We Train the Models, But Not the Operations - Welcome to Vibe-Ops
Welcome to “Day 2” for AI agents — and the rise of Vibe-Ops
We spend millions training AI models.
But we forget the one thing that makes them useful:
An operational culture that ensures they actually work in the real world.
Most AI failures aren’t caused by bad models.
They’re caused by bad assumptions.
This is “Day 2” — the moment after deployment when things break not because the code is wrong, but because nobody taught the system how to behave under pressure.
And when your team is made of LLM-powered agents?
You’re not just debugging code — you’re debugging intuition.
Building the Virtual CTO Advisor — and Its Ops Team
I’m not just experimenting with AI tooling.
I’m building a production-grade AI system called The Virtual CTO Advisor, grounded in my personal corpus:
570 blog posts
860+ enterprise video segments
Over 5,000 LinkedIn posts
7,000+ knowledge assets in total
These assets form the semantic memory behind Virtual Keith, queried in real-time using Vertex AI Search, and synthesized through Gemini 1.5 and 2.5 Flash models.
The system architecture is robust:
RAG pipeline
Thread-aware conversation memory
Grounded citations
Stateless Cloud Run backend
Responsive frontend via Firebase + Cursor
But none of that guarantees operational safety.
Because architecture doesn’t catch deployment logic errors.
Culture does.
This Isn’t Just DevOps.
This is Vibe-Ops.
DevOps was built to automate pipelines and tighten feedback loops across delivery teams.
But DevOps assumes human engineers are making decisions.
Vibe-Ops is what comes next.
It’s the operational discipline required for autonomous, agentic systems — systems that don’t just run themselves, but make decisions, interact with users, and evolve across sessions.
Where DevOps is about shipping faster,
Vibe-Ops is about failing smarter.
Where DevOps governs infrastructure and CI/CD,
Vibe-Ops governs prompts, models, and agent behavior.
It’s the layer of operational empathy and institutional memory needed to make agentic systems enterprise-ready.
Day 2 Lessons from the Field
When I deployed Virtual CTO Advisor, it “worked.”
But Day 2 exposed the gap — and it wasn’t in the code.
Just because the backend returns a 200 doesn’t mean the UI isn’t broken.
My AI agent confirmed the analytics microservice worked.
It validated the backend change. The endpoint was live. Logs were clean.
But what the agent didn’t check?
The frontend.
There was a broken dependency in the deployment script.
The analytics feature worked.
The production app did not.
The problem wasn’t the AI’s logic.
It was the lack of a governance layer.
No prompt directive for end-to-end validation.
No dependency awareness.
No operational handoff.
In short: the AI behaved like an engineer who never read the runbook.
And the fix wasn’t more code.
It was a change in expectations — a governance update.
I had to teach the agent to ask:
“What else might this affect?”
That’s Vibe-Ops in action.
Governance = Continuity = Risk Mitigation
Enterprise IT leaders know this:
Every operational gap is a governance risk in disguise.
AI makes that risk faster.
When agents operate with partial context or no visibility into adjacent systems, they increase the blast radius of every change.
And the wild part?
These systems are stateless. The agent you work with now might not be the one answering your next question.
Gemini 1.5 handles chat
Gemini 2.5 handles research
Model selection varies by prompt
Agents don’t persist unless you make them
Same UI (Cursor). Different reasoning engine. No continuity unless you create it.
Without clear governance, documentation, and validation?
You’re one prompt away from an outage.
Vibe-Ops = Culture for Agentic Systems
To make AI agents reliable, you need more than prompt engineering.
You need systems thinking. You need culture.
Vibe-Ops includes:
🧠 Clear role definitions (researcher, API executor, QA)
📄 Prompt-level documentation
🔁 Context-aware agent handoffs
⚠️ Trust boundaries and fallback paths
🧪 End-to-end test directives embedded in prompt logic
📊 Observability into agent behavior and decisions
It’s not about making the model “smarter.”
It’s about making the environment safer.
It’s about teaching agents to reason like a team — not just write code like one.
Architecture Alone Won’t Save You
Yes, the Virtual CTO Advisor is built to scale:
Gemini models (1.5 / 2.5) for fast, cost-effective generation
Vertex AI Search for semantic retrieval over 7K documents
Firestore for persistent session and message threading
Cloud Run for scalable backend microservices
RAG architecture with source citation, evidence scoring, and query decomposition
But no amount of infrastructure guarantees reliability.
You can’t ship safety into a system that doesn’t know how to protect itself.
That’s why we build Vibe-Ops.
Final Thought:
You don’t get reliability from AI.
You teach it.
That’s the job now.
We don’t need to ask, “How do I deploy more AI?”
We need to ask,
“How do I teach my AI to be a better teammate?”
Because the biggest risks aren’t in the models — they’re in the operational blind spots.
Let me give you one last example:
I asked Cursor to update the analytics service — a separate microservice.
The AI made the change successfully.
But it triggered a flawed deployment script, which broke the production frontend.
Our post-mortem didn’t point to a code failure.
It pointed to a governance failure.
The prompt lacked a directive for system-wide validation.
The agent did exactly what it was told — and nothing more.
The fix wasn’t technical.
It was cultural.
We taught the agent to validate across the stack.
Not because it’s “smart” — but because reliability is taught.
That’s the heart of Vibe-Ops:
Not just building systems — but building systems that reason about systems.
AI that can write code is easy.
AI that can reason about risk?
That’s leadership.
Welcome to Day 2.
Welcome to Vibe-Ops.
👇 Want to see the orchestration layer, prompt design system, or grounding strategy in action?
Drop a comment. Part 2 is already in progress.