Oaken AI Agent Auditor

Projected Results

Estimated state after implementing all recommendations  ·  April 13, 2026

Projections assume all recommended hooks, rules, and permission changes are implemented. AI consulting from Oaken AI can implement these in a single session.

85
Projected Score
+32 from 53
LOW
Autonomy Risk
Already low
100%
Observability
+59% coverage gained
4/4
Problem Types
Already covered

Metric Improvements

53
85
Architecture Score
+32 points after all recommendations
LOW
LOW
Autonomy Risk
Deny + ask rules prevent unintended destructive actions
42%
100%
Observability
All 6 hooks configured — full session traceability
4/4
4/4
Problem Coverage
Rules covering all four LLM failure modes

Observability After

With all 6 hooks in place, every agent sub-task, file change, bash command, and session boundary is logged. This is the baseline for production-grade multi-agent systems.

100% weighted coverage

HookEventPriorityStatus
Agent Tracing PostToolUse:Task CRITICAL ✓ Present
Session Logging Stop CRITICAL ✓ Present
Memory Preservation PreCompact IMPORTANT ✓ Present
Session Init / Auto-Recall SessionStart IMPORTANT ✓ Present
File Change Audit PostToolUse:Write|Edit USEFUL ✓ Present
Command Logging PostToolUse:Bash USEFUL ✓ Present

Problem Coverage After

With rules covering all four problem types, Claude has explicit guidance for every class of LLM failure — domain gaps, context overflows, hallucinations, and control.

Domain Knowledge Gaps
Rules that give Claude project-specific knowledge (memory, RAG, docs)
✓ Covered
Context Window Limits
Rules that manage what survives session boundaries (handoffs, compaction)
✓ Covered
Hallucinations
Rules that enforce verification before asserting facts about the codebase
✓ Covered
Difficulty of Control
Rules that gate destructive actions and enforce approval workflows
✓ Covered
Multi-Agent SystemsClaude Code ToolsAI ConsultingAI InfrastructureAI AdvisoryFree AI AssessmentAgent ArchitectureLLM Automation

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