The Disappeared Instance: Instrumenting the Edge Case in Autonomous Fleet Management

In message [msg 4679] of a sprawling coding session dedicated to building an autonomous LLM-driven fleet management agent for GPU proving infrastructure, the assistant performs what appears at first glance to be a mundane operation: it reads a section of a Go source file. The message consists of a single read tool call targeting lines 1617–1626 of /tmp/czk/cmd/vast-manager/main.go, annotated with the label "5. Any → killed (disappeared from vast)". This simple act of reading code is anything but trivial—it is the fifth step in a systematic campaign to instrument every state transition in the instance lifecycle, ensuring that the autonomous agent receives real-time notifications when machines in the fleet change status. The message captures a pivotal moment where the assistant bridges the gap between the system's internal monitoring logic and the agent's situational awareness, specifically at one of the most critical and easily overlooked edge cases: instances that vanish from the vast.ai platform without warning.

The Context: Making the Agent Aware of State Changes

The story begins with the user's directive in [msg 4659]: "The agent should also be notified when instance changes state, e.g. Params Ok->Running, or Any->Killed." This request came on the heels of a previous feature where the assistant had made the target proofs-per-hour an editable UI setting that automatically notified the agent of configuration changes. The user was extending the same principle—the agent should not operate in a vacuum; it should be aware of what is happening to the machines it manages.

The assistant responded by creating a helper function notifyAgentStateChange in agent_api.go ([msg 4665]) and then methodically working through every code path where an instance transitions between states. The transitions identified were:

registered → params_done: When parameters are fetched and benchmarking begins
params_done → bench_done or params_done → killed: When benchmarking completes (pass or fail)
bench_done → running: When a benchmarked instance starts proving
Any → killed (UI manual kill): When an operator manually destroys an instance
Any → killed (disappeared from vast): When an instance vanishes from the vast.ai API
Any → killed (monitor timeouts): When the monitor loop detects a stalled instance
Failed bench kill: A safety-net kill for instances that passed benchmark but shouldn't have Message [msg 4679] is the fifth item in this checklist, and it targets perhaps the most insidious failure mode in the entire system.

Why "Disappeared from Vast" Matters

The transition "Any → killed (disappeared from vast)" is not a normal lifecycle event. It represents instances that were running on the vast.ai marketplace but have silently ceased to exist from the platform's perspective. This can happen for many reasons: the underlying VM was terminated by the host, the GPU experienced a hardware fault, the instance ran out of its memory budget and was killed by a platform watchdog, or the vast.ai API simply stopped reporting it due to a network partition.

In the earlier chunk of this session ([chunk 32.0]), the assistant had diagnosed a production crash where multiple nodes reported cuzk daemon crashes with no automatic recovery. The root cause was eventually traced to vast.ai enforcing a separate mem_limit via a host-side watchdog that silently terminated processes. Instances that "disappear" are often the same phenomenon—the machine is killed externally, and the only signal is its absence from the API response.

This makes the notification critical. Without it, the agent might see a fleet of machines that were running and healthy in its last observation, then suddenly find them gone. It needs to know why they disappeared to make informed decisions about whether to launch replacements, adjust demand projections, or alert a human operator. A machine that vanished due to an OOM kill is a very different signal from a machine that was deliberately shut down.

The Code Being Read

The code that the assistant reads reveals the detection mechanism:

activeRows, err := s.db.Query(`SELECT uuid, label, state FROM instances WHERE state != 'killed'`)
// ...
for activeRows.Next() {
    var uuid, label, state string
    activeRows.Scan(&uuid, &label, &state)
    if _, ok := lookupVast(label, labelMap, idMap); !ok {
        log.Printf("[monitor] instance disappeared from vast uuid=%s label=%s state=%s", uuid, label, state)
        s.db.Exec(

The monitor loop queries all non-killed instances from the database, then checks each one against the current vast.ai API response (via lookupVast). If an instance's label is not found in the API response, it is considered "disappeared." The code logs the event and then (presumably) marks the instance as killed in the database. This is a reactive detection mechanism—it can only notice disappearance during the next monitor cycle, which means there is a window where the agent's view of the fleet is stale.

The assistant's goal is to inject a notifyAgentStateChange(uuid, state, "killed", "instance disappeared from vast") call at this point, so that the agent's conversation thread receives an immediate notification. This transforms a silent, asynchronous detection into an explicit event that the agent can act upon.

Systematic Engineering: The Checklist Mentality

What is most striking about this message is not the code itself but the methodology it reveals. The assistant is working through a numbered checklist of transition points, reading each one before editing it. This is visible in the message's opening line: "5. Any → killed (disappeared from vast) (line ~1623):" The numbering, the parenthetical line reference, and the consistent pattern of read-then-edit all point to a deliberate, systematic approach.

This is not a haphazard patch job. The assistant has identified every code path that changes instance state—both the happy path (registered → params_done → bench_done → running) and the failure paths (benchmark failure, manual kill, external disappearance, monitor timeout). Each one gets the same treatment: a notification call that records the transition in the agent's conversation history.

The systematic approach is particularly important because the state machine is distributed across multiple handlers and the monitor loop. Missing even one transition would create a blind spot where the agent could make decisions based on stale or incomplete information. For example, if the "disappeared from vast" transition were not instrumented, the agent might continue to count that instance in its capacity projections, leading to over-provisioning or, worse, under-provisioning when demand spikes.

Assumptions and Knowledge Required

To understand this message, one must know several things:

The notifyAgentStateChange function: Created in [msg 4665], this helper writes a structured message to the agent's SQLite conversation table. Its signature is notifyAgentStateChange(identifier, fromState, toState, reason).
The monitor loop architecture: The vast-manager runs a periodic monitor that reconciles the database state with the vast.ai API. The lookupVast function checks whether a given label appears in the current API response.
The instance lifecycle: Instances progress through states (registered → params_done → bench_done → running → killed), but can also transition directly to killed from any state via external termination.
The agent conversation model: The agent's "memory" is a conversation thread stored in SQLite. State change notifications are injected as user messages that the LLM reads on its next cycle. The assistant assumes that lookupVast is reliable—that if an instance is truly gone from vast.ai, it will not appear in the API response. This is a reasonable assumption but not foolproof: API rate limiting, transient network errors, or pagination issues could cause false positives. The assistant does not add any debouncing or confirmation logic at this point, accepting the risk of spurious notifications.

Output Knowledge Created

This message, combined with the edit that follows in [msg 4680], creates a critical piece of infrastructure: the agent will now be notified when instances vanish from the platform. This transforms the agent's awareness from a periodic snapshot (what was running at the last observation) to an event-driven understanding of fleet dynamics. The agent can now distinguish between "I deliberately shut down those instances" and "those instances were killed by an external force," enabling more intelligent responses such as throttling new launches if multiple instances are disappearing simultaneously.

Conclusion

Message [msg 4679] is a deceptively simple read operation that reveals the depth of engineering required to build reliable autonomous systems. It is not enough to build an agent that can make decisions; the agent must be grounded in real-time, accurate information about the state of the world. By systematically instrumenting every state transition—including the silent, external disappearance of instances—the assistant ensures that the agent's decisions are based on complete information rather than dangerous assumptions. The "disappeared from vast" transition is the canary in the coal mine for infrastructure failures, and making the agent aware of it is a critical step toward operational resilience.