Harness and Loop Engineering for AI Agents

About This Course

This course equips participants with practical skills to design reliable harnesses and execution loops for AI agents. Learners will explore how harness engineering combines instructions, context, tools, memory, permissions, validation rules, and evaluation mechanisms to guide agent behaviour and improve the quality of task execution.

Participants will learn to develop structured agent loops that enable AI agents to observe inputs, analyse information, plan actions, use tools, evaluate results, learn from feedback, and retry when required. The course covers loop controls such as stopping conditions, error recovery, state management, human approval, output validation, and performance monitoring to prevent repetitive, unsafe, or unsuccessful agent behaviour.

A key focus is the application of text analytics and text processing within agent harnesses and loops. Learners will prepare, clean, classify, summarise, and extract information from unstructured text such as documents, customer feedback, emails, reports, and online content. They will apply techniques including tokenisation, text normalisation, sentiment analysis, entity extraction, topic identification, semantic search, and text classification to help agents interpret inputs and make informed decisions.

Through hands-on projects, participants will build AI agent workflows that process text, retrieve relevant context, perform multi-step analysis, validate generated outputs, and refine results through iterative feedback loops. Emphasis is placed on reliability, traceability, security, human oversight, and measurable evaluation.

By the end of the course, learners will be able to design and optimise harness and loop engineering solutions that enable AI agents to process text effectively, complete complex tasks consistently, and generate dependable insights for real-world business applications.