Chapter divider plus the opening narrative: manual LLM iteration vs autonomous agents that search, filter, summarize, and format end-to-end (PDF 177–180).
AI Agents
What is an AI Agent? Imagine you want to generate a report on the latest trends in AI research. If you use a standard LLM, you might:
1. Ask for a summary of recent AI research papers. 2. Review the response and realize you need sources. 3. Obtain a list of papers along with citations. 4. Find that some sources are outdated, so you refine your query. 5. Finally, after multiple iterations, you get a useful output. This iterative process takes time and effort, requiring you to act as the decision-maker at every step. Now, let’s see how AI agents handle this differently: A Research Agent autonomously searches and retrieves relevant AI research papers from arXiv, Semantic Scholar, or Google Scholar.
● A Filtering Agent scans the retrieved papers, identifying the most relevant ones based on citation count, publication date, and keywords.
● A Summarization Agent extracts key insights and condenses them into an easy-to-read report.
● A Formatting Agent structures the final report, ensuring it follows a clear, professional layout.
Here, the AI agents not only execute the research process end-to-end but also self-refine their outputs, ensuring the final report is comprehensive, up-to-date, and well-structured - all without requiring human intervention at every step.
To formalize AI Agents are autonomous systems that can reason, think, plan, figure out the relevant sources and extract information from them when needed, take actions, and even correct themselves if something goes wrong.