Agentic AI Engineering with Python — Build Autonomous Agents Training Course
This curriculum provides practical engineering methodologies for the design, development, testing, and deployment of agentic (autonomous) systems utilizing Python. It addresses the agent execution cycle, tool integration, state management, orchestration patterns, safety controls, and production environment requirements for government applications.
This instructor-led, live training session (available online or onsite) is tailored for intermediate-to-advanced machine learning engineers, AI developers, and software professionals seeking to construct robust, production-grade autonomous agents using Python.
Upon completion of this training, participants will be equipped to:
- Architect and implement agent execution loops and decision-making workflows.
- Integrate external tools and APIs to expand agent functional capabilities.
- Deploy short-term and long-term memory architectures for agent systems.
- Coordinate multi-step orchestration and agent composability.
- Apply safety protocols, access control measures, and observability standards for deployed agents.
Instructional Format
- Interactive lectures and facilitated discussions.
- Practical laboratory exercises involving the construction of agents with Python and industry-standard SDKs.
- Project-based assignments resulting in deployable prototypes.
Customization Options for Government
- To arrange a customized training program aligned with specific agency requirements for this course, please contact our team.
Course Outline
Core Principles of Agentic AI Systems
- Defining autonomous agents: operational definitions and classification frameworks
- The agent execution cycle: sensing, deliberation, execution, and feedback loops
- Establishing boundaries for agent responsibilities and operational scope
Python Development Ecosystems and Agent Frameworks
- Leveraging LangChain and equivalent SDKs to initialize agent architectures
- Implementing asynchronous processing, task queuing, and subprocess control
- Managing software packaging, virtual environments, and consistent development standards
Integration of External Tools and Services
- Architecting secure tool interfaces and safe invocation methodologies
- Linking to web services, data repositories, and internal government systems
- Enforcing credential management, secret protection, and least-privilege access models
State, Memory, and Contextual Governance
- Optimizing short-term context windows and prompt construction techniques
- Designing persistent memory structures: Redis, vector databases, and retrieval strategies
- Maintaining data consistency, caching efficiency, and memory integrity
Workflow Orchestration, Planning, and Multi-Step Execution
- Sequencing actions, coordinating sub-agents, and decomposing complex tasks
- Evaluating algorithmic planning versus heuristic-based orchestration methods
- Mitigating failures, implementing retry logic, and executing compensating transactions
Security Assurance, Testing, and Observability
- Developing threat models, conducting red-team exercises, and sanitizing inputs and outputs
- Executing unit, integration, and end-to-end validation for agent systems
- Establishing logging, metrics collection, tracing, and alerting for behavioral oversight
Deployment Strategies, Scalability, and Agent MLOps
- Implementing containerization, continuous integration/deployment pipelines, and release controls
- Optimizing cost structures, applying rate limits, and maximizing resource efficiency
- Establishing monitoring protocols, governance frameworks, and operational response plans
Summary and Strategic Next Steps
Requirements
- Proficiency in Python programming
- Experience with REST APIs and asynchronous input/output handling
- Knowledge of machine learning concepts and pretrained large language models
Target Audience
- Machine learning engineers
- AI developers
- Software engineers
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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