Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

• Defining autonomous agents: core concepts and classification
• The agent cycle: the loop of perceiving, deciding, acting, and observing
• Establishing design patterns for agent scope and responsibilities

Python Ecosystem and Agent SDKs

• Leveraging LangChain and comparable SDKs to initialize agents
• Managing async programming, task queues, and subprocesses
• Ensuring reproducible workflows through packaging and virtual environments

Connecting External Tools and APIs

• Crafting tool interfaces and secure invocation patterns
• Linking agents to web APIs, databases, and internal services
• Handling credentials, secrets, and implementing least-privilege access

Memory, State, and Context Handling

• Optimizing short-term context windows and prompt engineering methods
• Structuring long-term memory using Redis, vector stores, and retrieval augmentation
• Maintaining consistency, implementing caching strategies, and ensuring memory hygiene

Orchestration, Planning, and Complex Workflows

• Managing action chains, sub-agents, and task breakdown
• Comparing planning algorithms with heuristic orchestration
• Managing errors, retries, and corrective actions

Safety, Testing, and Observability

• Establishing threat models, red-teaming, and sanitizing inputs/outputs
• Conducting unit, integration, and end-to-end tests for agents
• Implementing logging, metrics, tracing, and alerts for agent performance

Deployment, Scalability, and Agent MLOps

• Containerizing applications and managing CI/CD pipelines and rollouts
• Controlling costs, applying rate limits, and optimizing resources
• Implementing monitoring, governance, and operational playbooks

Wrap-Up and Future Directions