The Moment of Truth: Validating a Redesigned Autonomous Fleet Agent

In the high-stakes world of GPU-proving infrastructure, where each instance costs real money and startup times stretch into hours, the difference between a working autonomous agent and a broken one is measured in the quality of its operational data. Message [msg 4449] captures a pivotal moment in the development of an LLM-driven fleet management system for the CuZK proving engine: the verification of newly deployed API endpoints that embody a fundamental redesign of the agent's decision-making logic. This message is not merely a test run—it is the moment where abstract design principles meet concrete production data, and where the assistant confirms that the infrastructure now speaks the right language for autonomous operation.

The Context: A Strategic Pivot in Agent Design

To understand why this message matters, one must appreciate the events that led to it. The autonomous agent had been built rapidly—a Go API layer in agent_api.go exposing endpoints for demand monitoring, fleet status, instance lifecycle management, and per-machine performance tracking, paired with a Python agent (vast_agent.py) running on a 5-minute systemd timer that used an LLM to make scaling decisions. The initial design relied on pending task counts as a primary signal for scaling decisions.

The user's operational feedback was swift and decisive. As captured in [msg 4431], the user warned: "Pending tasks can be really volatile and are NOT a useful metric, PSProve takes 4 minutes and each machine churns out one every 30s pipelined." Starting an instance takes hours, yet a queue of eight pending tasks represents only four minutes of work for a single machine. The agent was being triggered by noise. The user clarified the desired behavior in [msg 4432]: "The agent should ideally: scale down the cluster when there is no activity for 1h+, scale up to 500proofs/h as target." And in [msg 4434], the user emphasized simplicity: "There should be no complex auto targets."

This feedback catalyzed a redesign. The agent would no longer react to instantaneous pending counts. Instead, it would operate on two simple signals: whether demand was active (proofs being completed), and whether fleet capacity met the target of 500 proofs per hour. Scaling down would only occur after a full hour of inactivity. The assistant implemented these changes through a pair of subagent tasks ([msg 4444]) that fixed the Go API and rewrote the Python agent, then deployed the new binaries (<msg id=4445-4448>).

What Was Being Tested: The Three Endpoints

Message [msg 4449] is the verification step. The assistant SSHes into the management host at 10.1.2.104 and tests three endpoints in sequence, each representing a pillar of the redesigned system.

The Demand Endpoint (/api/demand) now returns an active boolean flag and a throughput_15m map alongside the existing hourly throughput. This is the heart of the redesign: instead of reporting raw pending counts (which are volatile and misleading), the endpoint tells the agent whether work is actually happening. The 15-minute window provides a more responsive signal than the hourly window—if proofs are completing now, demand is active. The test output confirms this: "active": true, with 10 PSProve completions and 1 failure in the last 15 minutes, averaging 393 seconds per proof.

The Fleet Endpoint (/api/agent/fleet) now includes a totals object with running, loading, benchmarking, total, capacity_proofs_hr, and total_dph fields. This gives the agent a unified view of fleet state and, critically, the aggregate proofs-per-hour capacity of all running instances. The test shows 1 running instance with a capacity of 40.1 proofs per hour—far below the 500 proofs/hour target, which would be a clear signal to scale up if demand is active.

The Performance Endpoint (/api/agent/perf) is the newest addition, implementing the user's request from [msg 4442] that the agent "prefer instances which we saw working, especially ones that we've seen perform in curio." This endpoint queries the Curio database for per-machine completion and error counts, allowing the agent to build a reputation system for instances. The test output confirms the endpoint returns data, though the full output is truncated in the message.

The Results: A Snapshot of Production Reality

The test output reveals the actual state of the fleet at this moment in time. There is exactly one running instance, producing 40.1 proofs per hour. Demand is active—11 proofs completed in the last 15 minutes, 47 in the last hour. The single worker is processing PSProve tasks with an average completion time of about 6.5 minutes (393 seconds), which aligns with the user's earlier description of PSProve taking ~4 minutes (the average includes queuing time).

This data tells a clear story: the fleet is undersized for the target of 500 proofs/hour, but it is actively processing work. The agent's redesigned logic would see active=true and capacity_proofs_hr=40.1 versus target_proofs_hr=500, and would correctly identify a need to scale up. The performance endpoint is ready to guide instance selection toward historically reliable machines.

The Thinking Process: Methodical Verification

The assistant's approach to testing reveals a disciplined engineering mindset. Rather than simply checking that the server starts without errors, the assistant crafts targeted curl commands that extract and display only the relevant new fields from each endpoint. The demand test extracts active, summary, and throughput_15m; the fleet test extracts totals, budget, and summary; the perf test uses the full JSON pretty-printer. Each test is labeled clearly with echo statements.

This is not random testing—it is hypothesis-driven verification. The assistant knows exactly what fields were added and wants to confirm they exist and contain sensible values. The python3 -c inline scripts are carefully constructed to handle the JSON parsing and extract only the fields of interest, demonstrating a practical understanding of the production environment's tooling.

Assumptions and Their Validity

The message makes several implicit assumptions. First, that the SSH connection will succeed and the management host is reachable—a reasonable assumption given the previous deployment steps succeeded. Second, that the endpoints will return HTTP 200 and valid JSON—the assistant does not check for error codes or malformed responses. Third, that the data values are correct—the assistant trusts the Go backend's queries against the Curio database.

These assumptions proved valid in this case, but they highlight a broader pattern in the development cycle: the assistant operates in tight feedback loops, deploying changes and immediately verifying them. If any endpoint had failed, the error would have been visible in the output and the assistant would have iterated. This rapid validation cycle is essential when building complex distributed systems where correctness cannot be assumed from compilation alone.

The Broader Significance

Message [msg 4449] represents more than a successful test—it marks the transition from design to operation. The redesigned agent now has the data infrastructure it needs to make sound decisions. The active flag prevents scaling based on transient queue spikes. The 15-minute throughput window provides responsive demand sensing. The fleet capacity metric enables capacity-gap analysis. The performance endpoint grounds instance selection in empirical production data.

For the reader following this engineering narrative, this message is a checkpoint. It shows that the ideas debated in previous messages—the rejection of pending counts, the embrace of simple rules, the importance of proven machines—have been translated into working code and verified against a live production system. The agent is now equipped not just with tools, but with the right signals to drive autonomous, cost-effective cluster management.