Agentic AI Engineering with Python — Build Autonomous Agents Training Course
This course imparts practical engineering methodologies for designing, constructing, testing, and deploying autonomous agentic systems using Python. It explores the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production-grade considerations.
Delivered as instructor-led live training (available online or onsite), this program targets intermediate to advanced ML engineers, AI developers, and software engineers aiming to build robust, production-ready autonomous agents using 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 enhance agent capabilities.
- Implement short-term and long-term memory architectures for agents.
- Coordinate multi-step orchestrations and ensure agent composability.
- Apply safety, access control, and observability best practices for 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 a customized training session for this course, please contact us to arrange it.
Course Outline
Fundamentals of Agentic AI
- Understanding autonomous agents: definitions and taxonomy.
- The agent loop: the perceive, decide, act, and observe cycle.
- Design patterns for agent responsibilities and scope.
Python Tooling and Agent SDKs
- Leveraging 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, and retrieval augmentation.
- Ensuring consistency, caching strategies, and memory hygiene.
Orchestration, Planning, and Multi-Step Workflows
- Chaining actions, subagents, and task decomposition.
- Comparing planning algorithms with heuristic orchestration.
- Handling failures, retries, and compensating actions.
Safety, Testing, and Observability
- Threat models, red-teaming, and input/output sanitization.
- Conducting unit, integration, and end-to-end testing for agents.
- Implementing 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
- 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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