Agentic AI Engineering with Python — Build Autonomous Agents Training Course
This course teaches practical engineering techniques to design, build, test, and deploy agentic (autonomous) systems using Python. It covers the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production considerations.
This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level ML engineers, AI developers, and software engineers who wish to build robust, production-ready autonomous agents using Python.
By the end of this training, participants will be able to:
- Design and implement the agent loop and decision-making workflows.
- Integrate external tools and APIs to extend agent capabilities.
- Implement short-term and long-term memory architectures for agents.
- Coordinate multi-step orchestrations and agent composability.
- Apply safety, access control, and observability best practices for deployed agents.
Course Format
- Interactive lectures and discussions.
- Hands-on labs building agents with Python and popular SDKs.
- Project-based exercises that produce deployable prototypes.
Course Customisation Options
- To request a customised training session for this course, please contact us to arrange.
Course Outline
Fundamentals of Agentic AI
- What is an autonomous agent: definitions and taxonomy
- The agent loop: perceive, decide, act, observe cycle
- Design patterns for agent responsibilities and scope
Python Tooling and Agent SDKs
- Using LangChain and similar SDKs to bootstrap agents
- Asynchronous programming, task queues, and subprocess management
- Packaging, virtual environments, and reproducible development workflows
Integrating External Tools and APIs
- Designing tool interfaces and safe tool 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, retrieval augmentation
- Consistency, caching strategies, and memory hygiene
Orchestration, Planning, and Multi-Step Workflows
- Chaining actions, sub-agents, and task decomposition
- Planning algorithms versus heuristic orchestration
- Handling failures, retries, and compensating actions
Safety, Testing, and Observability
- Threat models, 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
Requirements
- A working knowledge of Python programming
- Experience with REST APIs and asynchronous I/O
- Familiarity with machine learning concepts and pre-trained LLMs
Audience
- ML engineers
- AI developers
- Software engineers
Open Training Courses require 5+ participants.
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