# Welcome to the Agent Project

A practical guide written by practitioners to help get your Agents running, scaling, and operating in production.

Given the many confusing options, https://www.AgentProject.ai aims to guide you through the choices, tools, and best practices to ensure your Agent project starts and runs in production.

The github repo is here : https://github.com/AgentProject-AI/agentproject

# Topics covered here

(Please note that these are work-in-progress topics and will be filled out as we get experienced folks helping us out)

# Part 1: Foundations of Agent Projects

  • Introduction to Agent AI:
  • Core Challenges in Agent Projects:
    • Reliability: Managing unpredictable outputs from AI agents and their implications on system design.
    • Orchestrating: Multiple agent orchestration to achieve complex goals
    • Discovery: How to publish your Agent and make it findable
    • Trust: How to trust an Agent across your organization and from the outside
    • Real-Time and near real-time Processing Demands: Designing agents for low-latency execution and high throughput applications
    • Data Handling at Scale: Efficient processing of large datasets and external knowledge sources
    • Testing Complexity: Adapting testing methodologies for non-deterministic Agentic systems.
    • Agent Observability: Addressing the complexities of evaluating AI agent performance.

# Part 2: Building Your Agent Project

  • Choosing the Right Agentic Framework:
  • Selecting the appropriate framework based on project requirements.
  • Designing the Agent Architecture:
    • Defining agent roles and responsibilities.
    • Designing conversation flows and task allocation strategies.
    • Implementing tools and integrating external data sources.
    • Strategies for creating modular, reusable agent components.
  • Implementing Key Agent Capabilities:
    • Tool Use: Integrating web browsers, search engines, and APIs.
    • Memory Management: Storing and retrieving information across interactions.
    • Planning and Reasoning: Implementing strategies for complex tasks.
    • Context Management: Handling long-form content and maintaining context.
  • Prompt Engineering for Agents:
    • Creating effective prompts for different agent tasks.
    • Techniques for improving prompt reliability and consistency.
    • One-shot prompts.
    • Managing prompt complexity and versioning.
  • Agentic Retrieval Augmented Generation (RAG)
    • How RAG enhances agent performance by incorporating external knowledge.
    • Context selection and indexing strategies.
    • Vector stores and chunking methods.
    • Implementing Reasoning RAG to rectify information.

# Part 3: Your Agent Project in Production

  • Deployment Strategies:
    • Considerations for deploying agent applications.
    • Containerization and orchestration.
    • API endpoints for accessing agent services.
    • Implementing caching strategies to optimize performance.
  • Security Strategies:
    • Resource Access Delegation
    • Controlled access to computing resources
    • Token-based delegation for API and service access
    • Memory and storage allocation permissions
    • Network access controls and limitations
    • Controlled sub-task delegation between agents
    • Permission inheritance rules
    • Chain of authority tracking
  • Monitoring and Logging:
    • Importance of monitoring and audit logs in AI systems.
    • Setting up logging and metrics for performance tracking.
    • Using tools like LangTrace, OpenLit and Portkey.
    • Collecting data for evaluation and system improvement.
  • Evaluation and Testing:
    • Building robust evaluation frameworks.
    • Goal-based testing for agent projects.
    • AUTs: Profile-based Agent-unit-testing
    • Using automated testing and metrics.
    • Incorporating human feedback in the evaluation loop.
    • Ad-hoc and offline evaluation methods.
  • Ensuring Reliability and Safety:
    • Addressing common safety issues in agent behavior.
    • Implementing content filtering, input validation, and output sanitization.
    • Using safety guards and monitoring alerts.
    • Best practices for building reliable and safe agent systems.
  • Cost Optimization:
    • Understanding LLM costs and token optimization.
    • Implementing caching strategies and other cost-saving measures.
    • Choosing cost-effective models and deployment options.
  • Iterative Improvement:
    • The importance of continuous monitoring and improvement of agent applications.
    • Using data and feedback to refine and optimize agent behavior.
    • Integrating evaluation into the development cycle.

# Part 4: Advanced Topics and Future Directions

  • Advanced Agent Architectures:
    • Exploring multi-agent systems and complex interaction patterns.
    • Implementing adaptive and self-improving agent systems.
    • Techniques for building more robust and resilient agents.
  • The Role of Human-in-the-Loop Systems:
    • Integrating human feedback and oversight into agent workflows.
    • Designing effective human-machine collaboration patterns.
    • Balancing automation with human control.
  • The Future of Agent AI:
    • Emerging trends and technologies in agent AI.
    • The potential impact of AI agents on society and the economy.
    • Ethical considerations and responsible development of AI agents.