Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Core Concepts of Agentic AI

• Defining autonomous agents: classifications and core definitions
• The agent loop: the perceive, decide, act, and observe cycle
• Structuring agent responsibilities and defining operational scope

Python Utilities and Agent Frameworks

• Leveraging LangChain and comparable SDKs to initialize agents
• Async programming, task queuing, and managing subprocesses
• Software packaging, virtual environments, and repeatable development cycles

Connecting to External Tools and APIs

• Crafting tool interfaces and secure invocation patterns
• Linking web APIs, databases, and internal service endpoints
• Handling credentials, secrets, and enforcing least-privilege access

Memory, State, and Context Handling

• Managing short-term context windows and advanced prompt engineering
• Long-term memory structures: Redis, vector databases, and retrieval augmentation
• Maintaining consistency, caching strategies, and memory hygiene

Orchestration, Planning, and Complex Workflows

• Linking actions, sub-agents, and breaking down tasks
• Comparing planning algorithms with heuristic-based orchestration
• Managing errors, retries, and executing compensating actions

Safety, Testing, and Monitoring

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

Deployment, Scaling, and MLOps for Agents

• Containerization, CI/CD pipelines, and deployment strategies
• Controlling costs, applying rate limits, and optimizing resources
• Overseeing monitoring, governance, and operational runbooks

Wrap-up and Future Directions