# 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, [<mark style="background-color:yellow;">https://www.AgentProject.ai</mark>](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](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). The team is currently focused on building this out
[<mark style="background-color:yellow;">**[Top 10 AI Agent Security and Governance Controls (OWASP Style)</mark>](security/Top-10-AI-Agent-Security-and-Governance-Controls.md)** :thumbsup:

### Part 1: Foundations of Agent Projects

* **Introduction to Agent AI:**
  * [Key functional areas of an AI agent](1.-foundation-of-agent-projects/the-key-functional-areas-of-an-ai-agent.md)
  * [Different AI agent workflow implementation patterns](1.-foundation-of-agent-projects/different-agent-workflow-patterns.md)
    * What type of AI agents are right for you
  * [How to think about designing and blueprinting agents in your organization using ORI design](1.-foundation-of-agent-projects/how-to-use-outcome-role-and-interaction-ori-to-design-ai-agents-for-your-organization.md)
  * 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](topics/agent-framework-scorecard-explained.md) 
  * **Comparative analysis of AutoGen, CrewAI, LangGraph**:.
    * [Agent Framework Analysis of Microsoft Autogen](topics/which-ai-agent-framework-to-choose/evaluating-microsoft-autogen.md)
* 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
* [<mark style="background-color:yellow;">**[Top 10 AI Agent Security and Governance Controls (OWASP Style)</mark>](security/Top-10-AI-Agent-Security-and-Governance-Controls.md)** :thumbsup:
* **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.