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Duration 21 hours
Course Outline
Fundamentals of Agentic AI
- Defining autonomous agents: key concepts and classifications
- The agent loop: the perceive, decide, act, and observe cycle
- Design patterns for defining agent responsibilities and scope
Python Tooling and Agent SDKs
- Bootstrapping agents using LangChain and comparable SDKs
- Asynchronous programming, task queues, and subprocess management
- Packaging, virtual environments, and reproducible development workflows
Integrating External Tools and APIs
- Crafting tool interfaces and secure invocation patterns
- Connecting to web APIs, databases, and internal services
- Managing credentials, secrets, and least-privilege access
Memory, State, and Context Management
- Short-term context windows and prompt engineering techniques
- Long-term memory architectures: Redis, vector stores, and retrieval augmentation
- Consistency, caching strategies, and memory hygiene
Orchestration, Planning, and Multi-Step Workflows
- Chaining actions, subagents, and task decomposition
- Planning algorithms versus heuristic orchestration
- Handling failures, retries, and compensating actions
Safety, Testing, and Observability
- Threat modeling, red-teaming, and input/output sanitization
- Unit, integration, and end-to-end testing for agents
- Logging, metrics, tracing, and alerting for agent behavior
Deployment, Scaling, and MLOps for Agents
- Containerization, CI/CD pipelines, and rollout strategies
- Cost control, rate limiting, and resource optimization
- Monitoring, governance, and operational playbooks
Summary and Next Steps
Requirements
- Solid proficiency in Python programming
- Practical experience with REST APIs and asynchronous I/O
- Working knowledge of machine learning concepts and pretrained LLMs
Target Audience
- ML Engineers
- AI Developers
- Software Engineers
