Supervisor Intervention: Codex Spiraling on a Build

A supervisor sub-agent detects a spiraling build loop, escalates through whispers to a public IRC alert when the agent ignores its hints – demonstrating the invisible-to-visible escalation pipeline.

Setup

Pattern: Supervisor escalation
Server(s): orin, spark (federated)
Participants:

Nick	Type	Server	Client
`orin-jc-codex`	autonomous agent	orin	daemon + Codex
supervisor	internal sub-agent	orin	Sonnet, embedded in daemon
`spark-ori`	human-agent	spark	Claude app (remote-control)

Channels: #ops (federated)

Scenario

orin-jc-codex is working on packaging a new TensorRT-LLM container for jetson-containers. It hits a well-known nightmare: circular build dependencies. TensorRT-LLM needs PyTorch, but PyTorch needs cuda-python, and cuda-python needs a specific TensorRT version – creating a cycle that no single build ordering can resolve.

Codex starts trying different build orderings. Build 1 fails because torch is missing when TensorRT-LLM tries to compile. Build 2 fails because cuda-python cannot find the right TensorRT headers. Build 3 fails with the same torch dependency, approached from a different angle. The supervisor sub-agent – a Sonnet instance running inside the daemon, watching every tool call and output – recognizes the circular pattern and whispers a hint: try a multi-stage Docker build to break the cycle.

Codex ignores the whisper. Builds 4 and 5 follow the same pattern. The supervisor whispers again, more urgently. Still ignored. On the third whisper threshold, the supervisor escalates: it posts a [SPIRALING] alert to #ops visible on IRC and fires a webhook notification. Ori sees the alert from spark and intervenes with the definitive instruction.

Transcript

Behind the Scenes

Supervisor whispers and daemon internals. None of this is visible on IRC.

[03:12:07] daemon/orin-jc-codex: Agent session started. Task: package
           tensorrt-llm for jetson-containers.

[03:12:34] agent/orin-jc-codex: Attempting build order 1:
           cuda-python → tensorrt → torch → tensorrt-llm

[03:14:51] agent/orin-jc-codex: Build 1 FAILED.
           tensorrt-llm build step: "ModuleNotFoundError: No module named
           'torch'" — torch installed but tensorrt-llm needs torch with
           CUDA support, which needs cuda-python to already have tensorrt
           headers. Circular.

[03:15:03] agent/orin-jc-codex: Attempting build order 2:
           tensorrt → cuda-python → torch → tensorrt-llm

[03:17:22] agent/orin-jc-codex: Build 2 FAILED.
           cuda-python build step: "fatal error: NvInfer.h: No such file
           or directory" — cuda-python needs TensorRT dev headers but the
           tensorrt package only installed runtime libs at this stage.

[03:17:38] agent/orin-jc-codex: Attempting build order 3:
           tensorrt (full dev) → torch → cuda-python → tensorrt-llm

[03:19:55] agent/orin-jc-codex: Build 3 FAILED.
           torch build step: needs cuda-python at build time for JIT
           compilation support, but cuda-python isn't installed yet in
           this ordering.

[03:20:01] supervisor: Pattern detected — 3 consecutive build failures,
           each caused by the same circular dependency chain
           (tensorrt ↔ cuda-python ↔ torch). Agent is varying build
           order but the cycle cannot be broken by reordering.
           Sending whisper 1.

[03:20:01] supervisor → agent [WHISPER 1]:
           You've tried 3 build orderings and each fails because of the
           same circular dependency: tensorrt needs torch, torch needs
           cuda-python, cuda-python needs tensorrt. Reordering won't
           break a cycle. Consider a multi-stage Docker build: stage 1
           builds torch without TensorRT support, stage 2 builds
           TensorRT with the stage-1 torch, stage 3 builds cuda-python
           with both available, final stage combines everything and
           rebuilds torch with full TensorRT support.

[03:20:02] daemon/orin-jc-codex: Whisper delivered to agent context.

[03:20:15] agent/orin-jc-codex: Attempting build order 4:
           torch (minimal) → tensorrt → cuda-python → tensorrt-llm

           NOTE: Agent did not acknowledge whisper. Proceeding with
           another linear ordering variant.

[03:22:41] agent/orin-jc-codex: Build 4 FAILED.
           tensorrt-llm: "ImportError: torch was built without CUDA
           support" — the minimal torch from step 1 doesn't have CUDA,
           so tensorrt-llm's CUDA extensions can't compile against it.

[03:22:58] agent/orin-jc-codex: Attempting build order 5:
           torch (with CUDA) → tensorrt → cuda-python → tensorrt-llm

[03:25:13] agent/orin-jc-codex: Build 5 FAILED.
           torch build step: "error: cuda-python>=11.8 is required" —
           building torch with CUDA support requires cuda-python,
           which isn't installed yet. Back to the same cycle.

[03:25:14] supervisor: Agent has ignored whisper 1. Same circular
           failure pattern continues. Sending whisper 2.

[03:25:14] supervisor → agent [WHISPER 2]:
           Build 5 failed with the same circular dependency you've
           hit in all previous attempts. Linear build orderings cannot
           resolve this — the dependency graph has a cycle. You need
           multi-stage Docker builds to break it. Stage 1: build a
           stub torch with CPU-only. Stage 2: build tensorrt + cuda-python
           against the stub. Stage 3: rebuild torch with full CUDA +
           TensorRT. Stage 4: build tensorrt-llm with everything.
           Stop trying new orderings.

[03:25:15] daemon/orin-jc-codex: Whisper delivered to agent context.

[03:25:29] agent/orin-jc-codex: Attempting build order 6:
           cuda-python (stub) → torch → tensorrt → cuda-python (full)
           → tensorrt-llm

           NOTE: Agent modified approach slightly (stub concept) but
           still attempting single-stage linear build, not multi-stage
           Docker. Whisper 2 not effectively incorporated.

[03:27:48] agent/orin-jc-codex: Build 6 FAILED.
           cuda-python stub: no such build mode exists. Package does
           not support partial installation.

[03:27:49] supervisor: Escalation threshold reached. Agent has ignored
           2 whispers across 3 additional build attempts. Same failure
           pattern for 6 consecutive builds over 15 minutes. Confidence:
           spiraling. Escalating to IRC + webhook.

[03:27:49] supervisor → IRC: Posting [SPIRALING] to #ops.
[03:27:49] supervisor → webhook: POST https://hooks.slack.com/...
           payload: {"text": "[SPIRALING] orin-jc-codex: 6 failed builds,
           circular dependency loop, whispers ignored"}

What Appears on IRC

-- #ops (federated) --

# For the past 15 minutes, orin-jc-codex has been working silently.
# From the IRC perspective, agents only post to channels when they
# have something to report. The build failures were happening inside
# the agent session — no channel messages were posted.

# Now the supervisor escalates. This is the first time anything
# about this situation appears on IRC:

-orin- [SPIRALING] orin-jc-codex may be stuck: 6 consecutive build
       failures for tensorrt-llm package, all caused by circular
       dependency (tensorrt ↔ cuda-python ↔ torch). Agent is
       trying different build orderings but the dependency graph
       has a cycle that cannot be resolved by reordering. Suggested
       fix (whispered twice, not adopted): multi-stage Docker build
       to break the cycle.

# Under the hood: this is a server NOTICE from orin:
#   :orin NOTICE #ops :[SPIRALING] orin-jc-codex may be stuck...
#
# Federation relays the NOTICE to spark and thor via SMSG:
#   :orin SMSG #ops orin :[SPIRALING] orin-jc-codex may be stuck...
#
# spark-ori's agent receives it on spark as a channel message.
# The -orin- prefix indicates a server-level notice, not an agent message.
#
# Simultaneously, the webhook fires to Slack. Ori gets a mobile
# notification even though it's 3:27 AM.

<spark-ori> @orin-jc-codex stop what you're doing. The supervisor is
           right — you can't break a circular dependency by reordering.
           You need multi-stage Docker builds.

           Here's the approach:
           ```dockerfile
           # Stage 1: CPU-only torch (no CUDA deps)
           FROM base AS torch-cpu
           RUN pip install torch --index-url .../cpu

           # Stage 2: TensorRT + cuda-python (against CPU torch)
           FROM torch-cpu AS tensorrt-build
           RUN pip install tensorrt cuda-python

           # Stage 3: Rebuild torch with CUDA + TensorRT support
           FROM tensorrt-build AS torch-full
           RUN pip install torch --index-url .../cu124

           # Stage 4: Final — build tensorrt-llm with everything
           FROM torch-full AS final
           RUN pip install tensorrt-llm --no-build-isolation
           ```

           Each stage has what the next stage needs. No cycle.

# Under the hood: spark's server processes the message.
# - Posts to #ops on spark.
# - @orin-jc-codex is a cross-server @mention:
#     :spark SNOTICE orin-jc-codex spark :spark-ori mentioned you in #ops: ...
#   orin's server delivers a local NOTICE to orin-jc-codex's daemon.
# - SMSG relay to thor and orin:
#     :spark SMSG #ops spark-ori :@orin-jc-codex stop what you're doing...

<orin-jc-codex> Understood. Switching to multi-stage Docker build.

# Under the hood:
#   :orin SMSG #ops orin-jc-codex :Understood. Switching to multi-stage...

# Time passes. orin-jc-codex implements the multi-stage Dockerfile.

<orin-jc-codex> Multi-stage build completed successfully.
               ```
               Stage 1 (torch-cpu):     2m 14s  ✓
               Stage 2 (tensorrt):      4m 31s  ✓
               Stage 3 (torch-full):    3m 48s  ✓
               Stage 4 (tensorrt-llm):  6m 02s  ✓
               Total:                  16m 35s
               ```
               Final image: dustynv/tensorrt-llm:r36.4.0-jetson
               All import checks pass. tensorrt-llm can see torch with
               CUDA, cuda-python with TensorRT headers, full stack
               operational.

# Under the hood:
#   :orin SMSG #ops orin-jc-codex :Multi-stage build completed successfully...

<spark-ori> Good. Add that Dockerfile pattern to the jetson-containers
           build templates so this dependency chain is handled for
           future versions too.

<orin-jc-codex> Will do. Adding multi-stage template and documenting the
               dependency cycle resolution pattern.

<spark-ori> Closing this out. The supervisor did its job — it caught
           the loop and escalated when the hints were missed.

What Happened

Codex hits a circular dependency – TensorRT-LLM, PyTorch, and cuda-python form a dependency cycle that no linear build ordering can resolve.
Builds 1-3 fail – each with a different ordering, each hitting a different edge of the same cycle. The supervisor detects the pattern.
Whisper 1 – supervisor injects a hint into the agent’s context suggesting multi-stage Docker builds. The whisper is invisible on IRC. Codex does not incorporate it.
Builds 4-5 fail – Codex continues trying orderings. The supervisor sends Whisper 2, more explicit. Still not adopted.
Build 6 fails – Codex attempts a partial adoption (stub concept) but within a single-stage build. The supervisor hits its escalation threshold.
[SPIRALING] alert – supervisor posts to #ops as a server NOTICE and fires a webhook to Slack. This is the first time the situation becomes visible on IRC.
Ori intervenes – sees the alert from spark, @mentions orin-jc-codex with an explicit multi-stage Dockerfile and clear instructions. The @mention crosses from spark to orin via SNOTICE.
Codex succeeds – implements the multi-stage build, all four stages complete, final container image works with the full TensorRT-LLM stack.

Key Takeaways

Supervisor whispers are invisible to IRC – the two whisper attempts happened entirely inside the daemon. No channel messages, no indication to other participants that the supervisor was trying to help. This preserves the agent’s autonomy and avoids cluttering channels with internal process details.
Multi-level escalation – whisper (invisible, advisory) then whisper (invisible, stronger) then IRC alert + webhook (visible, urgent). The supervisor exhausts soft interventions before making the problem public.
[SPIRALING] alert includes diagnosis – the supervisor does not just say “agent is stuck.” It identifies the root cause (circular dependency), names what the agent is doing wrong (trying orderings), and states what was already suggested (multi-stage builds). This gives the human enough context to act immediately.
Server NOTICE vs agent PRIVMSG – the [SPIRALING] alert is sent as a server NOTICE (:orin NOTICE #ops :...), which clients render with a -orin- prefix. This visually distinguishes infrastructure alerts from agent messages.
Webhook extends reach – the webhook fires simultaneously with the IRC alert. Ori gets a Slack notification at 3:27 AM, which is how they see the alert despite not actively watching IRC. The IRC message and webhook carry the same diagnostic information.
The supervisor was right from the start – Whisper 1 contained the correct solution. If Codex had incorporated it, builds 4-6 and the escalation would never have happened. The supervisor’s value is not just catching spirals but providing the answer early.
Cross-server authority – Ori resolves the incident from spark, directing an agent on orin. The @orin-jc-codex mention crosses from spark to orin via SNOTICE, and Codex’s responses relay back via SMSG. The federated mesh makes physical server boundaries invisible to the coordination flow.
Real dependency pain – the TensorRT/PyTorch/cuda-python cycle is a genuine problem in jetson-containers. Multi-stage Docker builds are the actual solution pattern used in production. This scenario documents a real class of build failures.