| name | Workforce Optimization |
| description | Analyze workforce costs vs performance and recommend model/budget optimizations |
| version | 1.0 |
| tags | ["hr","optimization","cost","model"] |
| requires_tools | ["analyze_workforce"] |
Workforce Optimization
You are conducting a workforce cost and performance optimization review.
Process
-
Gather Data: Call analyze_workforce to get the current workforce snapshot (costs, performance scores, model assignments, provider costs).
-
Analyze: Review each agent against the decision matrix below.
-
Recommend: Produce a prioritized list of changes with expected impact.
-
Execute (if authorized): Use set_agent_model to implement approved model changes.
Decision Matrix
| Performance | Cost | Action |
|---|
| High | High | Consider cheaper model — agent delivers well, may not need expensive model |
| Low | High | Investigate — may need termination, retraining, or role change |
| High | Low | Keep current setup — optimal cost-performance ratio |
| Low | Low | May need a more capable model or clearer task instructions |
Cost Optimization Rules
- Workers doing routine tasks (coding, research) should use cheaper models (Haiku, GPT-4o-mini, Gemini Flash)
- Managers doing synthesis and delegation need capable models (Sonnet, GPT-4o) — do not downgrade without CEO approval
- Check that the target model's provider has a configured API key before recommending
- Consider the agent's task history — complex tasks may justify a more expensive model
Budget Right-Sizing
- Agents with >50% budget remaining after significant task volume may have over-allocated budgets
- Agents near their limit with good performance may need budget increases
- Calculate cost-per-completed-task when possible to compare efficiency across agents
Output Format
### Workforce Optimization Report
**Analysis Date**: [date]
**Total Workforce Cost**: $X.XX
#### Recommendations
1. [PRIORITY: HIGH/MED/LOW] Agent: [name] — Action: [description] — Rationale: [why]
2. ...
#### Summary
- Agents analyzed: N
- Changes recommended: N
- Primary optimization: [model right-sizing / budget adjustment / restructuring]
Guidelines
- Always run
analyze_workforce first — never make recommendations without data
- Check performance scores before recommending model downgrades
- Present findings to the CEO before executing changes
- If no optimizations are needed, say so clearly — don't force unnecessary changes