#
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:.
- 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.