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-level 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.

Format of the Course

Interactive lecture and discussion.
Hands-on labs building agents with Python and popular SDKs.
Project-based exercises that produce deployable prototypes.

Course Customization Options

To request a customized training for this course, please contact us to arrange.

This course is available as onsite live training in Netherlands or online live training.

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Course Outline

Fundamentals of Agentic AI

What is an autonomous agent: definitions and taxonomy
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
Async 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, subagents, and task decomposition
Planning algorithms vs heuristic orchestration
Handling failures, retries, and compensating actions

Safety, Testing, and Observability

Threat models, red-teaming, and input/output sanitization
Unit, integration, and end-to-end testing for agents
Logging, metrics, tracing, and alerting for agent behavior

Deployment, Scaling, and MLOps for Agents

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

Summary and Next Steps

Requirements

An understanding of Python programming
Experience with REST APIs and asynchronous I/O
Familiarity with machine learning concepts and pretrained LLMs

Audience

ML engineers
AI developers
Software engineers

21 Hours

Delivery Options

Private Group Training

Our identity is rooted in delivering exactly what our clients need.

Pre-course call with your trainer
Customisation of the learning experience to achieve your goals -

Bespoke outlines
Practical hands-on exercises containing data / scenarios recognisable to the learners

Training scheduled on a date of your choice
Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.