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Duration 21 hours
Course Outline
Core Concepts of Agentic AI
- Defining autonomous agents: terminology and classification
- The agent loop: the perceive, decide, act, and observe cycle
- Design patterns for defining agent responsibilities and scope
Python Ecosystem and Agent Frameworks
- Leveraging LangChain and equivalent SDKs to initialize agents
- Asynchronous programming, task queuing, and subprocess control
- Package management, virtual environments, and reproducible dev workflows
Connecting External Tools and APIs
- Creating tool interfaces and safe invocation methods
- Linking with web APIs, databases, and internal services
- Handling credentials, secrets, and principle of least privilege
Memory, State, and Context Handling
- Managing short-term context windows and prompt engineering strategies
- Long-term memory structures: Redis, vector stores, and retrieval augmentation
- Maintaining consistency, caching tactics, and memory integrity
Orchestration, Planning, and Multi-Step Processes
- Sequencing actions, sub-agents, and task decomposition
- Comparing planning algorithms with heuristic orchestration
- Managing failures, retries, and compensatory actions
Security, Testing, and Observability
- Threat modeling, red-teaming, and input/output validation
- Unit, integration, and end-to-end testing for agent behavior
- Logging, metrics, tracing, and alerting mechanisms
Deployment, Scaling, and Agent MLOps
- Containerization, CI/CD pipelines, and release strategies
- Cost management, rate limiting, and resource efficiency
- Monitoring, governance, and operational runbooks
Recap and Future Directions
Requirements
- Proficiency in Python programming
- Practical experience with REST APIs and asynchronous I/O
- Knowledge of machine learning principles and pre-trained LLMs
Target Audience
- ML Engineers
- AI Developers
- Software Engineers
