Automations, CI & Plugins

From IDE tools to production automation

MCP in your IDE is the starting point. The next step: AI automations that run without you — CI checks, scheduled audits, webhook-triggered workflows. The engineering challenge shifts from "can the model do it?" to "can it do it safely unattended?"

Automation patterns

Pattern	Trigger	Example
CI gate	PR opened/updated	AI code review (Bugbot), security scan summary
Scheduled	Cron	Weekly dependency audit, stale doc detection
Event-driven	Webhook (deploy, incident)	Post-deploy smoke test analysis, incident triage
On-demand	Slack command / API	"Summarize this RFC," "Generate migration plan"
IDE-triggered	Developer action	Cursor Automations on save/commit

All patterns share: trigger → gather context (tools/RAG) → LLM action → output/approval gate.

Cursor Automations

Cursor supports scheduled and triggered agent workflows:

Trigger (schedule, git event, manual) → Agent with rules + MCP tools → Output (PR comment, Slack message, file update) → Optional: human approval before action

Design automations like cron jobs: explicit scope, timeout, error notification, and idempotent actions.

CI integration patterns

Pattern	Flow	Guardrail
PR review	Diff → LLM review → post comments	Comment only, never auto-merge
Doc freshness	Cron → detect code/doc drift → Slack flag	Read-only git access
Test failure triage	CI fail → logs + diff → root cause summary	Human decides fix
Dependency triage	Dependabot PR → impact assessment	Read-only codebase search

Guardrails for unattended AI

Automations run without human oversight. Required safeguards:

Guardrail	Implementation
Read-only default	Automation tools can't write to prod
Scope limits	Only access specified repos/directories
Output review	Post comments, don't merge/deploy
Cost caps	Max tokens/API calls per run
Timeout	Kill after N minutes
Audit log	Record every tool call and LLM output
Failure alerting	Notify on error, don't silently fail
Human escalation	"I'm not confident" → route to human

Custom pipeline loop

Outside Cursor: prompt → LLM → tool_calls → execute → feed results → repeat (max N). OpenAI and Anthropic SDKs support this natively. Engineering effort is in tool execution, auth, and error handling.

Observability for AI automations

You wouldn't deploy a service without metrics. Same for AI automations:

Metric	Why
Runs per day / success rate	Is it working?
Tokens consumed / cost per run	Budget control
Tool call frequency	Is the agent looping?
Human override rate	Are automations trusted?
Time to completion	Performance regression
Eval score over time	Quality regression on model upgrade

Log every run with: input, tools called, output, latency, cost, human action taken.

When NOT to automate

• Irreversible actions (prod deploys, data deletion, permission changes)
• High-stakes decisions (security incident classification with auto-response)
• Tasks requiring taste (API design, UX decisions)
• Tasks with < 80% AI accuracy on your eval set

Automate the research and draft steps. Keep human approval on action steps.

Interview framing

"I design AI automations with the same rigor as cron jobs: explicit triggers, read-only tools by default, cost caps, audit logging, and human approval gates for any write action. CI integration starts with review comments, not auto-merge."

Senior signal: Describe a specific automation with its guardrail stack. Explain why you chose read-only tools. Mention observability metrics you'd track.