Principles of Building AI Agents

Principles of Building AI Agents

• This book, "Principles of Building AI Agents," guides you through creating AI agents, focusing on key elements like providers, models, prompts, tools, and memory. It also covers complex task management, knowledge base access using RAG, and multi-agent systems.

• By reading this book, you'll be equipped to:

  • Master the core components of AI agents.
  • Design effective agentic workflows.
  • Integrate knowledge bases for enhanced agent performance.

Core Content:

1. Key Building Blocks of Agents:

• Providers: Different companies like OpenAI, Anthropic, Google, and Meta offer LLMs with varying strengths.
  • Choose a provider based on your specific needs, considering factors like cost, accuracy, and latency.
• Models: LLMs predict the next token (word or punctuation) in a sequence.
  • Select a model size based on your project's needs; larger models are more accurate but slower and more expensive.
• Prompts: Instructions that guide LLMs, which can be zero-shot, single-shot, or few-shot.
  • Use a "seed crystal" approach to generate initial prompts and refine them iteratively.
• Tools: Functions that agents use to perform specific tasks like fetching data or processing calculations.
  • Provide detailed descriptions and clear input/output schemas for each tool.
• Memory: Allows agents to maintain context over time, including working memory and hierarchical memory.
  • Use recent messages and relevant long-term memories to formulate responses.

2. Breaking Down Complex Tasks with Agentic Workflows:

• Graph-Based Workflows: Useful when agents don’t deliver predictable enough output.
  • Break down problems and define decision trees for agents to make binary decisions.
• Branching: Trigger multiple LLM calls on the same input.
  • Use branching to check for multiple symptoms in a medical record simultaneously.
• Chaining: Fetch data from a remote source before feeding it into an LLM, or feed the results of one LLM call into another.
  • Chain steps to create a sequence of actions that build upon each other.
• Merging: Combine results after branching paths diverge.
• Conditions: Make decisions based on intermediate results.
  • Execute steps conditionally based on the success or failure of previous steps.
• Suspend and Resume: Pause execution while waiting for third-party input.
  • Persist the state of the workflow and resume when input is available.

3. Giving Agents Access to Knowledge Bases with RAG (Retrieval-Augmented Generation):

• Chunking: Break down documents into smaller pieces for search.
  • Balance context preservation with retrieval granularity.
• Embedding: Transform data into vectors representing the meaning of the text.
  • Use LLMs to make embeddings more accurate.
• Indexing: Set up an index in a vector DB to store document chunks as vector embeddings.
• Querying: Compare the query string to all chunks in the database and return the most similar ones.
  • Use cosine similarity or other algorithms for efficient searching.
• Reranking: Improve the ordering of results using more computationally expensive methods.
• Synthesis: Pass results as context into an LLM to synthesize an answer to the user.

4. Multi-Agent Systems:

• Agent Supervisors: Coordinate and manage other agents.
  • Pass other agents as tools to the supervisor agent.
• Control Flow: Establish an approach before diving into execution.
  • Engage with agents on architectural details first.
• Workflows as Tools: Turn tasks into individual workflows and pass them as tools to agents.

5. Testing with Evals:

• Textual Evals: Evaluate the correctness, truthfulness, and completeness of agent responses.
  • Check for hallucinations, faithfulness, content similarity, completeness, and answer relevancy.
• Classification or Labeling Evals: Determine how accurately a model tags or categorizes data.
• Agent Tool Usage Evals: Measure how effectively a model calls external tools or APIs.
• Prompt Engineering Evals: Explore how different instructions impact agent performance.

6. Local Development and Serverless Deployment:

• Local Development: Use tools like agent chat interfaces, workflow visualizers, tool playgrounds, and tracing to iterate on code.
• Serverless Deployment: Deploy agents and workflows on serverless platforms like Vercel, Cloudflare Workers, and Netlify.

7. Observability:

• Tracing: Visualize application traces to debug functions.
  • Use the OpenTelemetry (Otel) standard for traces.
• Evals: See evals in a cloud environment to compare agent responses with expected outcomes.

Q&A

Q: What is the benefit of using a hosted provider like OpenAI versus an open-source model?

A: Hosted providers allow you to prototype quickly without worrying about infrastructure issues. Even if you plan to use open-source models eventually, starting with cloud APIs can save time and effort.

Q: How do I choose the right model size for my AI application?

A: Start with a more expensive, accurate model during prototyping. Once you have something working, you can tweak the cost by switching to a smaller, faster model.

Q: What are the key steps in setting up a RAG pipeline?

A: The key steps include chunking documents, embedding data into vectors, indexing vectors in a vector DB, querying the database, reranking results, and synthesizing an answer using an LLM.

Q: How can I ensure my AI application delivers sufficient quality?

A: Use evals to provide quantifiable metrics for measuring agent quality. Different types of evals include textual evals, classification evals, tool usage evals, and prompt engineering evals.