Agent E’s Design and Evaluation

• Agent E was built with Autogen, a framework developed by Microsoft.
• It features a sensing agent that supports multiple DOM representations and a planner agent that guides the browser navigation agent.
• The evaluation of Agent E included task success rate, self-aware vs. oblivious fail rate, task completion times, and the number of large language model (LLM) calls.

Key Takeaways from the Research

• A well-crafted set of primitive skills can enable powerful use cases.
• Hierarchical architecture is beneficial for complex tasks.
• De-noising data before sending it to the LLM is critical for reliable and efficient agent performance.
• Providing linguistic feedback after actions supports better decision-making.
• Human-in-the-loop support is necessary for current agent frameworks.
• Reflecting on past experiences is important for self-improvement.
• Introducing guardrails into agent systems ensures safe and effective operation.
• There is a trade-off between generic agents and task-specific agents, with specialization potentially leading to higher performance.

Conclusion

• The video presenter has not tested Agent E but believes the principles discussed are important for building better agents.
• The presenter encourages viewers to like, subscribe, and leave comments with suggestions or ideas for optimization.

Detailed Instructions and URLs

• No specific CLI commands, website URLs, or detailed instructions were provided in the transcript.