Agentic AI Engineering with Python — Build Autonomous Agents Training Course
This course provides practical engineering techniques for designing, building, testing, and deploying agentic (autonomous) systems using Python. It explores the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and considerations for production deployment.
Delivered as an instructor-led live training (available online or onsite), this program is designed for intermediate to advanced ML engineers, AI developers, and software engineers aiming to construct robust, production-ready autonomous agents with Python.
Upon completion of this training, participants will be able to:
- Design and implement the agent loop and decision-making workflows.
- Integrate external tools and APIs to expand agent capabilities.
- Implement short-term and long-term memory architectures for agents.
- Coordinate multi-step orchestrations and agent composability.
- Apply best practices for safety, access control, and observability in deployed agents.
Course Format
- Interactive lectures and discussions.
- Hands-on labs for building agents using Python and popular SDKs.
- Project-based exercises resulting in deployable prototypes.
Course Customization Options
- To request 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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