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AI Agents, Evals, Optimization & MLOps Demos
00:00:03 Introduction (Shri, Arise AI): Introduces the talk focusing on AI agents, their components, evaluation methods (evals), and optimization techniques.
00:00:52 Agent Components Analogy: Uses a travel agent chatbot (Priceline's Penny) to illustrate the core parts of an agent: user input/chat, prompting, memory, and tool/API calling.
00:01:39 Agent Breakdown: Details the key components:
Router: The reasoning part that decides the next steps or skills to use.
Skills/Execution: The logic chain involving tool use, API calls (application-specific customization happens here).
Memory: The shared state holding information (like user preferences or past interactions) accessible by different components.
00:04:28 Evaluating Agent Components: Discusses how to evaluate each part:
Router Evals: Check if the agent mapped the right plan/route using correct logic (can use ground truth).
Skills/Function Evals: Evaluate the individual logic blocks or tool chains (e.g., steps within a RAG skill).
Memory/Path Evals (Convergence): Measure the number of steps taken to solve a problem, aiming to avoid inefficient loops.
00:08:04 Prompt Optimization Techniques: Introduces methods to improve agent performance via prompting:
00:08:53 Few-Shot Prompting: Providing example inputs/outputs to guide model behavior, useful for niche tasks.
00:09:51 Meta Prompting: Using an LLM to refine and optimize prompts for another (or the same) LLM.
00:16:30 Introduction (John, Union): Introduces Union as an MLOps company for building and running compound AI systems at scale, demoing an AI workflow (train, finetune, eval, serve).
00:17:44 Union Platform Overview: Explains Union provides infrastructure and an SDK for durable AI workflows, handling containerization, orchestration, data serialization, GPU management, and scaling. Mentions open-source Flight and users like Lyft, Stripe, Spotify.
00:19:23 Union SDK & Workflow: Describes the Python SDK with @task and @workflow decorators, abstracting away container and data handling complexities.
00:20:36 Workflow Demo Plan: Outlines the demo: downloading data/model, EDA, fine-tuning a BERT model for sentiment analysis, evaluation, batch prediction, and serving via a Streamlit app.
00:23:08 Deploying Apps with Union: Shows how to define app dependencies (ImageSpec), specify resources (like L4 GPUs), configure auto-scaling, and deploy the fine-tuned model as a scalable web application. (Live demo encountered an issue).
00:28:50 Introduction (Loren, Crew AI): Introduces Crew AI as a leading open-source framework for building multi-agent systems easily.
00:30:07 Crew AI Framework Features: Highlights key features: inference agnostic, multiple memory types (short-term, long-term, entity), data pre-loading, flexible tooling, various orchestration methods (Crew, Hooks, Flows), LLM-as-a-judge evals, and RLHF training.
00:34:15 Getting Started & UI Studio: Explains how to start (pip install) and introduces the no-code UI Studio for generating initial agent configurations (roles, goals, tools, tasks).
00:35:18 Contract Analysis Use Case: Walks through building a contract analysis crew: pre-processing docs, using vector search tools, defining agents (Data Retrieval, Source Citer, Report Generator) and tasks.
00:37:23 Deployment & Enterprise Platform: Shows deploying a crew to the Crew AI Enterprise platform, providing an API endpoint and a suite of tools for execution monitoring, observability, tracing, testing (LLM-as-a-judge), UI-based training, and metrics tracking.
00:43:01 Community Demo (Amir, APIMatic): Discusses leveraging GenAI for API adoption, emphasizing the need for accuracy in code generation. Distinguishes between static (access code - better generated deterministically) and dynamic (learning, use cases - suitable for AI) parts of SDK consumption. Demos APIMatic's API Co-pilot generating reliable SDK code and usage examples by combining deterministic generation with AI.
00:50:55 Community Demo (Nezar, Launchable): Presents two services:
Test Failure Analysis: Automates debugging and fixing CI/CD test failures, handling complex scenarios like microservices and flaky tests.
AI Code Review: Provides automated code reviews (free for startups), summarizing PRs, finding issues (security, logic, potential errors like division-by-zero), and enabling Q&A about the code.
00:57:41 Community Demo (Zee, Superface AI): Introduces Superface as an "agentic tooling platform." Proposes using specialized mini-agents for tool use. Demos an agent that can browse the web for API documentation (e.g., Todoist), automatically create a tool/skill for it, test it, and make it available via an LSP server for clients like Claude Desktop.