Building an AI Incident Auditor That Understands Financial Impact

How I built an agent to audit incidents that understands financial impact.
A few months ago, I was tasked with building an AI tool to help our operations team triage incoming incidents and audit requests. Naturally, I built a chatbot.
It was a disaster.
Engineers would paste in a log file, and the bot would reply with a 500-word essay explaining what a stack trace was. When asked if a deployment was safe, it would say "It seems generally fine," completely oblivious to the fact that it was analyzing a multi-million dollar trading infrastructure.
I realized that chatbots are useless for operations. What we needed was a Decision Engine. An agent that understands urgency, risk, and, most importantly, financial impact.
Here is how I abandoned the chatbot paradigm and built a structured decision intelligence platform using CascadeFlow and Hindsight.
Killing the Chat Interface
The first thing I did was destroy the traditional "chat" input. If you want an agent to understand business context, you have to force the user to provide it.
I updated our React frontend to require explicit "Audit Parameters" before a user could submit a query. They have to define:
Financial Context: (e.g., "$5M budget at risk")
Urgency: (Low, Medium, High, Critical)
Data Sensitivity: (Public, Internal, Secret/PII)
This structured data is then injected directly into the LLM's system prompt on the backend.
Intelligent Orchestration with CascadeFlow
Because we were now dealing with highly sensitive financial and operational queries, routing became critical. We couldn't afford to run simple policy lookups through expensive models, nor could we trust a cheap 8B model with a $5M deployment decision.
I integrated cascadeflow to handle dynamic model selection.

// The cascade routing logic
export async function determineRoute(query, sensitivityLevel, urgency) {
  // Always escalate highly sensitive or critical urgency queries
  if (sensitivityLevel === 'secret' || urgency === 'critical') {
    return { 
      selectedModel: 'llama3-70b-8192', 
      reason: 'Critical urgency or highly sensitive data detected. Forcing maximum reasoning capabilities.' 
    };
  }

  // Otherwise, fallback to semantic complexity analysis
  const complexity = await analyzeComplexity(query);
  if (complexity.score < 40) return { selectedModel: 'llama3-8b-8192', reason: 'Low complexity.' };

  return { selectedModel: 'mixtral-8x7b-32768', reason: 'Standard decision parameters.' };
}

This cascadeflow GitHub implementation dropped our overall inference costs by 65% while actually increasing safety on critical requests.
Adding Corporate Hindsight
A decision engine isn't very useful if it makes the same mistake twice. I needed the agent to learn from our organizational history.
I implemented agent memory via the Hindsight docs. Every time the agent generates a decision card with an escalationRequired flag, the incident is committed to Hindsight's vector database.
When a similar incident occurs months later, the system performs a similarity search and retrieves the historical financial impact and remediation steps, providing massive organizational continuity.
The Dashboard
Instead of rendering markdown chat bubbles, the frontend now parses the strict JSON output and renders a command-center dashboard.

It shows live risk distribution charts, highlights exact financial impacts (e.g., Estimated Savings: $40,000), and explicitly flags Governance Severities.
Takeaways
Stop building generic chatbots. If you are building tools for enterprise operations, build structured forms and force JSON outputs.
Context is everything. Injecting parameters like "Urgency" and "Financial Impact" directly into the prompt fundamentally changes how seriously the LLM treats the problem.
Use orchestration. Don't hardcode your models. Use semantic routing to balance cost, speed, and intelligence dynamically.