Agentic AI Engineering with Python — Build Autonomous Agents Training Course
This course imparts practical engineering methodologies for designing, developing, testing, and deploying agentic (autonomous) systems via Python. Key topics include the agent loop, tool integrations, memory and state management, orchestration patterns, safety mechanisms, and production-grade considerations.
Designed as an instructor-led live training session (available online or on-site), this program targets intermediate to advanced ML engineers, AI developers, and software engineers seeking to create robust, production-ready autonomous agents using Python.
Upon completion of this training, participants will be capable of:
- Designing and implementing the agent loop and decision-making workflows.
- Integrating external tools and APIs to expand agent functionality.
- Implementing memory architectures that support both short-term and long-term retention.
- Coordinating complex orchestrations and enabling agent composability.
- Applying best practices for safety, access control, and observability in deployed agents.
Course Format
- Interactive lectures and discussions.
- Hands-on labs focused on building agents with Python and popular SDKs.
- Project-based exercises resulting 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
- Understanding autonomous agents: definitions and taxonomy.
- The agent loop: the perceive, decide, act, observe cycle.
- Design patterns for defining 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 establishing reproducible development workflows.
Integrating External Tools and APIs
- Designing tool interfaces and implementing safe invocation patterns.
- Connecting to web APIs, databases, and internal services.
- Managing credentials, secrets, and ensuring least-privilege access.
Memory, State, and Context Management
- Managing short-term context windows and employing prompt engineering techniques.
- Architecting long-term memory: Redis, vector stores, and retrieval augmentation.
- Ensuring consistency, optimizing caching strategies, and maintaining memory hygiene.
Orchestration, Planning, and Multi-Step Workflows
- Chaining actions, managing subagents, and decomposing tasks.
- Comparing planning algorithms with heuristic orchestration approaches.
- Handling failures, implementing retries, and executing compensating actions.
Safety, Testing, and Observability
- Developing threat models, conducting red-teaming, and sanitizing input/output.
- 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
- Utilizing containerization, CI/CD pipelines, and defining rollout strategies.
- Managing cost control, rate limiting, and resource optimization.
- Establishing monitoring, governance, and operational playbooks.
Summary and Next Steps
Requirements
- Familiarity with Python programming.
- Experience with REST APIs and asynchronous I/O.
- Understanding of machine learning concepts and pretrained LLMs.
Audience
- ML engineers.
- AI developers.
- Software engineers.
Open Training Courses require 5+ participants.
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