--- title: "Designing Agentic Control Systems for Data Integrity & Financial Readiness" date: 2024-08-30 author: "alex.lukashevich" featured_image: "https://stage.spiralscout.com/wp-content/uploads/2026/03/Car-Advise-Preview.png" --- # Designing Agentic Control Systems for Data Integrity & Financial Readiness ![CA Logo](https://stage.spiralscout.com/wp-content/uploads/2026/03/CA-Logo-Fearless-Orange.svg "CA Logo") # Designing Agentic Control Systems for Data and Financial Readiness at CarAdvise ### Canonical Agent States ### Read-Only Validation ### Human OVERSIGHT ![Car Advise Hero](https://stage.spiralscout.com/wp-content/uploads/2026/03/Car-Advise-Hero.jpg) About [About](#about-project) [Challenges & Solutions](#challenges-and-solutions) [Strategy](#highlights) [Results](#services) ## About THE Project CarAdvise operates a transaction-heavy automotive marketplace where pricing accuracy, reconciliation, and payouts depend on data flowing correctly across multiple systems. As the business scaled, small data defects increasingly caused downstream issues – Ops escalations, Finance delays, and manual reconciliation work that relied heavily on tribal knowledge. CarAdvise leadership and engineering recognized that adding more automation or “smart features” would not solve the root problem. The system lacked **clear control points** and **canonical definitions** for when data, orders, and payments were actually trustworthy. Spiral Scout was engaged to lead a [discovery and pilot-design effort](https://stage.spiralscout.com/services/it-consulting/product-discovery) focused on defining how AI agents could safely operate inside these workflows without introducing financial or operational risk. The work included building multi-tenant, long-running workflows, auditability, permissioning, failure recovery, integration within their live enterprise systems. This work centered on architecture, constraints, and decision-making, not feature delivery. ## Objectives - Identify where data quality failures originate and how they propagate - Define canonical system states for completion, settlement, and payout readiness - Reduce reliance on manual Ops and Finance judgment - Establish guardrails for introducing AI into financial workflows - Create a reusable agent pattern that future builds can rely on ![About CA](https://stage.spiralscout.com/wp-content/uploads/2026/03/About.jpg) ## Challenges ## Solutions Challenges Data Risk Was Invisible Until It Became Expensive Invalid VINs, missing vehicle classification, orphaned services, and pricing inconsistencies routinely flowed through the system undetected. These issues were typically discovered only during reconciliation or partner disputes – when resolution was slow and costly. Solutions Data Health Agent (Pilot Design) We designed a read-only Data Health Agent concept that continuously evaluates vehicles and orders using event-driven triggers and scheduled scans. The agent does not fix data; it detects anomalies, classifies business risk, and routes structured context to Ops early. This reframed data quality as an ongoing control problem, not a cleanup task. Challenges “Completed” Was Not a Reliable System State Orders could be marked complete internally while still lacking settled payments or matching vendor invoices. Teams relied on experience rather than system logic to determine payout eligibility. Solutions Agent-Defined Reconciliation States We defined a set of agent-derived reconciliation states computed via three-way matching across internal orders, payment records, and vendor statements. These states exist outside core order models, reducing schema risk while removing ambiguity. This clarified that completed and payout-ready are fundamentally different system states. Challenges Manual Reconciliation Didn’t Compound Learning Ops and Finance repeatedly resolved the same categories of reconciliation issues with no systematic way to capture or reuse decisions. Solutions Exception Classification Framework We designed a structured exception taxonomy that agents can apply consistently. Each failure is classified, contextualized, and routed for human resolution – creating a foundation for future automation without removing human authority. Challenges AI Introduces Risk in Financial Systems Allowing agents to modify pricing or approve payouts would introduce unacceptable operational and audit risk. Solutions Guardrails by Architecture All proposed agents are explicitly constrained to read-only access. They validate, classify, and recommend – but never change financial data or approve payouts. Perfect matches can be flagged, but authority remains with Finance. ![CA Challenge](https://stage.spiralscout.com/wp-content/uploads/2026/03/Challenge.jpg) ## our Project strategy This required production judgment across finance, data modeling, and AI risk – not just ML experimentation. ![CA Strategy](https://stage.spiralscout.com/wp-content/uploads/2026/03/Strategy-01.jpg "CA Strategy") ### Govern Before You Automate Agents were framed as control layers that make systems observable and trustworthy before they become autonomous. ![CA Strategy](https://stage.spiralscout.com/wp-content/uploads/2026/03/Strategy-02.jpg "CA Strategy") ### Narrow Scope, High Leverage Rather than building broad assistants, we focused on the smallest set of agent behaviors that meaningfully reduce risk. ![CA Strategy](https://stage.spiralscout.com/wp-content/uploads/2026/03/Strategy-03.jpg "CA Strategy") ### Architecture That Enables Independence The agent patterns, states, and workflows were designed to be owned and extended by the CarAdvise team – not locked to Spiral Scout. ## Current State & Direction At the end of this phase, CarAdvise had: – A clearly defined Data Health Agent architecture and scope – Canonical reconciliation logic separating completion from payout readiness – Guardrails for safely introducing AI into financial workflows – A repeatable agentic pattern ready for future pilots and early builds This work established an **architectural north star** for moving from manual processes and AI experimentation toward dependable, production-grade systems. ## Key Takeaways - System-level clarity matters more than feature velocity - Read-only agents can deliver leverage without increasing risk - Canonical states reduce human ambiguity better than dashboards - Independence is designed, not promised Worth thinking about if you’re trying to introduce AI into systems where money, trust, and operational correctness matter. ![CA Results](https://stage.spiralscout.com/wp-content/uploads/2026/03/Results.jpg) ## Related projects - ![Proxa preview](https://stage.spiralscout.com/wp-content/uploads/2026/04/proxa-preview.png "Proxa preview") ![proxa-logo-w-text](https://stage.spiralscout.com/wp-content/uploads/2026/04/proxa-logo-w-text.svg "proxa-logo-w-text")### Agent-Driven AI Data Hub with Grounded Executive Reporting Grounded AI retrieval, generative reporting, and conversational search for an executive data hub. Trusted answers. Stack owned by their team. AI Agents, Agentic Retrieval, Generative UI [Link](https://stage.spiralscout.com/case/agent-driven-ai-data-hub-proxa) - ![Preview](https://stage.spiralscout.com/wp-content/uploads/2026/03/Preview-1.png) ### Market Discovery & System Framing for an AI-Driven Investor Relations Platform Established the architectural and market foundation for an AI-native earnings call product. AI-Assisted Workflows, Discovery, Investor Relations, Capital Markets [Link](https://stage.spiralscout.com/case/ai-investor-relations-market-discovery) - ![Preview](https://stage.spiralscout.com/wp-content/uploads/2026/03/Preview-3.png) ### Conversational CRM Agent Orchestration for Salesforce-Native SaaS Shipped a resilient multi-agent architecture that turns complex Salesforce CRM data into a conversational interface for sales teams. AI Agent Automation, Workflow Orchestration, Salesforce Integration [Link](https://stage.spiralscout.com/case/conversational-crm-agent-salesforce) - ![Staq](https://stage.spiralscout.com/wp-content/uploads/2024/08/Staq.jpg) ![Staq logo](https://stage.spiralscout.com/wp-content/uploads/2024/08/Staq_idQ95uE_5__0.svg)### Agentic Workflow Orchestration for Autonomous Banking Infrastructure Architected a Temporal-backed agentic runtime for a fintech platform shipping autonomous financial workflows to regulated markets. AI Agents, Temporal Orchestration, Fintech/Banking [Link](https://stage.spiralscout.com/case/agentic-workflow-orchestration-autonomous-banking) - ![Car Advise Preview](https://stage.spiralscout.com/wp-content/uploads/2026/03/Car-Advise-Preview.png) ![CA Logo](https://stage.spiralscout.com/wp-content/uploads/2026/03/CA-Logo-Fearless-Orange.svg)### Designing Agentic Control Systems for Data Integrity & Financial Readiness Engineered a configuration system replacing tribal knowledge with enforced rules, deployed to distributors without IT drag. AI Agents, Data Governance, Workflow Orchestration [Link](https://stage.spiralscout.com/case/agentic-control-system-design-automotive-marketplace) - ![Danfos - preview](https://stage.spiralscout.com/wp-content/uploads/2024/08/Danfos-preview.png) ![Danfoss Logo](https://stage.spiralscout.com/wp-content/uploads/2026/03/RGB-Danfoss-boxlogo-2025-Red.png)### Rules-Driven Configuration Architecture for the Danfoss Network Engineered a configuration system replacing tribal knowledge with enforced rules, deployed to distributors without IT drag. Guided Configuration Logic, Platform Engineering, Supply Chain [Link](https://stage.spiralscout.com/case/intellibuild-danfoss-configuration-platform) ## Automating high-stakes workflows? Let’s discuss your pilot. WHAT’S NEXT 1 Meet the founders 2 Tell us your goals 3 Receive a proposal 4 Project kickoff ![John Griffin](https://stage.spiralscout.com/wp-content/uploads/2026/04/John.png "John Griffin")### John Griffin Co-Founder, CEO ![Anton JD Titov](https://stage.spiralscout.com/wp-content/uploads/2026/04/Anton-2.png "Anton JD Titov")### Anton “JD” Titov Co-Founder, CTO *“Anton is an exceptional technologist. I would feel comfortable having him work on any technical challenge.”* – Ryland Goldstein, Head of Product, Temporal