---
title: "Building Your First AI Agent"
description: "Step-by-step guide to building an AI agent that actually works. From idea to production in 2 weeks."
pillar: "AI Agents"
level: "beginner"
date: "2026-01-20"
url: "https://theglitch.ai/academy/agents/building-first-agent"
---

# Building Your First AI Agent

Step-by-step guide to building an AI agent that actually works. From idea to production in 2 weeks.


# Building Your First AI Agent

> **The Glitch's Take:** "Your first agent should be boring. Boring agents ship. Exciting agents fail."

**Part of:** [AI Agents & Automation Guide](/articles/agents/ai-agents-complete-guide)
**Level:** Beginner
**Reading Time:** 10 minutes

---

## The Point

Most first agents fail because they're too ambitious. This guide walks you through building something simple that actually works—then scaling from there.

---

## TL;DR

- **Choose boring:** Low-stakes, narrow scope, clear value
- **Use n8n:** Best balance of power and accessibility
- **Build time:** 4-8 hours for first agent
- **Suggest mode first:** Human approval for 4-8 weeks
- **Then automate:** Once you trust it

---

## Choosing Your First Agent

### Good First Agents

| Agent | Why It's Good |
|-------|---------------|
| Weekly competitor check | Low stakes, clear value, simple logic |
| New lead notification | Trigger-based, minimal reasoning |
| Content summary | One input, one output |
| Daily metrics digest | Scheduled, read-only |

### Bad First Agents

| Agent | Why It's Bad |
|-------|--------------|
| Customer support bot | High stakes, complex reasoning |
| Automated email sender | Risk of embarrassment |
| "Manage my marketing" | Scope too broad |
| Trading bot | High stakes, needs perfection |

### The Criteria

Your first agent should be:

1. **Narrow:** One specific task
2. **Low-stakes:** Failure doesn't matter much
3. **Measurable:** You can verify it works
4. **Useful:** You'll actually use it regularly

---

## The Agent We'll Build

**Competitor Price Monitor**

- Checks 3 competitor pricing pages daily
- Compares to previous prices
- Alerts you via Slack if anything changes
- Logs history for trends

**Why this agent:**
- Simple to build (4-6 hours)
- Clear value (saves manual checking)
- Low risk (worst case: missed alert)
- Easy to verify (you can manually check)

---

## Prerequisites

### Tools Needed

| Tool | Cost | Purpose |
|------|------|---------|
| n8n account | Free-$24/mo | Workflow platform |
| Anthropic API key | Pay-per-use | AI reasoning |
| Slack workspace | Free | Notifications |
| Airtable account | Free | Data storage |

### Time Required

| Phase | Hours |
|-------|-------|
| Setup | 1 |
| Build | 3-4 |
| Test | 1 |
| Deploy | 0.5 |
| **Total** | **5-6** |

---

## Step 1: Setup (1 hour)

### Create Accounts

1. **n8n:** Sign up at n8n.io (cloud) or self-host
2. **Anthropic:** Get API key from console.anthropic.com
3. **Slack:** Create a channel for alerts (#competitor-alerts)
4. **Airtable:** Create a base called "Competitor Tracking"

### Set Up Airtable

Create a table called "Price History" with columns:

| Column | Type |
|--------|------|
| Competitor | Single line text |
| Date | Date |
| Product | Single line text |
| Price | Currency |
| Previous Price | Currency |
| Changed | Checkbox |
| Raw Data | Long text |

### Connect Credentials in n8n

1. Go to Credentials
2. Add Anthropic (paste API key)
3. Add Slack (OAuth connection)
4. Add Airtable (API key)

---

## Step 2: Build the Workflow (3-4 hours)

### Overall Flow

```
Schedule Trigger (Daily 8 AM)
  → For each competitor:
      → Scrape pricing page
      → Extract prices (Claude)
      → Get yesterday's prices (Airtable)
      → Compare
      → If changed → Slack alert
      → Log to Airtable
```

### Node by Node

#### Node 1: Schedule Trigger

- Type: Schedule Trigger
- Frequency: Daily at 8:00 AM
- Timezone: Your timezone

#### Node 2: Competitor List

- Type: Set
- Define competitors:
```json
{
  "competitors": [
    {"name": "Competitor A", "url": "https://competitor-a.com/pricing"},
    {"name": "Competitor B", "url": "https://competitor-b.com/pricing"},
    {"name": "Competitor C", "url": "https://competitor-c.com/pricing"}
  ]
}
```

#### Node 3: Loop Over Competitors

- Type: Split In Batches
- Batch Size: 1

#### Node 4: Scrape Page

- Type: HTTP Request
- URL: `{{ $json.url }}`
- Method: GET

Or use Jina Reader for better scraping:
- URL: `https://r.jina.ai/{{ $json.url }}`

#### Node 5: Extract Prices (Claude)

- Type: Anthropic
- Model: claude-sonnet-4-5-20241022
- Prompt:
```
Extract all pricing information from this webpage content.

Return JSON in this exact format:
{
  "products": [
    {"name": "Product name", "price": 99.99, "period": "monthly"},
    {"name": "Product name", "price": 999, "period": "yearly"}
  ]
}

Only include actual prices found. If no prices found, return empty products array.

Content:
{{ $json.data }}
```

#### Node 6: Get Yesterday's Prices

- Type: Airtable
- Operation: List Records
- Filter: `{Competitor} = '{{ $json.name }}' AND {Date} = '{{ $today.minus(1, 'day').toFormat('yyyy-MM-dd') }}'`

#### Node 7: Compare Prices

- Type: Code
```javascript
const current = $input.item.json.products;
const previous = $input.item.json.previous || [];

const changes = [];

for (const product of current) {
  const prev = previous.find(p => p.name === product.name);
  if (prev && prev.price !== product.price) {
    changes.push({
      product: product.name,
      oldPrice: prev.price,
      newPrice: product.price,
      change: ((product.price - prev.price) / prev.price * 100).toFixed(1)
    });
  }
}

return { changes, hasChanges: changes.length > 0 };
```

#### Node 8: If Changes Detected

- Type: IF
- Condition: `{{ $json.hasChanges }}` equals `true`

#### Node 9: Send Slack Alert

- Type: Slack
- Channel: #competitor-alerts
- Message:
```
🚨 Price Change Detected

Competitor: {{ $json.competitor }}

{{ $json.changes.map(c => `• ${c.product}: $${c.oldPrice} → $${c.newPrice} (${c.change}%)`).join('\n') }}
```

#### Node 10: Log to Airtable

- Type: Airtable
- Operation: Create Record
- Fields: Map extracted data to columns

---

## Step 3: Test (1 hour)

### Test Each Node

1. Run trigger manually
2. Check each node's output
3. Verify data flows correctly
4. Test with a URL you know has prices

### Test Scenarios

| Scenario | Expected Result |
|----------|-----------------|
| Prices unchanged | No Slack alert, log created |
| Price increased | Slack alert with details |
| Price decreased | Slack alert with details |
| Page unavailable | Error handled gracefully |
| No prices found | Log empty, no alert |

### Common Issues

| Issue | Fix |
|-------|-----|
| Scraper blocked | Use Jina Reader or Firecrawl |
| Claude returns wrong format | Add "Return ONLY valid JSON" to prompt |
| Slack not sending | Check OAuth permissions |
| Airtable errors | Verify field names match exactly |

---

## Step 4: Deploy (30 minutes)

### Enable the Schedule

1. Activate the workflow
2. Verify schedule trigger is enabled
3. Check timezone is correct

### Set Up Monitoring

1. Add error webhook to Slack
2. Enable execution logging
3. Set up daily health check

### First Week: Suggest Mode

Even though this is low-stakes, monitor manually:
- Check Airtable logs daily
- Verify alerts match manual checks
- Note any false positives/negatives

---

## Deployment Options: Where Your Agent Lives

### Option 1: n8n Cloud (Simplest)

For the competitor monitor we built: n8n Cloud is fine. It's a scheduled workflow with limited scope.

**When cloud works:**
- Agent has narrow, defined scope
- No sensitive credentials beyond API keys
- Low autonomy (trigger → run → stop)

### Option 2: Self-Hosted (More Control)

For agents with more access or autonomy, consider self-hosting.

**Why self-host:**
- Control over environment
- Can restart when issues arise
- Your data stays on your infrastructure
- Lower cost at scale

**Options:** Railway ($12-20/mo), VPS providers, Docker on your own server.

### Option 3: Isolated VPS (For Autonomous Agents)

For agents that run continuously, browse the web, or access sensitive data: isolate them.

**The principle:** Think of an autonomous agent less like software and more like a VA who has access to your email, files, and passwords. It's autonomous. It's AI. It's not deterministic.

**Why isolation matters:**
- Agent has access to everything in its environment
- If compromised, attacker gets what agent has
- Prompt injection attacks can manipulate behavior
- Mistakes compound when agent runs unsupervised

**Isolation approach:**
1. Dedicated VPS (not your main machine)
2. Separate credentials (agent's own email, not yours)
3. Read-only permissions by default
4. Scoped access (only what it needs, nothing more)

**What NOT to give autonomous agents:**
- Access to your main email
- Access to your password vault
- Admin credentials to production systems
- Full disk access

This isn't paranoia. It's the same principle as not giving a new employee admin access on day one.

---

## Security Considerations

### Common Risks

| Risk | What Happens | Prevention |
|------|--------------|------------|
| **Prompt injection** | Malicious content in scraped pages manipulates agent | Sanitize inputs, limit what agent can execute |
| **Credential exposure** | Agent logs or leaks API keys | Use environment variables, limit logging |
| **Runaway execution** | Loop runs indefinitely, costs spiral | Hard limits on iterations and spend |
| **Unauthorized access** | Compromised agent accesses your systems | Scope permissions, isolate environment |

### Practical Security Defaults

For your first agent (low-stakes, narrow scope):
- Use API keys with minimal permissions
- Don't store sensitive data in agent environment
- Monitor execution logs
- Start in suggest mode (human approval)

For autonomous agents (high-autonomy, broader access):
- Isolated environment (VPS or container)
- Agent-specific credentials
- Read-only access as default
- Explicit permission escalation
- Regular audit of what agent accessed

---

## Step 5: Iterate

### Week 1-2: Observe

- Does it run reliably?
- Are the prices extracted correctly?
- Any false alerts?

### Week 3-4: Refine

- Improve Claude prompt based on errors
- Add more competitors if working well
- Adjust alert thresholds

### Month 2+: Expand

- Add feature tracking (not just prices)
- Add more competitors
- Build trend analysis
- Consider more agents

---

## Cost for This Agent

### One-Time

| Item | Cost |
|------|------|
| Setup time | 5-6 hours |

### Monthly

| Item | Cost |
|------|------|
| n8n (cloud) | $24 |
| Claude API (~90 runs/month) | $2-3 |
| Airtable | $0 (free tier) |
| Slack | $0 |
| **Total** | **~$27/month** |

### Value

| Metric | Value |
|--------|-------|
| Time saved | 2-3 hours/month |
| At $50/hr | $100-150/month value |
| ROI | ~4-5x |

---

## What's Next

Once this agent works reliably:

1. **Add complexity:** More competitors, feature tracking
2. **Build another:** Lead enrichment or content monitoring
3. **Connect agents:** Feed outputs into other workflows

---

## Quick Reference

### Agent Building Checklist

- [ ] Clear, narrow scope defined
- [ ] Low stakes confirmed
- [ ] All credentials connected
- [ ] Core workflow built
- [ ] Error handling added
- [ ] Tested with real data
- [ ] Monitoring set up
- [ ] Suggest mode enabled
- [ ] First week reviewed manually

### Troubleshooting

| Problem | Solution |
|---------|----------|
| Workflow won't trigger | Check schedule timezone |
| Claude returns errors | Simplify prompt, add format examples |
| Slack silent | Verify channel permissions |
| Costs higher than expected | Add caching, reduce frequency |

---

## FAQ

### How long until my agent is reliable?

Expect 2-4 weeks of daily monitoring before you can trust it to run unsupervised. Most issues surface in the first week.

### What if my agent stops working?

Check in this order: API credentials, source website changes, rate limits, n8n execution logs. 90% of failures are one of these four.

### Can I build agents without n8n?

Yes. Make (Integromat) and Zapier work too. But n8n has the best AI integration and costs less at scale.

### Should I start with a template or from scratch?

Start from scratch for your first agent. You'll understand the pieces better. Use templates after you've built 2-3 manually.

### How do I know if my agent is accurate?

Manual spot-checks. Compare 10 agent outputs to what you'd produce manually. If accuracy is below 90%, refine the prompts.

---

## What's Next

**Ready to go deeper on n8n?**
- [n8n Agent Building Guide](/academy/agents/n8n-agent-guide)

**Want to prevent failures?**
- [Agent Failure Modes](/academy/agents/agent-failure-modes)

**Want prompt templates for agents?**
- [Agent Building Pack](/packs/agent-building-pack)

---

## Sources

- [n8n Documentation](https://docs.n8n.io)
- [Anthropic Claude Documentation](https://docs.anthropic.com)

---

*Last verified: 2026-01-20. Build time based on actual first-time implementations.*