Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Agentic AI Fundamentals

• Defining autonomous agents: concepts and classification
• The agent loop: the cycle of perceiving, deciding, acting, and observing
• Design patterns for defining agent responsibilities and scope

Python Tools and Agent SDKs

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

External Tool and API Integration

• Crafting tool interfaces and secure invocation patterns
• Linking with web APIs, databases, and internal services
• Handling credentials, secrets, and least-privilege access controls

Memory, State, and Context Handling

• Short-term context windows and prompt engineering methods
• Long-term memory systems: Redis, vector stores, and retrieval augmentation
• Ensuring consistency, caching strategies, and memory hygiene

Orchestration, Planning, and Multi-Step Processes

• Chaining actions, utilizing subagents, and decomposing tasks
• Comparing planning algorithms with heuristic 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 frameworks for agents
• Logging, metrics, tracing, and alerting for agent behavior

Deployment, Scaling, and Agent MLOps

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

Conclusion and Next Steps