Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

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

Python Tools and Agent SDKs

• Initializing agents with LangChain and comparable SDKs
• Async programming, task queue management, and subprocess control
• Software packaging, virtual environments, and reproducible development cycles

Connecting External Tools and APIs

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

Managing Memory, State, and Context

• Short-term context windows and advanced prompt engineering
• Long-term memory solutions: Redis, vector databases, and retrieval augmentation
• Data consistency, caching tactics, and maintaining memory hygiene

Orchestration, Planning, and Multi-Step Processes

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

Security, Testing, and Observability

• Threat modeling, red-teaming, and data sanitization
• Unit, integration, and end-to-end testing strategies for agents
• Logging, metrics collection, tracing, and alerting on agent performance

Deployment, Scaling, and MLOps for Agents

• Containerization, CI/CD pipelines, and deployment strategies
• Managing costs, rate limits, and resource efficiency
• Monitoring, governance, and operational runbooks

Conclusion and Future Directions