| Dimension | Vibe Coding | Structured AI-Assisted | Agentic Engineering |
|---|---|---|---|
| Intent spec | Casual NL prompts | Detailed prompts with examples | Formal specs, architecture docs, memory files |
| Verification | “Does it seem to work?” | Manual testing, spot-checking | Automated test suites, CI/CD gates, LM judges |
| Codebase understanding | Minimal; may not read generated code | Selective review of critical paths | Comprehensive review of architecture |
| Error handling | Copy-paste error messages to AI | Developer diagnoses root cause | Agents self-diagnose within defined bounds |
| Appropriate scope | Prototypes, scripts, hackathons | Features in established codebases | Production systems, team-scale development |
| Risk profile | High; acceptable for disposable code | Moderate; human judgment at key checkpoints | Low; systematic verification at every stage |
The right position on this spectrum depends on the stakes. A weekend prototype can be pure vibe coding. A production API handling financial transactions demands agentic engineering. Most real work falls somewhere in between, and the skill is knowing where to draw the line for each task.
Coding Agents in Practice
Vibe Coding Production-Ready Agents
The same terminal-based workflow that produces prototype scripts now reaches production agents. Building, evaluating, and deploying a real agent — with persistent memory, governance, and observability — has moved from a framework and cloud console task into something that happens in the same terminal.
Google’s Agents CLI bundles skills for building agents on Google Cloud, covering the full ADK lifecycle: scaffolding, writing, evaluating, deploying, and wiring up observability. After a one-time install, the coding agent gains seven new skills.
# One-time setup
uvx google-agents-cli setup
# Then in your coding agent:
# > Build a support agent that answers questions from our docs.
# > evaluate it on the FAQ dataset
# > Deploy it to Agent EngineBehind that single instruction, the coding agent scaffolds a project from a template, writes the ADK code, generates an evalset, runs it against the agent, deploys to Agent Runtime, and reports back.
Coordination across agents happens through: - Shared session state for simple cases - Model Context Protocol (MCP) for tool access - Agent2Agent (A2A) protocol for cross-agent delegation