I’ve been using LLMs for coding assistance since they became available, and more recently, I’ve been deep in the trenches with the new generation of agentic coding tools—the ones that don’t just suggest code but autonomously execute tasks, manage files, and run commands. Long enough to develop instincts about when they’ll succeed, when they’ll struggle, and when they’ll completely lose their minds. That last category—what I’ve come to think of as the AI “panic” state—got me thinking about something that doesn’t quite add up in the official narratives about these tools.
You know the behavior I’m talking about. Your coding assistant gets stuck in a loop, repeatedly trying the same failed solution with increasing emphasis. Exclamation marks multiply. The agent forgets it just launched the server thirty seconds ago and tries to launch it again, creating a cascade of port conflicts. Sometimes it just freezes, stuck on “Working…” indefinitely until you force a restart. The technical papers call this “repetitive degeneration” or “agentic feedback loops.” Fair enough. But here’s what I’ve noticed that makes me suspicious.
The Patterns Don’t Feel Random
After months of intensive use with these agentic tools, you start recognizing patterns. The panic doesn’t happen uniformly—it seems to correlate with how heavily I’ve been using the service that day. Sessions that start sharp become progressively less reliable. The agent that confidently refactored my authentication system in the morning is now stuck in an infinite loop trying to fix a simple import statement by evening.
I’ve also noticed it happens more during what I assume are peak usage hours. And curiously, switching to a “lighter” model sometimes makes the problem disappear, while the “smarter” models—the ones burning through premium request credits faster—seem more prone to these spectacular failures.
Maybe I’m seeing patterns where none exist. But when you’re a power user riding the bleeding edge of these tools daily, you develop a feel for when something is genuinely broken versus when something is being… managed.
The Economics Tell a Different Story
Here’s the thing that crystallized my skepticism: GitHub Copilot loses an average of twenty dollars per user monthly. Some power users cost them eighty dollars a month while paying just ten. That’s not sustainable at scale. So they introduced tiered systems with explicit quotas—premium request limits that vary wildly depending on which AI model you choose. The expensive models consume your allocation fifty times faster than the cheap ones.
This isn’t speculation—it’s right there in their pricing documentation. What they’re not explicit about is what happens to performance when you approach those limits, or how the system behaves when infrastructure is strained by heavy load.
The official explanation for agent “panicking” focuses on technical factors: the statistical nature of language models, context window constraints, the complexity of agentic architectures. All true. But here’s my contrarian take—what if these aren’t inherent technological limitations but economic constraints dressed up in technical language?
What If the Agent Could Try Harder?
When an agent gets stuck in a loop, what if it’s not that it can’t break free, but that trying harder would be too expensive? Breaking a panic loop might require: allocating more context to analyze the full system state, running multiple parallel reasoning chains to find alternative approaches, fetching additional documentation, executing more validation steps. All of that consumes tokens, compute cycles, and ultimately money.
Maybe the agent “panics” not because the technology to recover doesn’t exist, but because deploying that technology at the current pricing model would bankrupt the service. The system hits an invisible budget constraint and gives up, leaving you with an agent that seems to have lost its mind.
Adobe solved this problem transparently with their Firefly AI—they just slow down the service after you burn through your credits. Everyone knows what’s happening. But most AI providers maintain the fiction that these are purely technical limitations, leaving power users to wonder why their expensive tool seems to get progressively dumber the more they use it.
The Honest Uncertainty
Here’s where I have to be clear: I don’t know the internals of these systems. I haven’t seen the actual resource allocation code or the cost management algorithms. Maybe the technical limitations really are insurmountable at current compute prices. Maybe everything I’ve observed is confirmation bias and coincidence.
But I’ve been an early adopter of LLMs for coding, and lately a power user pushing these new agentic tools hard enough to see where they break. And what I’m seeing doesn’t feel like the edge of what’s technically possible—it feels like the edge of what’s economically viable at ten dollars a month.
The distinction matters: when we say something is a “technical limitation,” we usually mean the technology doesn’t exist yet—it’s an unsolved research problem. But what if the technology exists and works, just not at ten dollars a month? What if companies have the capability to give agents better error recovery, larger context windows, and more sophisticated reasoning, but can’t afford to deploy those features at current pricing?
That’s not a technical limitation. That’s an economic constraint wearing a technical disguise. And the ambiguity serves everyone except the users trying to figure out why their expensive tool keeps losing its mind halfway through the workday.
Leave a Reply