Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Leaving agents completely unattended on irreversible actions such as payment approval, customer communication, record deletion or contract dispatch creates severe operational risk. This programme covers human-in-the-loop (HITL) architectures that embed human oversight into the agent loop without creating bottlenecks.

Participants classify low- and high-risk actions, build a prototype using LangGraph interrupt/resume and asynchronous webhooks that pauses execution and resumes after human approval or edits, and prepare an approval matrix and checklist for internal use.

Who Should Attend?

  • Process and product owners responsible for agent initiatives
  • Software and AI engineers building enterprise workflows and agents
  • Risk, compliance and operations teams
  • Prerequisite: basic agent concepts; intermediate Python/API familiarity for labs

Key Takeaways

  • Classify tool calls by reversibility and business impact
  • Build asynchronous approval flows that persist state until a human responds
  • Handle approve, edit-arguments and reject/redirect scenarios cleanly
  • Design operator notifications that present concise evidence and context
  • Turn human corrections into evaluation datasets and few-shot examples

Curriculum

Autonomy Spectrum and Risk Classification
  • Read-only, drafting and state-mutating external actions
  • Dynamic approval thresholds by amount, confidence score and segment
Interrupt and Resume Architecture
  • Freezing state with checkpointers and managing timeouts
  • Collecting asynchronous approvals via Slack, Teams or internal portals
Intervention Patterns: Approve, Edit, Redirect
  • Executing tool calls with human-edited arguments
  • Feeding rejection feedback back to the agent for replanning
Preventing Approval Fatigue and Operator UX
  • Evidence summaries, diff views and one-click decision cards
  • SLA tracking, fallback escalation and audit logging
Learning Loops from Human Feedback
  • Capturing and analysing human edits and overrides
  • Improving prompts, rules and eval datasets from approval history

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