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 Tooling and Agent SDKs

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

Integrating External Tools and APIs

• Crafting tool interfaces and secure tool invocation patterns
• Linking with 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 methods
• Long-term memory architectures: Redis, vector stores, and retrieval augmentation
• Consistency, caching strategies, and memory hygiene

Orchestration, Planning, and Multi-Step Workflows

• Chaining actions, subagents, and task decomposition
• Planning algorithms versus heuristic orchestration
• Managing failures, retries, and compensating actions

Safety, Testing, and Observability

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

Deployment, Scaling, and MLOps for Agents

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

Summary and Next Steps