AI Supervisor: AI agents do the work. AI Supervisor makes sure the work gets done.

Description and use

AI Supervisor adds planning, approval, monitoring, recovery, and verification to AI agent workflows, eliminating the need for manual supervision.

Problem

AI coding agents can write code and complete tasks, but a completion message does not guarantee a working result. Agents may repeat failed approaches, lose context during handoffs, or report success when changes still fail verification, requiring developers to supervise every run manually.

Outcome

AI Supervisor transforms AI agent workflows by adding structured mission planning, human approval, real‑time monitoring, failure recovery, and verification, reducing manual supervision and improving reliability.

Workflow

• Understand the goal
• Plan tasks, dependencies, and acceptance criteria
• Approve the plan
• Execute tasks with selected agents
• Monitor task states and watch for stalls or failures
• Recover from failures by selecting new agents or retrying
• Verify results against acceptance criteria

Evidence

AI coding agents can write code and complete tasks, but a completion message doesn't guarantee a working result.

Source

AI Supervisor is a control plane for AI coding agents. You give it a mission, and it structures the work into tasks, requires human approval before execution, coordinates compatible agent runtimes, monitors progress, responds to failures, and verifies results against acceptance criteria.

Source

We also integrated FalkorDB as the graph memory layer. Rather than treating execution history as disconnected logs, we connect missions, tasks, failures, supervisory decisions, handoffs, and verification evidence.

Source

The workflow is built around a mission lifecycle: Understand → Plan → Approve → Execute → Monitor → Recover → Verify

Source