ScopeGuard AI: Task Scoping and Execution Guardrails for AI Coding Agents

For small SaaS work, I care a lot about predictable AI task cost

For small SaaS work, I care a lot about predictable AI task cost

For small SaaS fixes, I would set the budget before the agent starts, not after it has already found more things to inspect. A pattern that works well is to classify the task first: - tiny: one file or one component, no schema/API contract change - small: a narrow flow, a few related files, targeted tests - risky: auth, billing, permissions, migrations, shared abstractions, data deletion, security, or anything customer-visible at scale Then give the agent a matching execution contract. For a tiny task, something like: - read only the named files plus direct imports if needed - summarize the suspected change before editing - make the smallest patch - run one targeted test/typecheck/lint command - stop and ask if the scope expands beyond N files or touches shared code The important part is that scope expansion should be an explicit state, not something the agent does silently. If it discovers risk, it can say: "this no longer fits the tiny-task budget because it touches auth middleware" or "there are two call sites I need to inspect before this is safe." Then you decide whether to upgrade the task. I also like using a fixed output budget for review tasks: require findings first, with file/line references, and forbid broad refactors unless a finding requires them. That keeps review attention predictable. For daily SaaS maintenance, I would optimize for bounded loops: narrow objective, allowed files, max changed files, exact verification command, and stop conditions. Deeper exploration is worth it, but it should be a deliberate mode switch, not the default behavior for every small change.