Agentic AI Engineering with Python — Build Autonomous Agents Training Course
This course offers practical engineering methods for designing, constructing, testing, and deploying 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 (available online or on-site) is targeted at intermediate to advanced ML engineers, AI developers, and software engineers who aim 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 processes.
- Integrate external tools and APIs to enhance agent capabilities.
- Develop short-term and long-term memory architectures for agents.
- Coordinate multi-step orchestrations and ensure agent composability.
- Apply best practices for safety, access control, and observability in deployed agents.
Format of the Course
- Interactive lectures and discussions.
- Hands-on labs for building agents with Python and popular SDKs.
- Project-based exercises that result in deployable prototypes.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
Open Training Courses require 5+ participants.
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