AI Agents That Finish the Job Safely
AI Agent Engineering: leave with an agent with scoped tools, checkpoints, human approval, termination rules and step budgets.
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Learn this topic with a mentor
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- Bring your own work — code, campaign, report or plan
- Mentor matched to this topic
- Time, format and fee confirmed by email first
What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why ai agent engineering matters — the common problem: Agents loop forever, take unapproved actions or fail without recovery.
- 2Core concepts in plain language: Agent loops, tool selection, planning, execution, state, memory
- 3Going further: checkpoints, task decomposition, human approval, termination conditions, recovery, execution budgets
- 4Framework walkthrough: Workflow-vs-Agent Decision, Human-in-the-Loop Approval, Model Context Protocol
- 5Practical workflow, built live: An agent with scoped tools, checkpoints, human approval, termination rules and step budgets.
- 6How to measure it: Task success rate, Tool-call correctness, Human-approval rate, Cost per completed task
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- An agent with scoped tools, checkpoints, human approval, termination rules and step budgets.
- A working understanding of Workflow-vs-Agent Decision and Human-in-the-Loop Approval
- A short list of measures to track: Task success rate, Tool-call correctness, Human-approval rate
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