How Observability Turns Vibe Coding into AI Engineering

In this blog post

1.A typical AI teammate’s first commit

2.The four layers of a reliable AI-generated code

3.Observability as a skill

4.The catch: Unpredictable bugs in production

5.The new software development loop

6.Building reliable AI engineering workflows with Dynatrace

Discover how to transform AI-generated code and Coding Agents from unpredictable contributors into reliable engineering work by combining prompt engineering, structured planning, skills, and a runtime feedback loop powered by observability.

By closing the loop between AI assistants, agents, and production telemetry, organizations can better support a disciplined AI engineering workflow.

AI is now part of how software gets built, no matter how you work. Some developers lean on copilots and inline suggestions inside the IDE. Some are building AI-powered features and applications. Others are orchestrating agents that scaffold services and open pull requests with little human input. The level of autonomy differs, but code generation gets faster than ever, and understanding how that code behaves in a real system is the new bottleneck.

According to research from CodeRabbit, AI-generated pull requests were reported to contain higher rates of certain defects than human-written pull requests.

Bugs are inevitable. The key is to identify them quickly and learn from them. The traditional software lifecycle, plan, code, deploy, test, observe, has historically been a one-way street, with humans transporting insights from the "observe" stage back to "plan." With the right tools, AI can travel that loop itself. The cycle becomes:

Plan → Code → Deploy → Test → Observe → and feed those observations back into the next plan.

To enable this loop, Agents need access to tools (for deployment, testing and observability). Modern coding agents are performing well in shell environments, which is one of the reasons why custom CLIs are emerging as a standard for Agents to access external systems. For live observability data, the Agent will use dtctl, which is an open-source CLI for accessing the Dynatrace platform, inspired by kubectl while the Agent can run deployments using the AWS CLI.

Observability can be a key enabler for moving from code generation toward scalable software engineering practices.

A typical AI teammate’s first commit

Consider a simple ask: "Write a function to fetch orders for a batch of users." Whether the code comes from an inline copilot or autonomous coding agent, the result often looks like this:

async def get_user_orders(user_ids: list[str]) -> list[dict]:<br>"""Fetch orders for a batch of users."""<br>async with httpx.AsyncClient() as client:<br>tasks = [<br>client.get(f"https://api.internal/orders?user_id={uid}")<br>for uid in user_ids<br>responses = await asyncio.gather(*tasks)

orders = []<br>for response in responses:<br>if response.status_code == 200:<br>orders.extend(response.json())

return orders<br>On the surface, this is good work. There are no syntax or logic errors, and the method’s purpose is documented. But the gaps are obvious to anyone who has run a service in production:

No error handling

No rate limiting

No observability

This is the default behavior of AI that hasn’t been told what "production quality" means at your organization. It made dozens of architectural decisions, HTTP client choice, error handling strategy, connection management, retry logic, timeouts – silently, and shipped them without telling you what or why.

The four layers of a reliable AI-generated code

The 4 layers of reliable AI-generated code<br>Prompting alone rarely gets you to production-quality code. A more reliable pattern is to make AI pause and explain its approach before it writes anything: what it is building, which systems it will touch, where it might fail, and how the result should be observed once it runs. That step doesn’t need to be elaborate, but it should force the reasoning before code generation.

This is also where lightweight skills or reusable instructions help. Instead of restating the same expectations in every prompt, teams can give agents and copilots a compact set of standards for things like error handling, logging, tracing, rate limits, and deployment conventions. Planning gives the agent a chance to think; skills make that thinking consistent with how your organization builds and operates software.

Together, those two practices move away from guessing and toward design. They won’t catch every runtime issue, but they dramatically improve the odds that the first version reflects your architectural assumptions, operational standards, and definition of production-ready code.

Preventing this requires layering guardrails on top of the agent:

Prompt engineering: Be clear and direct, add context, use examples, use XML tags, and define output format.

Planning: Make the AI "think" before writing code, optionally in a dedicated plan mode.

Skills: Extend what AI knows and how it...