Code with AI Agents and Still Own the Quality
AI-Assisted Software Engineering: leave with a spec, agent-implemented feature, generated-and-reviewed tests and a human verification log.
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- Bring your own work — code, campaign, report or plan
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why ai-assisted software engineering matters — the common problem: AI-generated code gets merged unverified, introducing subtle bugs and security holes.
- 2Core concepts in plain language: Repository understanding, specification-driven implementation, coding assistants, coding agents, context preparation, automated test generation
- 3Going further: debugging assistance, code-review assistance, documentation generation, sandboxed execution, human verification
- 4Framework walkthrough: Workflow-vs-Agent Decision, Human-in-the-Loop Approval, Model Context Protocol
- 5Practical workflow, built live: A spec, agent-implemented feature, generated-and-reviewed tests and a human verification log.
- 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
- A spec, agent-implemented feature, generated-and-reviewed tests and a human verification log.
- 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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