Six months ago, I used AI like most developers: copy-paste from ChatGPT. Today I run a multi-agent pipeline that ships code while I sleep.
Here's what changed — and it wasn't the models.
Stage 1: The chatbot phase
One prompt, one response, copy into IDE. Better than Stack Overflow, but still manual. I was the bottleneck at every step: reading output, deciding what's next, pasting code, running tests. The AI was fast. I wasn't.
Stage 2: The "do everything" agent
I gave one agent full access — filesystem, terminal, git. It worked... sometimes. But a single agent trying to analyze requirements, write code, test it, AND review it? That's like asking one person to be architect, developer, tester, and code reviewer simultaneously.
Quality collapsed on anything non-trivial.
Stage 3: Specialized agents in a pipeline
This is where things clicked. Instead of one mega-agent, I split the work:
- Agent 1 analyzes requirements and produces a spec
- Agent 2 designs the architecture
- Agent 3 implements the code
- Agent 4 reviews it and finds issues
- Agent 3 fixes them
Each agent reads the previous one's output. No shared context. No confusion about what phase we're in. The task tracker IS the communication bus — each agent attaches its artifact and moves the task forward.
The three changes that actually mattered:
1. Model routing by task, not by budget. Frontier model for conversation with me. Mid-tier for autonomous coding. Cheap model for bulk work. Same day, three models, costs under $2.
2. The human stays in the loop — but only at decision points. I don't watch agents type. I review artifacts between phases. Architecture decisions, tech stack choices, trade-offs — that's my job. Implementation? That's theirs.
3. Reference projects > instructions. Instead of writing 500-line prompts explaining my coding style, I point agents at a working codebase. "Follow this pattern." They infer the architecture, the naming conventions, the test structure. Pattern matching beats specification every time.
What I got wrong at first:
- Thinking more context = better results. It's the opposite. Focused agents with clear scope outperform omniscient agents drowning in context.
- Not tracking costs. When you're spawning 20 agents a day, pennies add up. Model routing cut my bill by 80%.
- Skipping the review agent. "The code works" ≠ "the code is good." A second agent consistently catches what the first one misses.
The real shift isn't technical. It's mental.
You stop being the person who writes code and start being the person who defines what good code looks like. You architect. You review. You make the calls that agents can't.
Six months in, I ship more than I ever did writing everything by hand. And the code is better — because it gets reviewed every single time.
The best developers won't be the fastest coders. They'll be the best orchestrators.