Training & Optimization
Zild Coach enables continuous improvement of both AI agents and human operators. Insights generated from evaluations can be used to refine:- Agent instructions
- Knowledge base content
- Escalation rules
- Workflow triggers
- Human training programs
AI Optimization Workflow
A typical optimization cycle:- Deploy Agent
- Monitor conversations
- Evaluate performance
- Identify failure patterns
- Adjust instructions or knowledge
- Re-test
- Measure improvement
Identifying AI Gaps
Common optimization triggers:- High escalation rate
- Low automation rate
- Frequent tool failure
- Repeated misunderstandings
- Incomplete data capture
Human Agent Training
When conversations escalate, Zild Coach evaluates:- Response clarity
- Policy adherence
- Resolution completeness
- Response time
- Professional tone
- Supervisor feedback sessions
- Targeted coaching
- Skill-based training
- Performance benchmarking
Feedback Loop Integration
Zild Coach supports structured feedback loops: Evaluation → Insight → Configuration Update → Performance Improvement This ensures AI and human teams evolve together.Best Practices
- Review evaluation reports weekly
- Track automation trends
- Refine instructions incrementally
- Align scoring criteria with business goals
- Separate AI scoring from human scoring when necessary