B

AI Agent Behavior Audit Log

3.35

Derivation Chain

Step 1 Advancement of AI future prediction and autonomous behavior models
→
Step 2 Increase in enterprises adopting agentic AI
→
Step 3 Post-hoc audit and accountability tracking of agent autonomous actions
→
Step 4 Audit log visualization and automated compliance report generation

Problem

In IT companies that have adopted AI agents (systems that autonomously perform API calls, data access, and decision-making), it is impossible to trace after the fact what data the agent accessed, in what order, and why it made certain decisions. To prepare for regulations such as the EU AI Act, companies spend 5,000,000-10,000,000 KRW per case (approx. $3,750-$7,500) on external law firm consultations, and it takes an average of 2 weeks to identify the cause of an incident.

Solution

Inserted as middleware into AI agent frameworks (LangChain, CrewAI, AutoGen, etc.), it automatically records all tool calls, data accesses, and decision branches, and visualizes them on a timeline. Provides automatic detection of anomalous behavior patterns, automatic mapping of compliance checklists for regulations (EU AI Act, Korean AI Basic Act draft), and one-click forensic report generation in case of incidents.

Target: CTOs and security officers at IT startups with 10-50 employees that operate AI agents in production.
Revenue Model: SaaS monthly subscription: 99,000 KRW per month for 5 agents (approx. $74.25), additional agents at 15,000 KRW per month each (approx. $11.25). Forensic report generation: 50,000 KRW per report (approx. $37.50).
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
4.0/5
M Market
3.0/5
R Realizability
3.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (72%)

Tech Complexity
29.3/40
Data Availability
23.1/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (58/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
16.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
12.0/15
Solo Buildability
5.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Backend [medium] Frontend [medium] Infrastructure [low]
Dashboard