Construyo mi propio arnés de IA… y te enseño como hacer el tuyo

Construyendo un Arnés de Inteligencia Artificial desde Cero

Introducción al Concepto de Arnés

• El video se centra en la construcción de un arnés completo para modelos de inteligencia artificial, transformando un chatbot en un agente capaz de razonar y utilizar herramientas.

• Se proporcionará un repositorio extensible como tutorial para que los espectadores experimenten y construyan su propia inteligencia artificial.

Contexto y Estructura del Arnés

• Este es el tercer video sobre Harness Engineering, donde se discuten las capas conceptuales del arnés, comparándolas con las capas de una cebolla.

• La primera capa es el núcleo que ejecuta llamadas a LLM (Large Language Model), actuando como el "cerebro" del sistema.

Bucle del Agente

• Se introduce el concepto del "bucle del agente", similar al bucle de juego en programación, donde se procesan entradas y actualizaciones.

• En cada ciclo, primero se lee la entrada del usuario, luego se actualiza el estado basado en esa entrada.

Implementación Práctica

• Al implementar un agente, se sigue un proceso RPL (Read, Evaluate, Print, Loop), donde cada fase tiene pasos específicos que deben ser seguidos.

• La fase de evaluación puede incluir múltiples pasos dependiendo de la complejidad de la entrada recibida.

Herramientas y Ejecución

• Dentro del bucle interno durante la evaluación, pueden ocurrir múltiples acciones como leer archivos o ejecutar herramientas específicas según lo requiera el modelo.

• Las herramientas son definidas por el arnés; la IA solicita su uso pero no las ejecuta directamente.

Seguridad en la Ejecución

• Se discute cómo implementar sistemas de permisos para evitar que el agente ejecute acciones sin supervisión.

• Un ejemplo incluye solicitar confirmación al usuario antes de ejecutar cualquier herramienta para garantizar seguridad.

Understanding Polymorphism in Project Development

Utilizing Polymorphism for Project Expansion

• The speaker discusses how polymorphism enables the creation of a project with tools and providers, allowing for extensive capacity to expand, experiment, and develop a custom harness.

• Modern harnesses like Cloud Code and Codex possess advanced capabilities such as generating subagents, enhancing the development process significantly compared to basic loops calling an SDK.

Creating Agents and Subagents

• The ability to create agents is highlighted; each agent represents a specific instance of a provider, similar to creating different tools on demand.

• A constructor can be defined that returns a set of providers, tools, and system prompts while enabling message handling within agents.

Internal Loops and Agent Configuration

• The internal loop is packaged into a class that allows instantiation of various internal loops akin to characters in a video game, providing control over agent configuration.

• Multiple agents can coexist within the same harness using different providers (e.g., OpenAI vs. Antropic), with access restrictions based on defined characteristics.

Defining Agent Capabilities

Access Control for Agents

• Specific capabilities can be assigned to agents; for example, limiting an agent's ability to read files or restricting another agent solely to Git-related tasks.

Infinite Potential in Harness Development

• The speaker emphasizes the limitless potential when defining classes of agents at such a low level in harness development.

Root Agent Creation

Instantiating the Root Agent

• The root agent is introduced as the primary entity receiving the hardcoded system prompt during initialization; it serves as the foundational layer for further operations.

Subagent Generation Mechanism

• Questions arise regarding how subagents are generated upon request; mechanisms are discussed that allow delegation of tasks from one agent to another.

Delegation and Subagent Functionality

Implementing Subagent Actions

• An action mechanism is established where agents can execute commands or delegate tasks by instantiating subagents based on specific requests.

Tool Utilization for Subagent Instantiation

• A dedicated tool called "delegate tool" is responsible for creating subagents with contextual descriptions provided during their definition.

Context Management in Agents

Contextual Independence of Subagents

• New subagents may start with an empty context rather than inheriting messages from their parent agent unless explicitly configured otherwise.

Implications of Switching Harnesses

• Transitioning between different harnesses has significant implications due to varying decision-making processes involved in implementation.

Enhancing User Interface Features

Basic UI Functionality

• Current UI features include command visibility and status bars but have room for enhancements like support for MCP (Multi-Code Providers).

Implementing MCP Support

• Tools are created specifically for registering MCP remotely by loading configurations from JSON files.

• Parallel loading techniques are employed to prevent blocking during HTTP requests while maintaining UI responsiveness.

Conversation Compression Strategies

Compacting Conversations

• Different strategies are implemented for conversation compression including sliding window methods and summarization via LLM (Language Model).

• Users can easily implement new strategies by modifying existing code structures related to compacting conversations.

Cost Tracking Mechanisms

• Systems track token usage per interaction with LLM providers, allowing users insight into costs associated with API calls based on model configurations.

Memory Systems Implementation

Developing Memory Tools

• Two new tools—recall (to remember things from memory), and remember (to save things)—are introduced as part of memory management systems within the harness framework.

Customizable Memory Storage Solutions

• Users have flexibility in implementing memory storage solutions ranging from JSON file systems to SQL databases depending on their needs.

Interaction with Memory Systems

• Functions are defined within memory stores allowing users customizable interactions through recall queries based on tags or keywords.

Building Complex Harnesses

Modular Design Approach

• Emphasizes modularity in building complex systems where components like memory systems or tools can be added or removed without disrupting overall functionality.

Visualizing System Operations

• Introduction of visual aids such as Babel T helps simplify terminal tool construction while offering flexibility across various platforms including web interfaces or command lines.

Educational Resources Available

Repository Overview

• A public repository will provide comprehensive documentation covering all aspects discussed throughout this presentation along with additional resources aimed at helping users build their own agents effectively.

Step-by-Step Tutorials

• Detailed tutorials guide users through constructing their own projects step-by-step while explaining key concepts behind each component involved in building effective harnesses.

Conclusion: Invitation To Explore Further

Encouragement To Engage With Content

• Viewers are encouraged to explore available resources actively engage with content shared throughout this series focusing on practical applications within AI development contexts.