Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

• Defining autonomous agents: concepts and classification
• The agent loop: the cycle of perception, decision, action, and observation
• Architectural patterns for agent responsibilities and scope

Python Tools and Agent SDKs

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

Connecting External Tools and APIs

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

Managing Memory, State, and Context

• Short-term context windows and prompt engineering strategies
• Long-term memory structures: Redis, vector stores, and retrieval augmentation
• Ensuring consistency, caching approaches, and memory maintenance

Orchestration, Planning, and Complex Workflows

• Action chaining, subagent coordination, and task breakdown
• Planning algorithms versus heuristic-based orchestration
• Managing failures, retries, and compensatory actions

Safety, Testing, and Observability

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

Deployment, Scaling, and Agent MLOps

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

Overview and Future Directions