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, and observe cycle
• Design patterns for defining agent responsibilities and scope

Python Ecosystem and Agent SDKs

• Initializing agents using LangChain and comparable SDKs
• Asynchronous programming, task queuing, and subprocess control
• Packaging, virtual environments, and reproducible development setups

External Tool and API Integration

• Crafting tool interfaces and secure invocation methods
• Linking with web APIs, databases, and internal systems
• Handling credentials, secrets, and enforcing least-privilege access

Memory, State, and Context Handling

• Short-term context windows and prompt engineering methods
• Long-term memory systems: Redis, vector databases, and RAG
• Data consistency, caching strategies, and memory maintenance

Orchestration, Planning, and Complex Workflows

• Sequencing actions, managing subagents, and decomposing tasks
• Comparing planning algorithms with heuristic orchestration
• Managing errors, retries, and corrective actions

Safety, Testing, and Monitoring

• Threat modeling, red-teaming, and sanitizing inputs/outputs
• Unit, integration, and end-to-end testing for agent systems
• Logging, metrics, tracing, and alerting for agent actions

Deployment, Scaling, and MLOps for Agents

• Containerization, CI/CD pipelines, and release strategies
• Cost management, rate limiting, and resource efficiency
• Monitoring, governance, and operational runbooks

Wrap-up and Further Directions