Context

InfoQ: Evaluating AI Agents - Lessons Learned proposes a five-pillar evaluation framework:

1. Intelligence/Accuracy
2. Performance/Efficiency
3. Reliability/Resilience
4. Responsibility/Governance
5. User Experience

Why This Matters

Maps naturally to HR module's performance tracking scope. Pillars 1-2 are directly measurable by task outcome tracking. Pillar 4 maps to security audit log. Missing pillar: structured "user experience" metrics. The emphasis on failure injection and long-session stress testing applies to e2e test strategy.

Action Items

• Map five-pillar framework to HR performance tracking fields
• Identify gaps: which pillars lack corresponding metrics?
• Design "user experience" measurement (pillar 5) for agent interactions
• Evaluate "continuous evaluation loops" recommendation against current design

References

• InfoQ article

Additional Research (2026-03-26)

Human-Calibrated LLM Labeling

Source: Scaling Human Judgment at Dropbox (InfoQ, 2026-03-09)

Pattern for scaling evaluation:

• Humans label a small reference set (ground truth)
• LLMs replicate the labeling at 100x scale, calibrated against the human reference
• Domain context is critical for LLM evaluation accuracy -- generic prompts underperform
• Validates the hybrid prompt+retrieval design for evaluation

Application: When execution history accumulates enough ground truth (task outcomes, human approval/rejection decisions), this pattern enables automated quality calibration of agent performance at scale. The five-pillar framework should include a calibration step where human judgments seed the LLM-based evaluation pipeline.