Welcome to the Agent Project

A practical guide written by practitioners to help get your Agents running securely 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 runs securely 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). The team is currently focused on building this out [Top 10 AI Agent Security and Governance Controls (OWASP Style) 👍

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: Securing Your Agent Project in Production

• [Top 10 AI Agent Security and Governance Controls (OWASP Style) 👍
• 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.