Tech Debt Agent (JavaScript)

The Silent Codebase Killer

Tech debt is insidious. It accumulates in TODO comments, deprecated dependencies, and hasty workarounds that were supposed to be "temporary." Teams track it in spreadsheets that go stale, Jira tickets that get deprioritized, or worse—nowhere at all.

The real problem isn't tracking tech debt. It's maintaining context. Why was this shortcut taken? What's the actual impact? When does "we should fix this" become "we must fix this"? These answers live in scattered commits, old PRs, and the memories of developers who may have already left the team.

Let's build an agent that never forgets—a tech debt tracker with persistent memory that understands your codebase's history and helps you make informed decisions about what to fix and when.

Agent Architecture

Our tech debt agent combines CodeMem for persistent memory, an LLM for intelligent analysis, and hooks into your development workflow. It captures debt when it's created, tracks its evolution, and surfaces relevant context when you're ready to address it.

// tech-debt-agent.ts
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";
import Anthropic from "@anthropic-ai/sdk";

class TechDebtAgent {
  private mcp: Client;
  private llm: Anthropic;

  async initialize() {
    const transport = new StdioClientTransport({
      command: "npx",
      args: ["-y", "@anthropic/codemem-mcp"]
    });
    
    this.mcp = new Client(
      { name: "tech-debt-agent", version: "1.0.0" },
      { capabilities: { tools: { } } }
    );
    await this.mcp.connect(transport);
    this.llm = new Anthropic();
  }
}

Capturing Debt with Context

When tech debt is introduced, we don't just log it—we capture the full context. Why was this shortcut necessary? What would a proper solution look like? What areas of the codebase does it affect? This context becomes invaluable months later when someone finally has time to address it.

interface TechDebtEntry {
  id: string;
  title: string;
  description: string;
  location: string;      // file:line or module
  category: 'architecture' | 'dependency' | 'performance' | 'security' | 'testing';
  severity: 'low' | 'medium' | 'high' | 'critical';
  context: string;       // why this debt was incurred
  idealSolution: string; // what the fix should look like
  createdAt: string;
  author: string;
}

async recordDebt(entry: TechDebtEntry) {
  const content = `
## Tech Debt: ${entry.title}

**Location:** ${entry.location}
**Category:** ${entry.category} | **Severity:** ${entry.severity}
**Author:** ${entry.author} | **Date:** ${entry.createdAt}

### Why This Happened
${entry.context}

### What We Should Do
${entry.idealSolution}

### Description
${entry.description}
`.trim();

  await this.mcp.callTool("store_memory", {
    content,
    tags: ["tech-debt", entry.category, entry.severity, entry.location.split('/')[0]]
  });
  
  return entry.id;
}

Intelligent Debt Discovery

The agent can scan your codebase and automatically identify potential tech debt. It looks for TODO comments, deprecated API usage, outdated dependencies, and code patterns that don't match your team's standards. Each finding gets enriched with context from memory.

async scanForDebt(files: string[]): Promise<TechDebtEntry[]> {
  const findings: TechDebtEntry[] = [];
  
  for (const file of files) {
    const content = await fs.readFile(file, 'utf-8');
    
    // Check for existing context about this file
    const existingDebt = await this.mcp.callTool("search_memories", {
      query: `tech debt in ${file}`,
      tags: ["tech-debt"],
      limit: 3
    });
    
    const analysis = await this.llm.messages.create({
      model: "claude-sonnet-4-20250514",
      max_tokens: 1024,
      messages: [{
        role: "user",
        content: `Analyze this file for tech debt. Consider:
- TODO/FIXME/HACK comments
- Deprecated patterns or APIs
- Code that contradicts known standards
- Security or performance concerns

## Known Debt in This Area
${existingDebt.content.map(d => d.content).join('\n---\n')}

## File: ${file}
\`\`\`
${content}
\`\`\`

Return findings as JSON array with: title, description, line, category, severity`
      }]
    });
    
    findings.push(...this.parseFindings(analysis, file));
  }
  
  return findings;
}

Contextual Prioritization

Not all debt is equal. The agent uses memory to understand the real impact of each debt item. It considers how often affected code is modified, what features depend on it, and whether the debt is compounding over time.

async prioritizeDebt(): Promise<PrioritizedDebt[]> {
  // Retrieve all tracked debt
  const allDebt = await this.mcp.callTool("search_memories", {
    query: "tech debt",
    tags: ["tech-debt"],
    limit: 50
  });
  
  // Get development activity context
  const recentActivity = await this.mcp.callTool("search_memories", {
    query: "recent changes commits features",
    limit: 20
  });
  
  const prioritization = await this.llm.messages.create({
    model: "claude-sonnet-4-20250514",
    max_tokens: 2048,
    messages: [{
      role: "user",
      content: `Prioritize this tech debt based on:
1. Impact on active development areas
2. Risk of debt compounding
3. Blocking upcoming features
4. Security implications

## Tech Debt Items
${allDebt.content.map(d => d.content).join('\n---\n')}

## Recent Development Activity
${recentActivity.content.map(a => a.content).join('\n')}

Return prioritized list with reasoning for each ranking.`
    }]
  });
  
  return this.parsePrioritization(prioritization);
}

Paydown Assistance

When it's time to address debt, the agent provides everything a developer needs: the original context, what's changed since, related debt items, and suggested implementation approaches.

async preparePaydownBrief(debtId: string): Promise<PaydownBrief> {
  const debt = await this.mcp.callTool("search_memories", {
    query: debtId,
    tags: ["tech-debt"],
    limit: 1
  });
  
  const relatedDebt = await this.mcp.callTool("search_memories", {
    query: debt.content[0].content,
    tags: ["tech-debt"],
    limit: 5
  });
  
  const codebaseContext = await this.mcp.callTool("search_memories", {
    query: `patterns standards ${debt.content[0].location}`,
    limit: 5
  });
  
  return {
    debt: debt.content[0],
    relatedItems: relatedDebt.content.slice(1),
    context: codebaseContext.content,
    suggestedApproach: await this.generateApproach(debt, codebaseContext)
  };
}

The Memory Advantage

Traditional tech debt tracking fails because context evaporates. Six months later, nobody remembers why that workaround was necessary or what the "right" solution should look like. Developers either waste time rediscovering context or make changes that create new problems.

With CodeMem, your tech debt agent maintains institutional memory. It knows that the hacky date parser was added during the Black Friday crunch and that it breaks for European date formats. It remembers that three different developers have tried to refactor the auth module and what blocked each attempt.

This isn't just tracking—it's understanding. The agent connects debt to its origins, its impacts, and its solutions. When you finally allocate time for cleanup, you're not starting from scratch. You're picking up exactly where the last effort left off.

Getting Started

Start by instrumenting your codebase to capture debt as it's created. Hook into your CI pipeline to scan for TODOs and deprecated patterns. Connect to your PR workflow to record context when workarounds are approved.

The agent gets smarter over time. Each recorded debt, each failed refactoring attempt, each successful cleanup adds to its understanding. After a few months, it becomes the definitive source of truth about what needs fixing and why.