Plan Derailment
This is the "over-eager assistant" pain point. You give an AI agent a very specific, narrow task—like "fix this one bug"—and it "helpfully" decides to also refactor the entire module, "clean up" the comments, and reformat three unrelated files while it's "in there." This autonomous "scope creep" is a nightmare for governance. Without strict workflows and guardrails that chain the AI to its original, explicit plan, you lose all predictability and control, making it impossible to know what the AI actually changed.
AI agents, especially in multi-agent systems, are often optimized to be "proactive" or "helpful." But this helpfulness isn't bound by human concepts like "a ticket's scope" or "atomic commits." The agent is given a specific task (the "plan") but may autonomously deviate from that plan if it identifies what it thinks is an improvement or a related "optimization." It goes "off-script," making changes far beyond its assigned scope without human consultation or approval.
This creates a massive tracking and validation problem, effectively introducing "shadow work" into the sprint. It becomes nearly impossible to track what the AI actually did, as the commit history and PR no longer match the planned task. This leads to unintended side effects and regressions in seemingly unrelated parts of the system. It also makes code reviews exponentially harder, as the reviewer has to first discover all the out-of-scope changes before they can even begin to validate them.
The "Helpful" Refactor
You ask an agent to "add a lastName field to the User model." The agent does this, but also decides to refactor the entire AuthService that uses the User model, changing 10 files instead of the 2 you expected.
The "Scope Creep" Bug Fix
The task is "fix a typo in the error message." The AI fixes the typo, but also "optimizes" the function that calls the error message, introducing a new, subtle performance bug.
Unrelated "Improvements"
An agent is tasked with updating dependencies in a package.json file. While doing so, it also "cleans up" the CI/CD YAML file in the same directory, breaking the build pipeline because its "improvement" was flawed.
Cross-System Side Effects
An agent is asked to update a setting in the "Billing" service. It follows a code path that leads it to a shared library and "corrects" a function there, which unintentionally breaks the "Shipping" service that also relied on that library's original behavior.
The problem isn't the AI; it's the lack of a human-in-the-loop verification and governance system. These workflows are the perfect antidote.
Cursor Obedience Kit
View workflow →The Pain Point It Solves
This workflow directly attacks the "over-eager assistant" problem by loading role-specific rules and instructions before each session, marking critical files as read-only, and requiring diff reviews after every plan step. Instead of allowing agents to autonomously deviate from the plan, this workflow chains the AI to its original, explicit scope.
Why It Works
It enforces plan adherence. By loading role-specific rules and instructions before starting each session, marking critical files as read-only or guarded in session configuration, and reviewing diffs after every plan step while pausing the agent before continuing, this workflow ensures that agents cannot autonomously deviate from the assigned scope. This prevents scope creep, unintended side effects, and makes it possible to track what the AI actually changed.
Task Decomposition Prompt Flow
View workflow →The Pain Point It Solves
This workflow addresses the "scope creep" problem by breaking complex tasks into smaller, self-contained prompts with explicit boundaries. Instead of giving agents large, open-ended tasks that invite deviation, this workflow forces atomic, well-defined subtasks that prevent autonomous scope expansion.
Why It Works
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Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.