Inspection Automation Platform

Viva Republica (Toss) · Jun 2026 – Present

Role · Platform design and development

10+departmentsused as a shared module across inspection tasks

Every inspection task needed its own agent, and when results were off, people rewrote the prompts by hand and ran them again.
When results were off, it was hard to tell whether the prompt was wrong or the policy and criteria (the direction) themselves needed to change.
Requirements, policies and past cases were scattered across chat threads, documents and sheets, so even preparing to build an agent took a long time.

C1 Telling prompt problems apart from problems where the policy (the direction) must change
C2 Cutting repeated manual steps such as organizing requirements, assembling agents and editing prompts
C3 Managing each task's policies, data and formats together and improving them by version

AI-driven improvement cycleC1

AI finds recurring format and evidence errors in run logs and review data, drafts prompt changes that fit the agent's structure, and re-runs the same data to compare
Errors a prompt cannot fix are shown to users as places where the data conflicts with the current direction (policy) or the policy has gaps, with supporting cases, affected scope and a proposed fix
Users set the direction and approve; AI does the editing, re-running and comparing, so the cycle is easy to repeat

AI features that cut the steps in betweenC2

AI gathers scattered material (chat threads, documents, sheets, owner notes), drafts the inspection requirements, and turns gaps into questions
From the requirements, AI proposes the components and their order and builds a draft inspection agent, flagging what it cannot build and what is missing
AI generates each task's prompts and supports versioned experiments, so prompt versions can be compared on results

Platform architecture and shared moduleC2C3

Ran inspection agents on LangGraph; built the intermediate AI features (requirements, agent drafts, prompt learning, policy refinement) as Claude Agent SDK services
Kept policies, data, cases and input/output formats together as reference information, and connected each task's agent in a develop → run → improve flow
Stored each run's verdict, evidence, run history and version, and fed reviewers' final decisions (confirm, edit, hold) into the next improvement

Framework / Platform: LangGraph, Claude Agent SDK
Methodology: Requirement drafting, Agent drafting, Prompt learning, Policy refinement, Versioned experiments