Case Study | Agentic Workflows

AI Agent Operations Console

A working product surface for an AI agent that turns a business request into a tool-driven workflow, checks risk, saves structured output, and routes uncertain items to review.

Input

Business request

Prompt

Model route Risk mode

Input contract

{
  "workflow": "product_launch",
  "market": "etsy_shopify",
  "required_checks": ["trademark", "margin", "novelty"],
  "output_format": "reviewable_json"
}

Runtime

Agent execution

Ready

Output

Structured result package

Launch brief

Waiting for agent run

The generated brief will appear here after the workflow completes.

Reviewable JSON

{
  "status": "not_started",
  "message": "Run the agent to generate a structured output."
}

Architecture

Where the agent sits in production

The client sees a console. Underneath it, the agent is a stateful service that routes requests, calls tools, persists run history, checks deterministic rules, and only expands automation after review metrics are reliable.

Client UI
Next.js console

→

API Route
auth + request contract

→

Agent Orchestrator
planner + tool router

→

Tool Registry
trend, catalog, compliance

→

Postgres State
runs, outputs, review queue

Code Pattern

Deterministic gates around model output

The important engineering move is deciding what belongs in the model and what belongs in code. The model can generate options; code should enforce thresholds, blocked terms, schema shape, and review routing.

type AgentDecision = {
  productTitle: string;
  opportunityScore: number;
  restrictedTerms: string[];
  marginEstimate: number;
  confidence: number;
};

function reviewGate(decision: AgentDecision) {
  const flags = [];

  if (decision.restrictedTerms.length) flags.push("restricted_term");
  if (decision.marginEstimate < 0.42) flags.push("low_margin");
  if (decision.confidence < 0.88) flags.push("low_confidence");

  return {
    canAutoPublish: flags.length === 0,
    reviewRequired: flags.length > 0,
    flags
  };
}