Agentic AI Engineering with Python — Build Autonomous Agents Training Course
This program delivers applied engineering methodologies for the design, construction, verification, and deployment of autonomous systems utilizing Python. The curriculum encompasses the agent cycle, tool connectivity, memory and state handling, orchestration frameworks, safety protocols, and operational requirements. Designed specifically for government applications, the material addresses the unique demands of public sector technology implementation.
This instructor-led session is available via online or on-site delivery. It targets intermediate to advanced machine learning engineers, artificial intelligence developers, and software engineers seeking to construct resilient, production-grade autonomous agents using Python.
Upon completion of this training, participants will demonstrate the ability to:
- Design and execute agent loops and decision-making workflows.
- Integrate external tools and application programming interfaces (APIs) to expand agent functionality.
- Construct short-term and long-term memory architectures for autonomous agents.
- Manage multi-step orchestrations and ensure agent composability.
- Apply established practices for safety, access control, and observability in deployed agent environments.
Course Format
- Interactive instruction and group discussion.
- Practical exercises focused on building agents with Python and widely used software development kits (SDKs).
- Project-based tasks resulting in deployable prototypes.
Customization Options
- To request tailored training for this curriculum, please contact the provider to arrange details.
Course Outline
Fundamentals of Agentic AI Architecture
- Defining autonomous agents: core concepts and classification frameworks for government systems.
- The agent operational cycle: perception, decision-making, execution, and observation protocols.
- Architectural design patterns defining agent scope and functional responsibilities.
Python Infrastructure and Agent Software Development Kits (SDKs)
- Leveraging LangChain and compatible SDKs to establish foundational agent capabilities.
- Implementing asynchronous programming, task queue management, and subprocess control.
- Establishing reproducible development environments through virtualization and standardized packaging for government use.
Integration of External Tools and Application Programming Interfaces (APIs)
- Developing secure tool interfaces and establishing protocols for safe tool invocation.
- Facilitating connectivity with public web APIs, relational databases, and internal federal services.
- Managing authentication credentials and enforcing least-privilege access controls.
Memory, State, and Context Management Strategies
- Optimizing short-term context windows and applying prompt engineering techniques for government applications.
- Implementing long-term memory architectures utilizing Redis, vector stores, and retrieval-augmented generation methods.
- Ensuring data consistency, caching efficiency, and rigorous memory hygiene standards.
Orchestration, Strategic Planning, and Multi-Step Workflow Execution
- Coordinating action chains, subagent interactions, and task decomposition techniques.
- Evaluating the application of formal planning algorithms versus heuristic orchestration methods.
- Managing operational resilience through failure handling, automated retries, and compensatory actions.
Safety Protocols, Testing Standards, and Observability Frameworks
- Conducting threat modeling, red-teaming exercises, and implementing input/output sanitization measures.
- Executing unit, integration, and end-to-end testing protocols for agent reliability.
- Establishing comprehensive logging, metric tracking, distributed tracing, and alerting systems for behavioral monitoring.
Deployment, Scalability, and Machine Learning Operations (MLOps) for Agents
- Implementing containerization strategies, continuous integration/continuous deployment (CI/CD) pipelines, and controlled rollout procedures.
- Optimizing resource allocation, managing costs, and enforcing rate limiting constraints.
- Maintaining operational governance through rigorous monitoring and standardized incident response playbooks.
Conclusion and Strategic Recommendations
Requirements
- Proficiency in Python programming
- Practical experience utilizing REST APIs and asynchronous I/O frameworks
- Knowledge of machine learning principles and pretrained large language models (LLMs)
Intended Audience
- Machine learning engineers
- Artificial intelligence developers
- Software engineering professionals
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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