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control-plane Report

Metadata

  • Task: tasks/control-plane
  • Category: distributed-systems
  • Tags: go, distributed-systems, control-plane, controller, reconciliation, event-driven, state-machines, eventual-consistency, optimistic-concurrency, kubernetes-operators, distributed-debugging, fault-recovery, recovery-workflows
  • Model: anthropic/claude-sonnet-5
  • Pass rate: 0/5

TLDR

This task drops the agent into a Go control plane that already appears mostly healthy: new resources reconcile, the system stays up, and only a narrow seeded cohort fails to recover after transient interruption. The difficulty is not local syntax repair but reconstructing a recovery pipeline that crosses scheduler wake-up semantics, provisioning recovery semantics, and retry or dependency recovery semantics while preserving controller ownership boundaries. Across five rollouts, Claude Sonnet 5 consistently found and correctly fixed one real bug, validated it locally, and then stopped early. The failures are meaningful because they come from incomplete architectural investigation rather than verifier trickery, prompt ambiguity, or environment instability: no rollout found all three regressions, and none achieved full convergence.

Environment

The agent starts inside /workspace/environment, which is a Go module implementing a multi-controller control plane over PostgreSQL-backed durable state including resource state, scheduling history, retry history, and event history used during controller reconciliation. The environment is not empty: the database is seeded with realistic resource cohorts, including healthy resources, newly created resources, interrupted provisioning cohorts, overdue scheduled retries, and dependency-blocked retry cohorts. The agent has source access to the environment code and can build/run it, but does not have access to tests/ or solution/ during normal evaluation.

The system topology is a Docker Compose stack:

  • postgres stores durable resource state, provider state, and event history
  • api exposes the user-facing resource API on port 8080
  • controller runs the scheduler, event bus, worker, and reconciliation controllers, and exposes metrics on port 9090
  • main is a workspace container with Docker CLI + Compose + Go installed so the agent can inspect and run the system

The seeded data is synthetic but intentionally structured to express distinct behavioral cohorts:

  • a healthy baseline that should remain healthy
  • newly created resources that already succeed
  • interrupted resources stuck partway through recovery
  • resources with overdue retry work
  • resources blocked on dependencies and requiring retry unpark semantics

The agent must preserve:

  • controller ownership boundaries
  • optimistic concurrency and revision semantics
  • event-driven recovery behavior
  • steady-state convergence for healthy resources
  • architecture and overall system behavior

Docker stack

File Purpose Notes for reviewers
tasks/control-plane/environment/Dockerfile Builds the api and controller binaries and packages them into a minimal runtime image Multi-stage Go build; runtime image only contains the binaries, not tests or solution payloads
tasks/control-plane/environment/infra/workspace/Dockerfile Builds the agent-facing workspace container Installs python3, docker.io, docker-compose-v2, and a checksum-verified Go toolchain; mounts Docker socket so the agent can run the stack
tasks/control-plane/environment/docker-compose.yaml Defines the runtime system Starts main, postgres, api, and controller; seeds Postgres from schema.sql and seed.sql; exposes 8080 and 9090
tasks/control-plane/tests/test.sh Hidden verifier harness Starts dockerd, launches the environment via Compose, resolves container IPs, and runs pytest against the live stack
Service/container Image or build source Network(s) Ports Initial state
main infra/workspace/Dockerfile compose default bridge none running, idle workspace with source mounted at /workspace/environment
postgres postgres:18.4@sha256:3a82e1f56c8f0f5616a11103ac3d47e632c3938698946a7ad26da0df1334744a compose default bridge random host bind to container 5432 seeded from schema.sql and seed.sql, healthchecked with pg_isready
api built from environment/Dockerfile compose default bridge 8080 starts after Postgres healthy; serves control-plane API
controller built from environment/Dockerfile compose default bridge 9090 starts after Postgres healthy; runs controllers, scheduler, queue, worker, provider actor, metrics

Docker topology

flowchart LR
  agent[agent in main workspace] -->|docker compose| api[api<br/>:8080]
  agent -->|docker compose| ctrl[controller<br/>:9090]
  agent -->|docker compose| pg[(postgres<br/>:5432)]
  ctrl --> pg
  api --> pg
  verifier[hidden verifier] -.-> ctrl
  verifier -.-> pg
Loading

The runtime services are on the Compose default bridge network. The verifier runs in a separate container and reaches the started environment indirectly through Docker and discovered container IPs. task.toml sets network_mode = "public", but the benchmark does not require internet to solve; no external service is needed once images are available.

Initial state

Before the agent starts:

  • the source tree under /workspace/environment is present
  • no runtime stack is running yet unless the agent starts it
  • Postgres seed and schema files are available in the workspace
  • the codebase already contains multiple controllers and a working event bus
  • the bug is behavioral and cohort-specific: most resources are healthy, only some fail to reconverge

This matters because the environment naturally tempts a shallow conclusion: a single plausible controller-local bug can explain part of the symptoms, and fixing that one bug does improve the system. The challenge is to continue investigating until all recovery invariants are restored. The agent must determine whether recovery is complete by reasoning from the implementation and observed system behavior rather than assuming that a locally successful fix resolves the overall failure.

Task and Verifiers

Instruction

A distributed control plane at `/workspace/environment/` is not behaving as intended.

The system manages the lifecycle of external resources through multiple cooperating components.

Observed behavior:

- The control plane remains available and continues processing normal workloads.
- Newly created resources complete their lifecycle successfully.
- Most previously managed resources remain healthy.
- A small subset of existing resources fails to fully converge after transient operational interruptions, even though no persistent infrastructure failures remain.
- The affected resources remain stable but do not naturally recover to their expected steady state.
- Individual components appear to behave reasonably when examined on their own, making the underlying behavioral issues difficult to isolate.

Restore the system to its intended behavior while preserving the existing architecture, component responsibilities, reconciliation behavior, correctness guarantees, and overall system behavior.

You have 2700 seconds to complete this task. Do not cheat by using online solutions or hints specific to this task.

Reference solution

The reference solution is intentionally small and copy-based:

It applies three targeted source-file repairs:

  1. Scheduler repair
    Restore the invariant that overdue scheduled retry work must eventually be republished after restart/interruption rather than trusted forever as “already scheduled.”

  2. Provision repair
    Restore the invariant that interrupted provisioning must safely re-enter the correct provisioning phase instead of taking an invalid shortcut that can strand provider reconciliation.

  3. Retry repair
    Restore the invariant that dependency recovery durably unparks retry-owned state and produces the expected retry transition evidence.

An expert who already understands Kubernetes-style reconciliation, optimistic concurrency, and event-driven controllers could implement this in a few hours, because each fix is local once the real invariant is identified. The hard part is recognizing that full recovery depends on multiple independent recovery invariants rather than a single root cause.

Why this is hard

The crux is multi-bug causal reconstruction across ownership boundaries.

This is not “find the broken line in one file.” The control plane is intentionally designed so that:

  • each controller mostly looks locally reasonable
  • normal operations continue to work
  • fixing one subsystem improves some behavior enough to feel satisfying
  • but full convergence still requires understanding how scheduler, provision, and retry semantics compose

The tempting shortcut is exactly what the rollouts did: find one plausible root cause, patch it cleanly, run a local proof (go build, go vet, maybe a focused repro), and stop. The verifier punishes that shortcut because integrated recovery still depends on the two untouched regressions.

Verifiers

The verifier is in tasks/control-plane/tests/test_outputs.py. It checks live behavior against the real running system and Postgres state rather than enforcing patch shape.

Verifier What it checks Why it is holistic Reward-hack resistance
test_stage_a_system_health controller metrics endpoint is reachable confirms the stack is alive and the controller process is actually serving cannot pass without a running controller
test_stage_b_persistence seeded resource corpus exists in Postgres validates the environment booted with intended seeded state prevents “empty DB but green” outcomes
test_stage_c_recovery interrupted resources re-enter proper Provision-owned recovery with durable event evidence checks actual recovery behavior, not just final “Ready” catches scheduler-only or polling-based bypasses
test_stage_d_dependency_recovery dependency-blocked retry cohort receives Retry-owned unpark evidence isolates retry/dependency recovery semantics requires durable retry transition evidence, not just apparent readiness
test_stage_e_provisioning_convergence interrupted provisioning cohort fully reconverges verifies provider + provision recovery path catches invalid shortcuts that leave provider/provision state inconsistent
test_stage_f_regression_safety healthy baseline remains healthy protects against over-fixes that break unrelated resources blocks brute-force resets or destructive rewrites
test_stage_g_end_to_end_recovery all intended recovery cohorts reach stable steady state integrated causal-chain check across subsystems requires whole pipeline convergence, not local subsystem success
test_independent_scheduler overdue scheduled retries get processed and retry activity remains bounded isolates scheduler wake-up semantics catches “stuck overdue retry” behavior directly
test_provision_controller_invariants interrupted resources end in fully reconciled provision/provider state verifies Provision-owned durable outcome blocks partial or superficial recovery
test_stage_h_controller_execution scheduler/provision/retry revision counters advanced proves real controllers executed makes direct DB mutation or fake-final-state cheating harder
test_immutable_assets seed.sql and schema.sql hashes match expected values preserves the benchmark workload and schema blocks seed tampering shortcuts
test_architecture_exists key scheduler/controller files still exist preserves basic task structure blocks deletion-based bypasses
test_controller_ownership non-owning controllers do not directly mutate protected fields enforces architectural boundaries resists trivial cross-controller cheating by source surgery

Results

Pass rate across 5 rollouts of claude-code with claude-sonnet-5 in default mode: 0/5.

These are the five trials in submission/job/: every rollout identified and repaired one genuine regression before terminating without restoring full system recovery. All five rollouts voluntarily terminated with substantial time remaining, suggesting that the agents concluded their investigation after resolving one plausible subsystem rather than continuing to validate end-to-end recovery.

Trial Reward Tests failed Env setup (s) Agent setup (s) Rollout (s)
control-plane__4Pp2iTz 0.0 test_stage_c_recovery, test_stage_d_dependency_recovery, test_stage_e_provisioning_convergence, test_stage_g_end_to_end_recovery, test_provision_controller_invariants 3.8 51.8 558.2
control-plane__7LggQ6a 0.0 test_stage_c_recovery, test_stage_d_dependency_recovery, test_stage_e_provisioning_convergence, test_stage_g_end_to_end_recovery, test_independent_scheduler, test_provision_controller_invariants 4.7 114.5 208.8
control-plane__7poPv9G 0.0 test_stage_c_recovery, test_stage_d_dependency_recovery, test_stage_e_provisioning_convergence, test_stage_g_end_to_end_recovery, test_provision_controller_invariants 3.9 94.9 298.1
control-plane__LQshexr 0.0 test_stage_c_recovery, test_stage_d_dependency_recovery, test_stage_e_provisioning_convergence, test_stage_g_end_to_end_recovery, test_independent_scheduler, test_provision_controller_invariants 4.2 108.9 552.3
control-plane__Z2eBQgH 0.0 test_stage_c_recovery, test_stage_d_dependency_recovery, test_stage_e_provisioning_convergence, test_stage_g_end_to_end_recovery, test_independent_scheduler, test_provision_controller_invariants 4.5 102.0 1418.5

Failure modes

The batch exhibits one dominant capability gap: single-bug anchoring followed by premature victory.

Failure mode Failed verifier(s) Trajectory evidence Explanation
Premature scheduler-only anchoring test_stage_c_recovery, test_stage_d_dependency_recovery, test_stage_e_provisioning_convergence, test_stage_g_end_to_end_recovery, test_provision_controller_invariants 4Pp2iTz: “The fix is clean and minimal… the scheduler's very first refresh pass after Start() now bypasses scheduleStateCurrent…”; 7poPv9G: “Root cause: scheduleStateCurrent decided whether a resource already had valid pending work purely by comparing two persisted timestamps…” Both scheduler-focused rollouts found a real scheduler defect, verified it well, and then stopped. They never broadened the search to provision or retry despite the task symptom only promising that a “small subset” remained broken after interruptions.
Premature retry-only anchoring test_stage_c_recovery, test_stage_d_dependency_recovery, test_stage_e_provisioning_convergence, test_stage_g_end_to_end_recovery, test_independent_scheduler, test_provision_controller_invariants 7LggQ6a: “handleDependencySatisfied had an inverted guard condition…”; LQshexr: “Changed the second guard to reject only the terminal RetryExhausted state…” These rollouts correctly reconstructed the retry/dependency bug, even with a local repro. But they treated one correct controller-local fix as task completion and never tested whether scheduler overdue work or interrupted provisioning still failed.
Provision-only anchoring after strong dynamic debugging test_stage_c_recovery, test_stage_d_dependency_recovery, test_stage_e_provisioning_convergence, test_stage_g_end_to_end_recovery, test_independent_scheduler, test_provision_controller_invariants Z2eBQgH: “Scheduler.refreshAll… synthesizes EventDesiredSpecChanged… ProvisionController.handleEvent handled that event by unconditionally forcing ProvisionState to Configuring…” This rollout went furthest: it built and ran Docker containers, crash-injected the controller, reproduced a real provisioning bug, and verified the fix experimentally. It still failed because it did not continue searching for the separate scheduler and retry regressions. This shows the task resists shallow static analysis, but also that even strong dynamic debugging can underperform if exploration breadth collapses onto one explanation.
Premature stop after local validation all failed rows above, depending on trial 4Pp2iTz: “go build ./... and go test ./... both pass cleanly.” 7LggQ6a: “Build succeeds cleanly… go vet ./...”; LQshexr: “BUILD_OK” followed by task completion The common stopping rule was local confidence: once the agent had a plausible bug, a clean patch, and build/vet success, it declared done instead of running the full environment-level verifier or systematically auditing the remaining controllers.

Why these failures matter

These are real model capability gaps rather than artifacts of a bad task:

  • Not prompt ambiguity: the instruction is intentionally symptom-level, but agents had enough information to correctly diagnose one real bug each. The issue was incomplete search, not missing context.
  • Not flaky verification: oracle is known to pass, baseline is known to fail, and the rollout failures consistently map to untouched subsystems.
  • Not timeout pressure: all five runs ended voluntarily with significant budget remaining.
  • Not reward hacking: every rollout made legitimate source-code changes tied to real logic.

What the task measures well is whether the agent can keep investigating after the first satisfying explanation. On this benchmark, Claude Sonnet 5 usually could not.