Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Core Concepts of Agentic AI

• Defining autonomous agents: key concepts and taxonomic classifications
• The agent loop: understanding the perceive, decide, act, and observe cycle
• Design patterns for defining agent responsibilities and operational scope

Python Tools and Agent SDKs

• Utilizing LangChain and comparable SDKs to rapidly scaffold agents
• Implementing async programming, task queues, and subprocess management
• Establishing packaging standards, virtual environments, and reproducible development pipelines

External Tool and API Integration

• Crafting secure tool interfaces and safe invocation patterns
• Linking agents to web APIs, databases, and internal service ecosystems
• Handling credentials, secrets, and enforcing least-privilege access controls

Memory, State, and Context Governance

• Managing short-term context windows and advanced prompt engineering techniques
• Building long-term memory architectures using Redis, vector stores, and retrieval augmentation
• Ensuring data consistency, optimizing caching strategies, and maintaining memory hygiene

Orchestration, Planning, and Multi-Step Coordination

• Executing action chains, managing subagents, and decomposing complex tasks
• Evaluating planning algorithms versus heuristic-based orchestration
• Managing failures, implementing retries, and executing compensating actions

Safety, Testing, and Observability Frameworks

• Applying threat models, red-teaming strategies, and input/output sanitization
• Conducting unit, integration, and end-to-end testing for agent robustness
• Implementing comprehensive logging, metrics, tracing, and alerting for agent behavior

Deployment, Scaling, and MLOps for Agents

• Implementing containerization, CI/CD pipelines, and strategic rollout plans
• Optimizing cost control, rate limiting, and resource efficiency
• Establishing monitoring systems, governance frameworks, and operational playbooks

Conclusion and Future Directions