Group Coursework

1 - Set exercises - 100

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<ul><li>Foundations of AI-Assisted Development: The shift from coding to orchestration; AI coding assistants landscape (GitHub Copilot, Cursor, Claude Code); prompt engineering fundamentals; iterative refinement and debugging with AI.</li><li>Natural Language to Analytics: Translating business questions into analytical pipelines; conversational data exploration; automated report generation; text-to-SQL and text-to-visualisation systems.</li><li>AI Agent Architectures: Single-agent vs. multi-agent systems; ReAct patterns (Reasoning &#43; Acting); tool use and function calling; memory and context management; agent orchestration frameworks (LangChain, AutoGen, CrewAI).</li><li>Workflow Automation: Automated data ingestion and cleansing pipelines; scheduled analytical workflows; event-driven analytics; integration with enterprise systems (APIs, databases, cloud services).</li><li>Human-AI Collaboration: Effective delegation patterns; when to intervene vs. trust AI outputs; collaborative debugging; maintaining analytical judgment in AI-augmented workflows.</li><li>Validation and Governance: Testing AI-generated code; reproducibility in AI-assisted analysis; audit trails and explainability; managing hallucinations and errors; compliance considerations.</li><li>Advanced Applications: Autonomous data analysis agents; self-improving analytical pipelines; AI-driven hypothesis generation; real-time adaptive analytics.</li></ul>

Feedback is provided in a number of ways throughout your module. This includes feedback you receive during teaching from both peers and lecturers and that provided for formative assessment. Following an assessment, you will be given your marks and feedback in line with the assessment regulations and policy. More information on the timing and type of feedback that will be provided for each assessment will be available from the module leader. <a href='https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations'>https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations</a>

This module will support the development of all the graduate attributes. Your module leader will provide you with information about how your learning, teaching and assessment activities support your development of these attributes. The graduate attributes are: - Always learning - Engaged in the world - Technical and digital - Connected Professional - Creating Impact

In-Person - 

Directed Asynchronous - 

Self-directed - 

A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

AI Engineering: Building Applications with Foundation Models

AutoGen: Enabling Next-Gen LLM Applications. 

LangChain Documentation. 

Co-Intelligence: Living and Working with AI. 

DeepLearning.AI. [Online course materials]

AI Agentic Workflows. DeepLearning.AI. 

Claude Documentation: Tool Use and Agents. 

BBM161 - AI Orchestrated Workflows

BBM161-PRD3-PER-ALV1

The module is delivered through hands-on lab sessions where students work directly with AI coding assistants and agent frameworks to build increasingly sophisticated analytical workflows. Lectures introduce conceptual frameworks for understanding AI capabilities and limitations, while workshops provide structured practice in prompt engineering and workflow design. Given the rapidly evolving nature of AI tools, the module emphasizes transferable principles alongside practical tool proficiency.

Contact hours are designed to be flexible and are delivered using a variety of methods to help you learn in the most effective way. A proportion of the core content may be delivered online and additional support materials and content will also be made available through Moodle.

AI Orchestrated Workflows

Explain the capabilities and limitations of current AI coding assistants and autonomous agents.

Knowledge

Reflect on the importance of transparency, reproducibility, and governance in AI-augmented analytics.

Values and attitudes

Build automated pipelines that integrate data sources, analytical tools, and reporting systems.

Skills

Design and implement multi-step analytical workflows using agent orchestration frameworks.

Use AI coding assistants to rapidly develop, debug, and refine analytical code in Python, R, and SQL.

Apply prompt engineering techniques to elicit high-quality outputs from large language models.

Evaluate and validate AI-generated analyses for accuracy, reproducibility, and business relevance.

Articulate best practices for validating and governing AI-generated analytical outputs.

Demonstrate critical judgment in evaluating AI-generated outputs and knowing when human oversight is essential.

Describe and assess different agent architectures and their suitability for various analytical workflow challenges.