Skip to main content
Live. This area is documented as current, user-reliable behavior.

​
Goal

Set accurate expectations for the AI features before you rely on them.

​
Prerequisites

  • Familiarity with the StackShift AI agents

​
Workflow

1
Use agents for the tasks listed as fully live below.
2
Account for the current limitations before depending on an agent for production work.
3
Keep human operational judgment and direct logs in the loop.

​
What is fully live

  • The deploy, database, debug, ops, and WordPress agents.
  • Automatic build-failure diagnosis (pattern matching for everyone; LLM analysis on paid plans).
  • The propose-then-confirm action model across every agent.

​
Current limitations

  • LLM-backed build diagnosis requires an active paid plan.
  • Agents propose actions for you to confirm; they do not act autonomously except when you explicitly ask the deploy agent to run a project creation all the way through.
  • Agents will not touch environment files, secrets, GitHub workflow files, or credentials.

​
Operator-first by design

These limits are deliberate. The agents are built to accelerate real operational work while keeping a human in the loop for anything that changes infrastructure. When a diagnosis or proposal disagrees with the raw evidence, trust the evidence.

​
Expected result

You rely on the AI features with accurate expectations and know where the guardrails are.

StackShift AI agents

StackShift runs specialized agents that can create projects, fix failed builds, manage databases, triage incidents, and operate WordPress — each one proposing a confirmable action before anything changes.

AI build diagnosis

Automatic root-cause analysis for failed builds: a known-pattern matcher backed by Claude LLM analysis produces a categorized diagnosis with confidence and concrete fix steps.