The Two-Character Edit That Saved Dollars: Catching Stuck Instances in the Vast.ai Fleet

Subject Message: [assistant] [edit] /tmp/czk/cmd/vast-manager/main.go — Edit applied successfully.

At first glance, message 4877 in this coding session appears to be the most unremarkable entry imaginable. A single line confirming that an edit was applied to a Go source file. No fanfare, no explanation, no visible diff. Yet this message represents the culmination of a meticulous forensic investigation into a subtle production failure, and the fix it embodies would go on to prevent the autonomous fleet management agent from bleeding money on dead instances that the monitoring system had been trained to ignore.

The Context: A Stuck Instance Bleeding Storage Charges

The story begins with a user report in message 4869: an instance on vast.ai with ID 33032537 was showing "Error" in the vast.ai web UI, but the autonomous fleet management system was doing nothing about it. The user's request was straightforward — the instance should be automatically removed, and the agent should be notified of the error and its reason.

What the assistant discovered next was far more interesting than a simple error state. When querying the vast.ai API directly via vastai show instance 33032537 --raw, the assistant found a contradictory picture. The actual_status field reported "loading" — a status that the monitor's hard policy did check for, but only in conjunction with a 3-hour age threshold. However, the cur_state field reported "stopped", the next_state field reported "stopped", and the intended_status field also reported "stopped". The instance was, for all practical purposes, dead — stuck in a limbo where vast.ai had attempted to stop it but the transition had never completed, leaving it accruing storage charges at $0.148/hour while masquerading as a "loading" instance.

The assistant's reasoning trace in message 4871 reveals the full depth of the diagnosis:

"The monitor's hard policy only checks for actual_status == "exited" || "error" || "loading" || "scheduling" — but it also checks StartDate for the age threshold. [...] The monitor doesn't catch this because it only checks actual_status which says 'loading'. I need to: 1. Parse status_msg and cur_state from vast API, 2. Include error info in the notification to the agent, 3. Fix the monitor to detect cur_state=stopped as an error state."

This is a classic operational blind spot: the monitoring system was designed around a single API field that the engineers had assumed would be sufficient to determine instance health. The vast.ai API, however, uses a multi-field state model where actual_status reflects what vast.ai thinks the instance is doing, while cur_state and intended_status reflect the actual virtual machine state. An instance can be "loading" in the scheduler's eyes while being fully stopped in reality — and the original monitor code had no awareness of this distinction.

The Fix: Two Edits, One Structural and One Logical

The assistant executed the fix in two surgical edits to /tmp/czk/cmd/vast-manager/main.go. The first edit, in message 4875, added two new fields to the VastInstance struct — the Go data model that represents a vast.ai instance as parsed from the API:

CurState    string `json:"cur_state"`
NextState   string `json:"next_state"`

These fields were already present in the vast.ai API response but had never been parsed into the application's data model. The JSON deserialization was silently dropping them because the struct had no corresponding fields. This is a common pitfall in Go's encoding/json package: extra fields in the JSON payload are silently ignored unless the struct explicitly declares them.

The second edit — message 4877, the subject of this article — updated the monitor's hard policy logic to use these new fields. Where the original code only checked vi.ActualStatus for values like "exited", "error", "loading", and "scheduling", the updated logic now also detects instances where vi.CurState == "stopped" or vi.IntendedStatus == "stopped". This catches the exact class of failure that instance 33032537 exemplified: instances that are functionally dead but whose actual_status hasn't transitioned to a recognized terminal state.

The assistant also extended the fix beyond the monitor itself. In messages 4878–4880, the VastSummary struct in the agent API layer was updated to include CurState and NextState fields, ensuring that the LLM-powered agent could see error details when making scaling decisions. This completed the three-layer fix: data model → monitoring policy → agent visibility.

The Deeper Significance: Brittle Monitoring in Autonomous Systems

This message, for all its brevity, illuminates a fundamental challenge in building reliable autonomous infrastructure management. The original monitor code was written with a reasonable assumption — that actual_status from the vast.ai API would be the authoritative source of truth about instance health. But production systems have a way of exposing the gaps in any single-source-of-truth approach.

The vast.ai API's state model is nuanced. An instance can be:

Input Knowledge Required

To understand the significance of message 4877, one needs several layers of context:

  1. The vast.ai API semantics: Understanding that actual_status, cur_state, next_state, and intended_status are distinct fields with different meanings. actual_status reflects the scheduler's view; cur_state reflects the hypervisor's view.
  2. The monitor's existing logic: The hard policy in the monitorLoop function that iterates over the vast cache and decides which instances to destroy. The original code only checked actual_status against a fixed set of strings.
  3. The Go data model: The VastInstance struct and how JSON deserialization works — fields not declared in the struct are silently dropped, meaning the data was available from the API but invisible to the application.
  4. The economic stakes: Storage charges accrue on stopped instances. Instance 33032537 was costing $0.148/hour while doing no work. Across a fleet, undetected stuck instances can add significant monthly costs.
  5. The agent architecture: The LLM-powered agent makes scaling decisions based on data from the Go backend. If the backend doesn't surface error information, the agent can't make informed decisions about whether to kill or keep instances.

Output Knowledge Created

Message 4877 produced a concrete operational improvement: the monitor's hard policy now detects instances in the "loading but actually stopped" state and destroys them. This means:

Mistakes and Incorrect Assumptions

The original implementation made several assumptions that proved incorrect:

  1. actual_status is sufficient for health detection: This was the root cause. The vast.ai API uses actual_status as a high-level status, but it can report "loading" for instances that are functionally stopped. The monitor needed to cross-reference cur_state to get the true picture.
  2. The vast.ai API fields are stable: The assistant initially assumed that status_msg would contain error details, but for instance 33032537, status_msg was None. The actual signal was in the mismatch between actual_status and cur_state.
  3. Instances not in the local DB are irrelevant: The assistant noted that instance 33032537 was "NOT in our DB — it was probably created by the user directly or the agent, but never registered with our manager." The monitor iterates over the vast cache (all instances visible on the vast.ai account), not just the local database, so this wasn't a blocker — but it highlighted a gap in the registration flow.

The Thinking Process: From Observation to Fix

The assistant's reasoning in messages 4870–4873 reveals a methodical diagnostic process:

  1. Verify the report: SSH into the management host and query the vast.ai API directly to confirm the instance's state.
  2. Identify the discrepancy: Compare actual_status=loading with cur_state=stopped. Recognize that the monitor only checks actual_status.
  3. Check the data model: Read the VastInstance struct to see which fields are parsed. Discover that CurState and NextState are missing.
  4. Plan the fix: Three steps — add fields to struct, update monitor logic, update agent API.
  5. Execute surgically: Two edits to main.go (struct + monitor), one edit to agent_api.go (agent visibility). This is a textbook example of debugging a monitoring blind spot: the data was always available from the upstream API, but the application's data model had never been extended to capture it, and the monitoring logic had never been updated to use it. The fix required no new API calls, no new infrastructure, and no changes to the vast.ai integration — just the addition of two JSON fields and a conditional check.

Conclusion

Message 4877 is a reminder that the most impactful production fixes are often the simplest in terms of code change, but the most complex in terms of the reasoning required to arrive at them. A single edit to a Go source file — adding a check for cur_state=stopped to a monitoring loop — closed a blind spot that was allowing dead instances to accrue charges indefinitely. The message itself is terse, but the chain of investigation that produced it — from user report to API query to struct inspection to policy update — represents the kind of deep systems thinking that separates robust autonomous infrastructure from brittle automation.