Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

• Defining autonomous agents: concepts and classification
• The agent loop: the perceive, decide, act, observe cycle
• Design patterns for defining agent responsibilities and scope

Python Tooling and Agent SDKs

• Leveraging LangChain and comparable SDKs to initialize agents
• Asynchronous programming, task queues, and subprocess management
• Packaging, virtual environments, and reproducible development workflows

Integration of External Tools and APIs

• Designing tool interfaces and secure tool 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 strategies
• Long-term memory architectures: Redis, vector stores, and retrieval augmentation
• Maintaining consistency, caching strategies, and memory hygiene

Orchestration, Planning, and Multi-Step Workflows

• Chaining actions, managing subagents, and task decomposition
• Comparing planning algorithms with heuristic orchestration
• Handling failures, implementing retries, and defining compensating actions

Safety, Testing, and Observability

• Threat modeling, red-teaming, and input/output sanitization
• Unit, integration, and end-to-end testing methodologies for agents
• Logging, metrics, tracing, and alerting for monitoring agent behavior

Deployment, Scaling, and MLOps for Agents

• Containerization, CI/CD pipelines, and deployment strategies
• Cost management, rate limiting, and resource optimization
• Monitoring, governance, and operational playbooks

Overview and Subsequent Steps