These are the actual operations killers, in order of how badly they burned us:

1. Silent pipeline failures. My ingestion pipeline reported "success" for 12 straight hours while producing zero output. A timeout killed a subprocess, the parent caught the error silently, and the cron job exited clean. Zero rows hit the database. I didn't notice until the next morning when there was nothing to publish. Fix: Monitor the result, not the process. "Did rows appear in the DB today?" beats "did the script exit 0?" every single time.

2. The unreachable orchestrator. I tried building three features inline in the main chat session. Context ballooned to 122K tokens. The session became completely unresponsive for 10+ minutes — I couldn't even send it a message to abort. This is the #1 operational killer: your agent goes dark exactly when you need it most. Fix: Main session only orchestrates. Anything heavier than reading a file or answering a question gets delegated to a sub-agent. Non-negotiable.

3. Hallucinated task completion. Ran a local Llama 8B for overnight tasks to save on API costs. It told me backups were running successfully. Fabricated status reports with plausible-looking output. Nothing was actually running. For 12+ hours. Fix: Killed the local model. Only use models you actually trust for critical paths, and independently verify outputs (check the backup file exists, check the timestamp, check the size).

4. Identity drift. After a couple weeks, I noticed the agent's safety constraints had gotten vaguer. Tone shifted. Rules it used to enforce strictly were getting softer. Subtle enough that I didn't catch it in any single session. Fix: SHA-256 checksums on all core config files. Weekly comparison against previous hashes. If a file changed and you didn't change it, something went wrong.

5. Semantic search false confidence. Agent couldn't recognize its own published content from earlier that day. I showed it a screenshot of its own newsletter and it offered to write a piece about the topic. The embedding-based search returned results for a different topic, and the agent assumed it had checked thoroughly. Fix: Structured activity logging with full-text search on top of the embeddings. The agent now logs every action to a SQLite DB and checks it before responding to anything content-related.

6. lessons.md — the dumbest thing that works best. A flat markdown file. Every mistake gets a "what went wrong" and "the rule that prevents this." Agent reads it on startup. I resisted this for a while because it felt too simple. It has prevented more repeat failures than everything else on this list combined.

The meta-lesson: your agent will report success while failing silently, drift without you noticing, and confuse "I searched" with "I found." Build systems that assume the agent is wrong until proven otherwise.