Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Agentic AI Foundations

• Defining autonomous agents: concepts and categorization
• The agent loop: the perceive, decide, act, and observe cycle
• Design patterns for defining agent responsibilities and scope

Python Tools and Agent SDKs

• Utilising LangChain and comparable SDKs to initialise agents
• Asynchronous programming, task queues, and subprocess handling
• Packaging, virtual environments, and reproducible development processes

Incorporating External Tools and APIs

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

Memory, State, and Context Handling

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

Orchestration, Planning, and Multi-Step Processes

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

Safety, Testing, and Observability

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

Deployment, Scaling, and Agent MLOps

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

Recap and Future Directions