Agent foundations and tools
Explain the agent loop and build validated, tested tools that a model can call.
Build AI agents that use tools, follow approval rules and recover from failures, then test them before they touch real systems. Proposed programme; academic approval and batch confirmation pending.
Build, test and deploy tool-using AI agents with human approval, injection defences and budget limits.
Proposed curriculum · academic approval pendingCore learning areas at a glance. Detailed units and teaching plans are in the private learning workspace.
Explain the agent loop and build validated, tested tools that a model can call.
Build multi-step agents that plan within limits and answer only from approved, cited sources.
Gate consequential actions behind human approval and defend against prompt injection.
Measure agent quality with a harness and control failures, retries and spend.
Deploy, evaluate and present a sandboxed agent with clear limitations.
Comfortable writing Python functions, classes and tests Calling REST APIs and handling JSON responses Basic Git and command-line use Access to an LLM API account approved by the institute, with spending limits set
Build, check and explain your work with trainer feedback. Certificate and assessment terms are confirmed by the institute.
Detailed syllabus and weekly plans are available to authorised staff and learners enrolled in this course.