Agentic AI in Backend Development: Building Multi-Agent Systems for Production

I deployed my first AI agent on a Monday morning. By Wednesday, it had "optimized" a database query that broke my app for about 20 minutes before I caught it. By Friday, my OpenAI bill was $180.

For a side project, that hurt.

But I learned a ton about making AI agents actually useful without breaking the bank. Here's what worked (and what definitely didn't).

What These Things Actually Do

Forget the buzzwords. Here's what matters: these agents can break down tasks, make decisions, call APIs, and adjust when things go wrong.

For my projects, that means agents that review code, spot slow queries, and sometimes catch bugs I would've missed. The trick is keeping them cheap enough to actually use.

Why I Went Multi-Agent

My first attempt was one big agent trying to do everything. Terrible idea.

It would get confused switching between reviewing TypeScript code and optimizing SQL queries. Like asking one person to be a frontend dev and a DBA at the same time—technically possible, but messy.

So I split it up:

// Each agent has one job
const agents = {
  'code-review': new CodeReviewAgent(),
  'query-check': new DatabaseAgent(),
  'error-handler': new RecoveryAgent()
}

// Simple routing
function handleTask(task) {
  const agent = agents[task.type]
  return agent.execute(task)
}

Way better. Each agent got good at its thing.

The $180 Week (And How I Fixed It)

Here's what happened: I set up three agents without thinking about API costs. They were calling GPT-4 for every tiny decision.

Monday: $25. "That's fine." Wednesday: $70. "Hmm, that's high." Friday: $180. "Okay, this needs to stop."

The fix was embarrassingly simple—cache everything:

class CostOptimizer {
  private cache = new Map()
  
  async executeAgent(agent, task) {
    // Check cache first
    const cached = this.cache.get(task.hash())
    if (cached) return cached
    
    // Only call API if we have to
    const result = await agent.execute(task)
    this.cache.set(task.hash(), result)
    
    return result
  }
}

Now I'm spending about $30/month. Still not free, but manageable for the value I'm getting.

The Query Optimizer That Broke My App

This one's embarrassing but educational.

I had an agent that looked at slow queries and suggested optimizations. Worked great in testing. Then it hit my production database.

It saw a query with a subquery and thought, "I can flatten this!" It was right—the new query was 3x faster. But it also changed the logic in a subtle way that broke my duplicate detection.

I didn't notice for 20 minutes. About a dozen records got duplicated before I rolled it back.

The fix wasn't technical—it was process:

class ValidationLayer {
  async validateOptimization(original, optimized) {
    // Run both on test data
    const originalResults = await runQuery(original, testData)
    const optimizedResults = await runQuery(optimized, testData)
    
    // Must be identical
    if (!deepEqual(originalResults, optimizedResults)) {
      throw new Error('Results don't match')
    }
    
    return true
  }
}

Now every optimization gets validated before it touches production. Slower, but I haven't broken anything since.

What Actually Works on a Budget

After six months of experimenting, here's what I'm running:

Code Review Agent: Catches obvious issues before I commit. Saves me maybe 30 minutes a week. Cost: ~$15/month.

Query Analyzer: Flags slow queries and suggests indexes. I review and apply them manually. Cost: ~$8/month.

Log Pattern Finder: Scans logs for weird patterns I'd never spot manually. This one's been surprisingly useful. Cost: ~$7/month.

I tried an error recovery agent but it was too unpredictable. Turned it off.

The Three Phases (Don't Skip These)

Don't do what I did and go straight to production. Use these phases:

Phase 1: Shadow Mode (1-2 weeks)

Agent makes decisions but doesn't execute them. You compare what it suggests to what you'd do.

async function shadowMode(task) {
  const agentDecision = await agent.decide(task)
  
  // Log it, don't execute it
  console.log('Agent suggests:', agentDecision)
  
  // You do it manually
  return manuallyHandle(task)
}

This is where you catch the scary stuff.

Phase 2: Assisted Mode (ongoing)

Agent suggests, you approve. I'm still in this phase for database stuff.

Phase 3: Autonomous Mode (maybe someday)

Agent acts independently. I only do this for super low-risk stuff like code formatting.

Keeping Costs Down

Here's what actually works:

Use GPT-3.5 when possible: It's 10x cheaper than GPT-4 and good enough for most tasks.

Cache aggressively: Same question = same answer. Don't pay twice.

Batch operations: Process multiple items in one API call when you can.

Set hard limits: I have a $50/month cap. When I hit it, agents stop until next month.

What I'd Do Differently

Start even smaller. I tried to automate too much too fast. Pick one annoying task and nail it.

Budget $50-100 for the first month while you're learning. You'll waste some of it. That's fine.

Be paranoid about validation. Agents sound confident even when they're wrong.

Is It Worth It?

For me? Barely, but yes.

I'm saving maybe 2-3 hours per week. At my freelance rate, that's worth more than the $30/month I'm spending. Plus I'm learning a lot about AI systems.

But here's the thing—I'm not trying to replace myself. I'm trying to automate the boring parts so I can focus on the interesting problems.

The code review agent catches typos and missing error handling. The query analyzer spots obvious performance issues. That's it. They're not writing features or architecting systems.

And honestly? That's enough.

Should You Try This?

If you're curious and have $50 to experiment with, go for it. Start with code review or log analysis—they're low-risk and actually useful.

Don't expect magic. These agents are tools, not teammates. They'll catch some bugs and miss others. They'll save you time on boring tasks and occasionally waste your time with bad suggestions.

But if you're building stuff solo or with a tiny team, having an AI agent catch the obvious mistakes while you focus on the hard problems? That's pretty valuable.

Just watch your API bill.

---

Experimenting with AI agents on a budget? I'd love to hear what you're trying. Hit me up on LinkedIn if you've got stories to share.