Nanny Coding: Why We Still (should) Babysit our AI Engineers

When Your AI Coder Acts Like an Overly Confident Intern (And Why That's Still Worth Supervising)

Let's call it what it is: Nanny Coding.

You spin up a fancy autonomous agent. You give it a scoped task. You hit "run" like a responsible adult. And then—you sit there. Watching. Refreshing logs. Cross-checking output like you're grading a freshman comp-sci assignment.

You're not just debugging. You're babysitting.

Here's what it should look like:

$ agentic-coder "Add Redis caching to the user service"
✓ Analyzing user service architecture...
✓ Implementing cache layer with appropriate TTL...
✓ Adding cache invalidation on user updates...
✓ Running tests... all passing
✓ Updating documentation
✓ Ready for deploy

What actually happens:

$ agentic-coder "Add Redis caching to the user service"  
✓ Adding cache... (writes to wrong Redis instance)
✓ Tests passing... (didn't include cache hit/miss scenarios)
✓ Done! (cache never actually gets used)

That gap between expectation and reality? That's why we babysit.

The Promise Was Automation

The whole premise of agentic coding is that the machine should be able to carry a task from spec to solution with minimal supervision. Break down goals. Handle API calls. Manage state. Refactor on the fly.

But the reality today? You still need to hover.

• It might hallucinate a schema that doesn't exist

• It might loop on retries that make no sense

• It might write to the wrong table—or delete the right one

• And it almost never names things well

We built copilots. What we got—at least for now—are interns with infinite confidence and no fear of prod.

Why Nanny Coding Persists

Four things I'll call out here:

1. Silent Failures Are Everywhere
   Most agents don't scream when something goes wrong. They return "success" with broken logic or incomplete reasoning.

2. Latent Context Gaps
   Even well-architected agents lose thread coherence. One file updated correctly, five others left untouched. You catch it—if you're watching.

3. We Don't Trust the Infrastructure (Yet)
   Observability for agentic workflows is still primitive. Most teams haven't built guardrails into their CI/CD. So human oversight fills the gap.

4. Context Windows Degrade Performance
   As tasks run long, agents eat through their context budget. The model that was brilliant at the start becomes confused and inconsistent after hitting token limits. You'll watch quality drop in real-time. Augment Code actually does warn you about this—you'll see a note in the prompt window when threads get too long, suggesting it might be time to start fresh. That's refreshingly honest for a tool that wants to seem autonomous. LLMs are paradoxical this way. In most domains, spending more time on something leads to better results. With language models? The longer they work on a task, the worse they often get. It's the rare case where persistence actually hurts performance.

You could avoid nanny coding by using less autonomous modes—Augment's Chat mode, for instance, where you have to approve every step. But the cost is velocity. You trade babysitting for micromanagement, which defeats the whole point of having an AI assistant in the first place.

Cursor handles this balance cleverly with its "YOLO mode" toggle. When disabled (the default), Cursor asks for approval before running any terminal commands. You still get autonomous code generation, but with a human gate on potentially risky operations. Windsurf takes a similar approach—its Cascade agent will ask for approval before running terminal commands by default, though it has a "Turbo mode" that auto-executes commands for experienced users.

Both tools recognize the same tension we're discussing: full autonomy speeds things up but requires more vigilance; manual approval slows things down but gives you control. The sweet spot seems to be selective autonomy—let the AI run free on safe operations like code generation and file editing, but require approval for anything that could have side effects.

What You're Actually Watching For

Nanny Coding isn't about micromanagement. It's about catching high-probability failure patterns that still show up in agentic workflows—and learning to recognize the warning signs.

You develop a sixth sense for when your agent is struggling. Watch for these tells:

• "Let me approach this another way" (translation: the first attempt failed)

• "Let me take a more direct approach" (code for: I'm simplifying the problem)

• The dreaded "Let's add mock data to make this work" (red alert: it's giving up)

• "Actually, let's modify the requirements slightly" (it can't solve what you asked for)

When you see these phrases, that's your cue to jump in and take the reins.

The funny thing is, most of the time—maybe 70% or so—everything runs smooth. No intervention needed. The agent cranks through the work, and you feel that perfect flow state. That's the promise realized: you're writing at the speed of thought, the AI handling the grunt work seamlessly.

But then—bam. You hit that mogul field like a ton of bricks.

Some of the most common failure patterns:

Giving Up Silently

Instead of solving a tough edge case, the agent wires in a mock, stubs out a handler, or leaves a vague "TODO: implement" and moves on. You'll miss it—unless you're watching.

Over-Simplifying the Problem

When the model hits complexity it doesn't fully understand, it often "reframes" the goal—i.e., punts. A classic move is reducing a multi-step flow to a single action that skips the actual business logic.

Forgetting Workflow Instructions

Even with structured prompting, long-running or multi-turn agents frequently lose track of constraints like "use this specific API" or "write results to this path." The further the task drifts, the fuzzier the memory gets.

Naming, Routing, and Integration Errors

The logic may work in isolation but fail at integration boundaries—wrong file paths, inconsistent function signatures, or broken assumptions about context.

False Confidence in Completion

Perhaps the most dangerous: when the agent claims success but has only completed part of the task. There's no error, no warning—just a subtle failure of scope.

These aren't just quirks—they're structural risks. The solution isn't distrust. It's designed supervision.

What Good Nanny Coding Looks Like

This isn't about rewriting the agent's work. It's about strategic supervision.

• Set clear checkpoints: break goals into discrete, testable steps

• Use typed outputs: don't just ask for JSON, validate it with Zod or Pydantic

• Build in auto-eval: let another model critique the output before you even see it

• And when in doubt—run it in staging. Always

You're designing a feedback loop. Not just watching for failure—guiding the system to avoid it.

The Long Game: Watching Less, Shipping More

Nanny Coding is a tax. But it's also a sign of progress.

You watch closely when you're teaching a new team, testing a new framework, or onboarding a junior dev. That's where we are with autonomous agents. The tooling will mature. Confidence will rise. Eventually, we'll stop hovering.

But for now, if you're not watching your AI coder work—you're probably missing something.

It comes down to this: we've made incredible progress with AI coding tools, but we're still in the early phases. The best teams aren't just using these tools—they're developing systematic ways to supervise them.

Let's make sure we're solving the real problem.

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Want to discuss this further? Drop a comment or reach out directly. I'm particularly interested in hearing about your own experiences with autonomous coding agents and how you're handling supervision.