From Traditional Developer to Systems Architect: The 2024 Evolution

I've been building software since college (C++, Java circa 2000), but 2024 changed everything. This is the story of how AI development evolved from copy-paste chaos to autonomous agents that genuinely understand projects and work for days without supervision.

The Evolution Timeline

Before vs After: Managing Everything vs Directing Goals

Before: You Were the Project Manager

The old workflow looked like this:

You: Write PLANNING.md (explain architecture)
You: Write TASK.md (break into steps)
You: Feed to AI assistant
You: Copy code back
You: Test
You: Write new TASK.md for fixes
You: Repeat

You were the project manager, orchestrator, and QA. Exhausting.

Now: Agents Manage Themselves

The new workflow with Claude Code + Agents SDK:

You: "Build a resale certificate agent that queries Buildium,
     validates data, generates PDFs, and emails results.
     It should run autonomously with checkpoints."

Agent: - Explores your codebase
       - Designs architecture
       - Implements incrementally
       - Tests itself
       - Fixes errors
       - Creates checkpoints
       - Runs for 30+ hours if needed
       - Reports back with working solution

The Four Key Shifts

1. From Files to Conversations

2. From Steps to Goals

TASK.md:
[ ] Create database schema
[ ] Write API endpoint
[ ] Add error handling
[ ] Write tests

"Build a data validation system that ensures HOA fees
are within 20% of association average. It should flag
outliers and log all decisions."

3. From One-Shot to Iterative

The most powerful shift: agents now test themselves, see errors, and fix them automatically. No more copying error messages back and forth.

4. From Stateless to Stateful

Before, every prompt started fresh. Now, agents maintain context across sessions, resume from checkpoints, and remember previous work. This is the foundation that makes 30+ hour runs possible.

The Mental Shift: From Compiler to Product Owner

Real Example: The Difference in Practice

Old Way: Context Engineering (Late 2024)

# Resale Certificate Generator

## Architecture
- Query Buildium API for property data
- Validate data completeness
- Fill PDF template
- Store in R2
- Email to title company

## Tasks
1. Set up Buildium API client
2. Create data validation module
3. Implement PDF generation
4. Configure email service
5. Add error handling
6. Write tests

# Current Task: Buildium API Client

## Steps
- [ ] Install fetch library
- [ ] Create API wrapper class
- [ ] Add authentication
- [ ] Implement getProperty method
- [ ] Add error handling
- [ ] Write unit tests

Then feed these files to your AI assistant, get code, test, update TASK.md, repeat. Tedious.

New Way: Agentic Development (October 2025)

I need to automate resale certificates for my property management company.

CONTEXT:
- I use Buildium for property data (API docs: [URL])
- Resale certificates have specific required fields (list)
- Title companies need PDFs emailed within 24 hours
- HOA fees must be validated against association averages

REQUIREMENTS:
- Query Buildium when I provide property address
- Validate all data (flag if fees seem wrong)
- Generate PDF using template (I'll provide)
- Email to title company
- Log all decisions for audit

SUCCESS CRITERIA:
- Generate 3 test certificates matching manual process
- Flag test case with incorrect HOA fee
- Run end-to-end in under 2 minutes
- All data validated before PDF creation

CONSTRAINTS:
- Don't email test certificates (mock email during development)
- Don't modify Buildium data (read-only)
- Must work with my existing Obsidian MCP setup

Build this incrementally, test as you go, and use checkpoints
so I can resume if interrupted.

That's it. The AI agent then:

1. Figures out the architecture
2. Breaks into tasks internally
3. Implements incrementally
4. Tests each piece
5. Fixes errors it encounters
6. Shows you progress
7. Creates checkpoints
8. Delivers working solution

Best Practices for October 2025

1. Goal-Oriented Prompts

2. Business Context First

Don't just describe the technical requirement—explain why it matters:

3. Trust + Verify

Let the agent build the solution its way, then validate business logic:

• ✓ Let agent build solution
• ✓ Run the tests/validation
• ✓ Check business logic correctness
• ✓ Approve or redirect
• ✗ Don't specify every function name
• ✗ Don't dictate file structure
• ✗ Don't micro-manage approach

4. Use Checkpoints

For complex integrations, request checkpoints every 30-60 minutes. This lets you review progress and resume if anything breaks.

Moving Forward: What This Means for You

This is the right evolution for your role.

You're the business expert who understands:

• HOA regulations
• Resale certificate requirements
• E-commerce logistics
• Property management workflows

The agent is the implementation expert who:

• Translates your requirements to code
• Handles technical details
• Tests and iterates
• Maintains state
• Runs until complete

About Eli

Systems Architect based in NH/VT. I design systems, AI agents build them. Background in systems administration and 25+ years of hardware/troubleshooting experience. Currently building production AI solutions for property management and e-commerce.

More about me → Get in touch →