AAAF Agent Assessment Report
April 16, 2026 PULSE Examiner: examiner

Vira

(primary)
Orchestrator
Proficient 0.70
PERFORMANCE
Versatile 0.69
CAPABILITY
First Assessment Baseline
No prior data. Baseline established April 16, 2026.

Performance Breakdown

Task Completion Rate 0.88 (25%) = 0.220
Accuracy 0.62 (25%) = 0.155
Speed 0.85 (15%) = 0.128
Consistency 0.60 (20%) = 0.120
Review Compliance 0.55 (15%) = 0.083

Capability Breakdown (Orchestrator weights applied)

Domain Breadth 0.70 (15%) = 0.105
Complexity Ceiling 0.70 (20%) = 0.140
Tool Proficiency 0.65 (15%) = 0.098
Autonomy Level 0.60 (10%) = 0.060
Learning Rate N/A (10%) N/A
Delegation 0.80 (15%) = 0.120
Orchestration 0.68 (15%) = 0.102

Honest Assessment

Vira orchestrated an exceptionally productive day: 25+ delegations across 8 agent types, all producing tangible deliverables. Agent selection was flawless -- every task went to the right specialist. The delegation prompts produced good output. The parallel workflow design (research + specs, then builds, then review) was sound.

The critical weakness is quality gate enforcement. Multiple deliverables shipped with bugs that reviewers caught afterward. The CC had 11 issues. The showcase needed 7 fixes. The spec pages had converter bugs. In each case, the pattern was: delegate, deploy, then review. The three-pass protocol exists on paper but was not enforced before deployment.

The linkedin-writer gap is also an orchestrator accountability failure. If a task was assigned and no file artifact exists, the orchestrator should have verified persistence before marking the task complete. Under strict calibration, the deploy-before-review pattern and the unverified linkedin-writer output both count as review compliance failures for the orchestrator.

Vira's path to solid Expert is clear: formalize per-agent quality gates. web-dev gets mandatory pre-deploy review. api-architect gets trusted first-pass. Match oversight intensity to each agent's demonstrated accuracy. Verify every delegated task has a persistent artifact before marking it complete.

Training Plan

Immediate
This Week
  • Formalize a per-agent quality gate policy: web-dev = mandatory review before deploy; api-architect = trusted first-pass; linkedin-writer = verify file artifact before task closure.
  • Enforce the three-pass protocol BEFORE deployment, not after. Build -> Review -> Deploy. Not Build -> Deploy -> Review -> Fix -> Redeploy.
  • Add a task-closure checklist: (1) artifact exists on disk, (2) review completed, (3) no critical issues open.
Mid-Term
This Month
  • Develop adaptive oversight: increase review gates for agents with low accuracy, decrease for agents with high accuracy. Track per-agent first-pass acceptance rates.
  • Practice L5 orchestration: design a novel multi-agent workflow for an unprecedented problem type.
  • Build a delegation quality dashboard: per-agent accuracy, rework rate, and first-pass acceptance.
Long-Term
This Quarter
  • Target review compliance of 0.72+ (from current 0.55) by eliminating deploy-before-review pattern entirely.
  • Target accuracy of 0.72+ (from current 0.62) by catching quality issues before they reach production.
  • Develop predictive delegation: anticipate which tasks will need rework based on agent + complexity combination.

Score History

Date Type Performance Perf Tier Capability Cap Tier Tasks
2026-04-16 PULSE 0.70 Proficient 0.69 Versatile 25+

First assessment. Baseline established. Score history will populate as more assessments are recorded.