From Chaotic Vibes to Real Velocity With AI-First Engineering
Vibe coding made software feel accessible. Weekend hustlers shipped prototypes. Teams saw "wow" moments on repeat. Then reality hit: inspiration is not production.
If you want shipping speed without breaking everything, you need a system. That system is AI-first engineering.
Where Vibes Hit the Wall
Most teams see ~20% gains because they bolt AI onto old habits. Same meetings, same tickets, just better autocomplete. That's a ceiling you can't brute-force past.
On the other end, disciplined teams run fleets of agents with review loops. One engineer burned through 3.5 billion tokens in a month, four agents in parallel, agents reviewing agents. With a proper system, throughput doubled over a few months. That's the point: process beats vibes.
AI-First Engineering: The System
This isn't a bag of tricks. It's a way of working. Think fewer heroics, more predictable speed. The core practices below stack together.
- Spec-Driven Development (SDD): Three steps every time.
- High-level spec: goals, constraints, acceptance criteria.
- Technical spec: real interfaces, data models, edge cases.
- Execution plan: step-by-step tasks with owners (human or agent).
- Agents as a team, not a blob: Define roles (Researcher, Tech Spec, Implementer, Reviewer). Use separate agents for creation and critique to reduce drift.
- Context beats prompts: Feed agents repo maps, API schemas, env files (sanitized), and past decisions. Retrieval over long prompts.
- Token discipline: Set budgets per task, cache context, prefer diff-based changes. Track tokens per ticket like a cost center.
- Review gates: Human-signoff on specs. Automated checks on code. Agent reviewer first, human reviewer last.
- Small, steady merges: Short-lived branches, small PRs, fast feedback. Big-bang changes create rework.
- Measure what matters: Cycle time, PR size, incident rate, test coverage deltas, tokens per task, agent-to-human edit ratio.
- Continuously update the knowledge base: Turn specs, decisions, and postmortems into reusable patterns agents can consume.
Example: Adding Google Calendar Integration with SDD
Here's how the flow looks when you do it right. Keep the roles separate and the steps tight.
- Ask an agent to draft product requirements: user stories, permissions, sync behavior, failure states. You review and edit.
- Send the approved requirements to a different agent to analyze your repo and write a technical spec: endpoints, data models, auth strategy, rate limits, testing plan.
- You review the technical spec (and let a reviewer agent punch holes in it). Approve.
- Have a new agent produce a step-by-step execution plan: tasks, owners, test cases, rollout plan. Approve.
- Implementer agent generates changes in small slices. Reviewer agent checks diffs and tests. Human reviews final PRs.
- Ship behind a flag. Monitor logs and error rates. Document what worked and feed it back into your knowledge base.
If you need API details, the docs are solid: Google Calendar API.
What to Stop Doing
- Letting agents "go build it" without a spec.
- Copy-pasting the same prompt across tasks.
- Judging progress by lines of code or demo flair.
- Opening giant PRs no one can review well.
- Jamming multiple features into one ticket to "save time."
How to Start This Week
- Pick one area (a feature or bug cluster) and commit to SDD for two sprints.
- Create simple templates for high-level spec, technical spec, and execution plan.
- Assign 3-4 agent roles (Researcher, Tech Spec, Implementer, Reviewer) and set handoff rules.
- Track five metrics: cycle time, PR size, test coverage change, incidents, tokens per task.
- Run daily 10-minute checks on blockers, not status theater.
- End each week with a blunt retro. Keep what worked. Cut what wasted time.
Why This Works
AI can write code, but systems ship products. Specs reduce rework. Roles reduce confusion. Small batches reduce risk. Measurement keeps you honest.
Do that on repeat and you'll feel the shift: less chaos, more throughput, fewer fires. That's real velocity.
Helpful References
- Agile Manifesto - Short, still useful when adapted for AI-era workflows.
- AI Certification for Coding - For teams formalizing agent workflows and prompt standards.
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