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:

  • Key functional areas of an AI agent

  • Different AI agent workflow implementation patterns

    • What type of AI agents are right for you

  • How to think about designing and blueprinting agents in your organization using ORI design

  • The transformative potential of AI agents in various industries.

  • Understanding the core challenges in building and deploying AI agents.

• 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:

  • A Scorecard for Agent Frameworks 👍
  • Comparative analysis of AutoGen, CrewAI, LangGraph:.

    • Agent Framework Analysis of Microsoft Autogen
• 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.