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How to Build Your First AI Agent in 30 Days (Complete Guide)
We built our first AI agent in 22 days. It now handles work that used to take two engineers full-time.
Here's the exact playbook we used-and you can replicate it.
Week 1: Choose the right workflow
Don't start with your hardest problem. Start with your most repetitive one.
The ideal first agent:
- High volume (runs daily/weekly)
- Rule-based (clear success criteria)
- Low risk (failures are recoverable)
- Measurable impact (easy to track ROI)
Examples from our portfolio:
Red flags to avoid:
- Customer-facing (too risky for v1)
- Ambiguous outcomes (hard to measure success)
- Requires creative judgment (better for generative AI)
Pick one workflow. Commit to it for 30 days.
Week 2: Build the perception layer
- Your agent needs to "see" what's happening.
- What you're building:
- Data collection (APIs, webhooks, scrapers)
- State management (what changed since last check?)
- Event detection (when should the agent act?)
Our QA flow example:
- Monitors GitHub for new commits
- Detects which files changed
- Identifies affected test suites
- Triggers when confidence score drops below threshold
Tools we use:
- Temporal for workflow orchestration
- PostgreSQL for state management
- Webhooks for real-time events
Time investment: 5-7 days, 1 engineer
Week 3: Build the reasoning & action layer
Now your agent needs to decide what to do and do it.
What you're building:
- Decision logic (if X, then Y)
- LLM integration (for complex reasoning)
- Tool connections (APIs to take actions)
- Error handling (what if something fails?)
Our QA flow example:
- Analyzes code changes with GPT-4
- Determines which tests to run
- Executes test suite via API
- Analyzes results and decides next steps
Tools we use:
- OpenAI/Anthropic for reasoning
- LangChain for orchestration
- Custom APIs for actions
- Retry logic for reliability
Time investment: 7-10 days, 1 engineer
Week 4: Build the learning layer
Your agent needs to improve over time.
What you're building:
- Feedback collection (what worked/failed?)
- Performance metrics (speed, accuracy, cost)
- Model fine-tuning (optional for v1)
- Human-in-the-loop for edge cases
Our QA flow example:
- Tracks which bugs it caught vs missed
- Measures time saved vs manual testing
- Flags low-confidence decisions for human review
- Improves test selection based on historical data
Tools we use:
- PostHog for analytics
- Custom dashboards for monitoring
- Slack for alerts
- Weekly review meetings
Time investment: 5-7 days, 1 engineer
The tech stack
Here's what actually works:
Orchestration: Temporal or Inngest
- LLMs: GPT-4 or Claude (we use both)
- Databases: PostgreSQL for state, Redis for caching
- Monitoring: Datadog or PostHog
- Infrastructure: AWS or GCP (we prefer AWS)
Common pitfalls we hit
- Over-engineering v1
Don't build general AI. Build a specific solution for one workflow. Generalize later.
2. Under-investing in monitoring
You need to know when your agent fails. Build observability from day one.
- Skipping human review
Even autonomous agents need human oversight initially. Build confidence gradually.
- Optimizing too early
Get it working first. Optimize for cost/speed in month two.
Measuring Success
Track these metrics from day one:
- Tasks completed (volume)
- Success rate (accuracy)
- Time saved (hours)
- Cost per task (economics)
- Human interventions needed (autonomy)
What happens after 30 days
You'll have a working AI agent. It won't be perfect. That's fine.
Now you enter the improvement phase:
Month 2: Optimize performance and reduce costs
Month 3: Add edge case handling
Month 4: Scale to more workflows
The Compounding Effect
The first agent is the hardest. The second takes 2 weeks. The third takes 1 week.
That's what we've seen across our portfolio. The patterns repeat. The infrastructure is reusable.
Build one agent. Learn the patterns. Then scale.
Need help building your first AI agent? Islands offers consulting and implementation.
Want to learn more?
Let’s talk about what you’re building and see how we can help.
No pitches, no hard sell. Just a real conversation.
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You’re all set!
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