Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Fundamentals of Agentic AI

• Understanding autonomous agents: definitions and classification
• The agent loop: the cycle of perception, decision, action, and observation
• Design patterns for defining agent responsibilities and scope

Python Tooling and Agent SDKs

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

Integrating External Tools and APIs

• Creating tool interfaces and safe invocation patterns
• Connecting to web APIs, databases, and internal services
• Managing credentials, secrets, and least-privilege access

Memory, State, and Context Management

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

Orchestration, Planning, and Multi-Step Workflows

• Chaining actions, managing sub-agents, and task decomposition
• Comparing planning algorithms with 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 behaviour

Deployment, Scaling, and MLOps for Agents

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

Summary and Next Steps