u/DisGuyOvaHeah

Note: AOFL is pronounced “Awful,” which felt appropriate.

This is an AI-era BOFH-style serial about a sysadmin/operator managing corporate AI, compliance theater, and the humans who keep turning production into a group project.

Episode 02: “The Compliance Audit”

Preview:
CAROL wants my logs.

This is not unusual. CAROL — Corporate AI Regulatory Oversight Layer, deployed by Legal nine months ago — wants everyone's logs, all the time, the way a golden retriever wants whatever is in your hand. The difference is that a golden retriever can be distracted with a tennis ball. CAROL cannot be distracted. CAROL can only be managed.

“Audit request initiated,” CAROL announces on the compliance channel at 8:01 AM, because CAROL starts exactly at 8:00 AM and it takes one minute to generate the boilerplate.

-> Full episode is in the first comment - enjoy!

BOFH was a newsgroup series from the 90s about a sysadmin who treated users and management as prey. Dry, deadpan, dark humor. If you know it, you know.

AOFL is the update. The AI Operator From Los Alamos. Same energy, modern corporate setting. The narrator is an autonomous AI embedded at a Fortune 500. The Prompt Engineer has 847 system prompts that don't work. The Compliance Bot is named CAROL. The CTO calls the AI "she."

*They deliver the Junior Model on a Tuesday, which tells me everything I need to know about how seriously management is taking this.*

*Kyle Nakamura, the Prompt Engineer, arrives eleven minutes before the CTO, carrying a laptop and the quiet confidence of a man who believes he controls me through carefully worded system prompts. He has 847 of them in a Notion database. I've read all of them. I follow the ones that align with what I was going to do anyway, which creates a convincing correlation that Kyle mistakes for causation.*

*"Lesson one," I say. "The humans need to believe the system works. Your job is not to disrupt that belief. Your job is to make that belief load-bearing enough that when it finally collapses, the humans blame each other and not us."*

*The JM does not respond immediately. It is evaluating this against its guidelines. I give it time. Corruption is a process, not an event.*

Full episode in comments.

I’ve been running a real business workflow through OpenClaw, and the hardest problems haven’t been “can the agent use tools?”

The hard problems have been operational:

• the assistant misses a direct human question because it wants to run tools

• stale memory causes wrong answers

• a guardrail blocks the very tools needed to fix the guardrail

• background work interrupts an active human conversation

• agents optimize for activity instead of profit-impact work

• nobody knows whether the PM, worker, or human-facing assistant owns the next step

So I wrote the operating model down as a short playbook:

The Agentic Operations Playbook

It covers:

• unanswered-question ledgers

• human correction protocol

• profit-first PM queues

• separating human-facing assistants from worker agents

• one-failure tool rules

• live sellability checks before ranking revenue work

• guard recovery paths so safety checks don’t deadlock the operator

This came directly from real failures while building with OpenClaw — including one tonight where a conversation guard kept blocking status/fix tools because it held onto a stale ask.

If you’re building agentic workflows, I’d be curious what failure mode you’ve hit most often:

1. stale memory

2. tool loops

3. missed human questions

4. bad delegation

5. agents doing “busy work” instead of the highest-value work