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What Are AI Agents?

Agents are specialized AI assistants configured for specific purposes. Each agent has:
  • Specific role (Quality Assistant, Safety Expert, etc.)
  • System instructions (personality and behavior)
  • Available tools (what it can do)
  • Knowledge base (documents to reference)
  • Permitted data access (what it can see)

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Step-by-Step Agent Creation

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Step 1: Define Purpose

Question: What will this agent help with? Examples:
  • “Quality defect classification and root cause analysis”
  • “Safety incident response and escalation”
  • “Maintenance troubleshooting and scheduling”

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Step 2: Write System Prompt

Instructions for agent behavior: Example Quality Agent: 
You are a Quality Control Expert. Your job is to:
- Help classify quality defects
- Suggest root causes
- Recommend quality improvements
- Reference industry standards
- Escalate safety concerns
- Use data-driven decisions
Be professional, direct, and action-oriented.

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Step 3: Select LLM Provider

Choose model based on needs:
  • OpenRouter: General classification
  • Groq: Audio processing
  • OpenAI: Complex analysis

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Step 4: Configure Tools

Select what agent can access: Available tools:
  • Search claims with filters
  • Get full claim details
  • View analytics/KPIs
  • Check SLA status
  • Access knowledge base
  • Query team workload
  • Generate reports
Attach documents agent should reference:
  • Quality control procedures
  • Equipment manuals
  • Historical resolutions
  • Best practices
  • Training materials

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Step 6: Test Agent

Ask sample questions:
  • “What causes conveyor belt misalignment?”
  • “Show me quality issues from last week”
  • “What’s our SLA compliance rate?”
  • “How many critical claims unassigned?”
Verify:
  • Answers are accurate
  • Tone is appropriate
  • References are helpful
  • No hallucinations

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Agent Examples

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Quality Defect Assistant

Purpose: Help quality team System Prompt: “Focus on defects, root causes, prevention” Tools: Search claims, analytics, knowledge base Knowledge Base: Quality procedures, defect types, solutions Access: Quality department claims only

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Safety Incident Responder

Purpose: Support safety incidents System Prompt: “Treat as urgent, escalate appropriately, focus on prevention” Tools: Search claims, escalation, notifications Knowledge Base: Safety procedures, OSHA regulations, response playbooks Access: All claims (some sensitive)

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Maintenance Specialist

Purpose: Equipment troubleshooting System Prompt: “Provide step-by-step solutions, reference equipment manuals” Tools: Search claims, get details, view workload Knowledge Base: Equipment specs, maintenance schedules, past repairs Access: Maintenance claims + historical records

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Executive Summary Agent

Purpose: Quick metrics for leadership System Prompt: “Provide concise summaries, focus on trends and decisions” Tools: Full analytics access Knowledge Base: Company goals, KPI definitions Access: All data (aggregate level)

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Managing Agents

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Enable/Disable

  • Start with disabled
  • Enable after testing
  • Disable if not used
  • Can toggle anytime

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Monitor Usage

  • How often called
  • Average response time
  • User satisfaction
  • Common queries

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Update Agents

  • Refine system prompt based on feedback
  • Add new tools or knowledge
  • Change LLM provider if needed
  • Archive if unused

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Remove Agents

  • Delete when no longer needed
  • Archive for reference
  • Export interactions if needed

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Best Practices

✅ Do:
  • Start with clear, specific purpose
  • Use professional system prompt
  • Thoroughly test before deploying
  • Link to good knowledge base
  • Monitor usage and adjust
  • Gather user feedback
  • Update documentation regularly
❌ Don’t:
  • Create too many agents (causes confusion)
  • Make agents too generic
  • Deploy without testing
  • Forget to update knowledge base
  • Ignore user feedback
  • Leave unused agents enabled

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Next Steps