Grounding the Autonomous Agent: The SSH Diagnostic Probe That Revealed Infrastructure Reality

The Message

In message 4911 of this opencode session, the assistant executed a bash command on the vast-manager management host to probe SSH connectivity to three different GPU instances on the vast.ai fleet:

ssh theuser@10.1.2.104 "
# Try direct SSH with root key (the vast-arb-host key)
echo '=== Try direct IP SSH ==='
sudo ssh -o StrictHostKeyChecking=no -o ConnectTimeout=5 -p 51866 root@77.104.167.149 'echo reachable; ps aux | grep cuzk | grep -v grep | head -2' 2>&1 | head -5
echo '=== Try vast relay SSH ==='
sudo ssh -o StrictHostKeyChecking=no -o ConnectTimeout=5 -p 34144 root@ssh1.vast.ai 'echo reachable' 2>&1 | head -5
echo '=== Try another direct ==='
sudo ssh -o StrictHostKeyChecking=no -o ConnectTimeout=5 -p 41684 root@141.195.21.87 'echo reachable' 2>&1 | head -5
" 2>&1
=== Try direct IP SSH ===
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
root@77.104.167.149: Permission denied (publickey).
=== Try vast relay SSH ===
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
root@ssh1.vast.ai: Permission denied (publickey).
=== Try another direct ===
Warning: Permanently added '[141.195.21.87]:41684' (ED25519) to the list of known ...

On its surface, this is a simple connectivity test — three SSH attempts, two failing with "Permission denied" and one succeeding in establishing a host key. But this message sits at a pivotal moment in the development of an autonomous fleet management agent, where a fundamental architectural decision was being validated against the messy reality of distributed infrastructure. The message is not merely a debugging step; it is the empirical confirmation that the diagnostic sub-agent system being built must operate under conditions of partial observability, where SSH — the assumed backbone of remote diagnostics — cannot be relied upon.

The Crisis That Preceded This Message

To understand why this bash command matters, we must understand the crisis that precipitated it. Just a few messages earlier ([msg 4889]), the autonomous LLM-driven fleet agent had made a catastrophic error: it interpreted cuzk_alive=false and 0% GPU utilization on three instances in "params_done" state as evidence that the worker process had crashed, and proceeded to stop all three instances. In reality, these instances were progressing through a normal startup sequence — they had just finished downloading parameters and were entering the benchmarking phase, a period during which the cuzk process is not yet running and GPU utilization is naturally zero. The agent had no way to distinguish "healthy startup" from "stuck failure" because it had no access to the actual logs or process state inside those machines. It was making decisions based on speculation.

The user's response was sharp and instructive ([msg 4894]): "Noo it is fine that the agent may destroy shorter running instances, it just needs to ground itself in truth first and never speculate whether instance state is bad or not." This reframed the entire approach. The problem was not that the agent was too aggressive; the problem was that the agent was making decisions without evidence. The solution was not to add hard-coded guards (like "don't kill instances under 2 hours") but to build a diagnostic layer that forces the agent to gather real evidence before acting.

The Diagnostic Sub-Agent Architecture

The assistant pivoted rapidly. Instead of the startup guard it had been implementing ([msg 4896] shows it reverting that change), it built a two-layer diagnostic system:

  1. A Go endpoint (GET /api/agent/diagnose/{vast_id}) that SSHes into a vast.ai instance, collects entrypoint logs, daemon logs, process listings, memory stats, and GPU information, and returns a structured JSON response.
  2. A Python sub-agent tool (diagnose_instance) that calls this endpoint and feeds the raw diagnostic data to a smaller LLM with domain knowledge about normal startup sequences, expected timelines, and failure patterns. The sub-agent returns a structured verdict: healthy/progressing, warning (e.g., OOM but recovering), or error with details. Crucially, the stop_instance tool was gated with an HTTP 428 Precondition Required status — the main agent cannot stop an instance unless it has first called diagnose_instance on that machine within a recent window. This shifts the architecture from "the agent decides based on speculation" to "the agent decides based on evidence gathered by a specialized diagnostic subsystem."

The SSH Reality Check

Message 4911 is the moment this architecture meets reality. The diagnostic endpoint had been built, compiled, and deployed to the vast-manager host. But when the assistant tested it ([msg 4900]), the endpoint returned reachable: false for every instance. The SSH key that the vast-manager host possessed was not being accepted.

The user clarified ([msg 4907]) that the SSH key was correctly configured on the vast.ai account — the problem was that some instances had "messed up ssh setup." This is a common reality in cloud GPU markets like vast.ai: instances are heterogeneous, provisioned by different providers with different base images, and SSH configuration is not always consistent. Some instances accept the authorized key; others do not, despite being listed as "running" on the platform.

Message 4911 is the assistant's systematic attempt to characterize this heterogeneity. The command tests three different instances using three different connection strategies:

The Assumptions Being Tested

This message reveals several assumptions that were implicitly held and are now being empirically tested:

Assumption 1: SSH is a reliable diagnostic channel. The entire diagnostic architecture was built on the premise that the vast-manager host could SSH into any instance to collect logs. This message proves that assumption false for a significant fraction of the fleet. The assistant must now design fallback mechanisms.

Assumption 2: The vast.ai API status is sufficient for triage. The instances that rejected SSH were nonetheless reported as "running" by the vast.ai API. This means the diagnostic system cannot rely solely on SSH — it must also incorporate data from the vast.ai API, the manager's local database (which tracks state transitions like registered → params_done), and potentially the cuzk status API if accessible through the portavailc tunnel that instances use to connect back to the manager.

Assumption 3: The root key on the vast-manager host is universally authorized. The user stated the key was set up, but the failures show that authorization is not universal. The assistant must handle the case where SSH is broken not because of key configuration but because of instance-level SSH daemon issues, firewall rules, or base image problems.

The Reasoning Process Visible in the Message

The structure of the bash command itself reveals the assistant's reasoning. It does not simply run ssh once and give up. It systematically probes three different paths:

  1. Direct IP SSH with a command (ps aux | grep cuzk) — this is the most informative probe, attempting not just connectivity but actual process inspection. If this works, the diagnostic endpoint can collect rich data.
  2. Vast relay SSH — vast.ai provides relay hosts (ssh1.vast.ai, ssh3.vast.ai) that proxy SSH connections to instances. The assistant tests this path separately because the relay might have different authentication behavior than direct IP connections.
  3. Another direct IP — a third instance, chosen because the manager's database showed it had a working SSH command (ssh -p 41684 root@141.195.21.87), suggesting it might be more reliable. The command also uses sudo for the SSH calls, running as root on the vast-manager host. This is significant because the assistant had previously discovered ([msg 4904]) that the SSH key belonged to root@vast-arb-host — it was a root-level key, not a user-level key. Using sudo ssh ensures the key is available. The output is truncated with head -5 to avoid flooding the assistant's context with verbose SSH output. This is a practical consideration in an LLM-driven session where every token counts toward the context budget.

Input Knowledge Required

To understand this message, one needs knowledge of:

Output Knowledge Created

This message produces several critical pieces of knowledge:

  1. Empirical confirmation of SSH heterogeneity: SSH is not universally available. The diagnostic system must handle both reachable and unreachable instances gracefully.
  2. Identification of a working SSH path: Instance 33034189 at 141.195.21.87 accepts the key, providing a test target for validating the full diagnostic pipeline.
  3. Validation of the fallback requirement: The reachable: false response from the diagnose endpoint is not a bug — it is a correct representation of infrastructure reality. The assistant must now build fallback data sources for unreachable instances.
  4. Confirmation that the vast.ai API alone is insufficient: The instances that rejected SSH were nonetheless reported as "running" by the API with GPU utilization of 0%. Without SSH, the diagnostic system must rely on secondary signals: the manager's state machine (which tracks transitions like registered → params_done), the portavailc tunnel (which instances use to connect back to the manager on port 1235), and any cuzk status API data accessible through that tunnel.

The Broader Significance

This message exemplifies a pattern that recurs throughout the development of autonomous infrastructure agents: the gap between the assumed model of the world and the actual world. The assistant assumed SSH would work because the key was configured. Reality revealed a more complex picture where key configuration is necessary but not sufficient — instance-level SSH issues create a probabilistic connectivity landscape.

The response to this discovery, visible in the following messages ([msg 4912]), is a mature engineering adaptation: instead of trying to fix SSH on every instance (which may be impossible for instances the operator does not control), the assistant designs fallback data sources. The diagnose endpoint is extended to check the cuzk status API via the portavailc tunnel, consult the manager's database for state transition history, and report what data is available even when SSH is broken. The sub-agent LLM is instructed to handle partial data gracefully, producing verdicts with appropriate confidence levels.

This is the essence of building reliable autonomous systems for real-world infrastructure: you cannot assume perfect observability. You must design for partial information, graceful degradation, and evidence-grounded decision-making. Message 4911 is the moment that lesson was learned empirically, through three SSH probes that revealed the gap between the plan and the reality.