Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

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

Python Ecosystem and Agent SDKs

• Leveraging LangChain and comparable SDKs to initiate agent development
• Asynchronous programming, task queue management, and subprocess handling
• Dependency management, virtual environments, and reproducible development pipelines

Connecting External Tools and APIs

• Structuring tool interfaces and ensuring secure invocation patterns
• Integration with web APIs, databases, and proprietary internal services
• Effective management of credentials, secrets, and least-privilege access

Managing Memory, State, and Context

• Optimizing short-term context windows and prompt engineering strategies
• Designing long-term memory systems: Redis, vector databases, and retrieval augmentation
• Maintaining consistency, implementing caching, and ensuring memory hygiene

Orchestration, Planning, and Complex Workflows

• Sequencing actions, utilizing subagents, and decomposing tasks
• Comparing planning algorithms with heuristic orchestration approaches
• Addressing failures, implementing retries, and executing compensating actions

Safety, Testing, and System Observability

• Developing threat models, conducting red-teaming, and sanitizing inputs/outputs
• Executing unit, integration, and end-to-end tests for agent reliability
• Implementing logging, metrics, tracing, and alerting for behavioral monitoring

Deployment, Scaling, and MLOps for Agents

• Containerization strategies, CI/CD pipeline integration, and deployment rollouts
• Managing costs, enforcing rate limits, and optimizing resource usage
• Establishing monitoring frameworks, governance structures, and operational guides

Conclusions and Future Directions