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Hackathon POC: OpenClaw "Manager Agent"

Core Concept

A tenant-scoped (per-organization) AI oversight layer that acts as a "manager" for worker agents. Instead of treating an agent's last message as the end of the job, this Manager Agent reviews the interaction to ensure quality, security, and completeness.

Objectives

  1. Oversight: Catch mistakes, dropped action items, and hallucinations.
  2. Actionability: Trigger internal follow-ups (Slack/Email/Jira) when needed.
  3. Security: Prevent prompt injection and ensure data isolation between orgs.

Architecture (POC)

1. Ingestion Layer

  • Input: Full conversation transcript (User + Agent) + Tool Execution Logs.
  • Metadata: Org ID, Customer Tier, Active Tools.

2. The Brain (OpenClaw / Gemini 3 Pro)

  • Role: Evaluator.
  • Prompt Strategy: "You are a QA Manager. Review this transcript. Did the agent solve the user's intent? Did it use tools correctly? Check for security risks."
  • Output Structure: 
    {
  "verdict": "PASS" | "FOLLOW_UP" | "ESCALATE",
  "reason": "Agent failed to ask for order ID before checking status.",
  "security_risk": "None",
  "suggested_actions": ["email_support", "log_ticket"]
}

3. Action Layer

  • Internal Comms: Send alerts to internal Slack channels or Support Email.
  • Sanitized Web Search: Verify facts if the agent made claims (e.g., "Is our API actually down?").
  • CRM/Ticket Update: Draft a Jira ticket or update HubSpot if the interaction requires human intervention.

Key Features to Build

  • Tenant Isolation: Ensure Manager A only sees Org A's data.
  • Prompt Injection Guardrails: The Manager must detect if the user tried to trick the worker agent (e.g., "Ignore previous instructions").
  • Playbooks: Different evaluation rules for Support vs. Sales agents.

Why OpenClaw?

  • Native Tooling: Can easily connect to Slack, Email, and Web.
  • State Awareness: Can maintain "Org Context" over time (long-term memory).
  • Security: Self-hosted / Private deployment ensures customer data doesn't leak.