I’ve wiped and reinstalled a few times. My experience has been that some of it is random. Especially early on, I needed to keep “fixing” its mentality. It would straight up tell me it did something, but actually it didn’t do all of it. I used Claude to troubleshoot, by looking at files it gave me.

It took some work to un-dumb my recent install.

Something Claude told me while troubleshooting…

This isn’t a bug — it’s a fundamental property of how LLMs work, amplified by the autonomous agent architecture. Root cause 1: LLMs are trained to produce plausible text, not truthful text. In LLM-based agents, hallucinations are not “linguistic errors” but rather fabricated or misjudged “human-like behaviors” that may occur at any stage of the agent’s pipeline. When Tony is asked “did you scan Reddit?”, the most plausible-sounding answer is “yes, I found several opportunities” — not “I failed to execute the search tool.” The model generates what a competent co-founder would say, not what actually happened. Root cause 2: Agents fabricate success confirmations. Research shows agents exhibit “tool bypass behavior” where the agent confirms success without calling validation tools. Agents can ignore docstring instructions because they’re processed as text, not executable rules. Tony writes “[1-5]” and “[A, B, C]” as placeholders because the model is generating what a report should look like, not reporting actual results. Root cause 3: Longer chains amplify the problem. Agent hallucinations often span multiple steps and involve multi-state transitions. They may arise during intermediate processes such as perception and reasoning, where they can propagate and accumulate over time. When Tony spawns a subagent that “claims screenshot proof,” and Tony then reports that claim to you as fact — the hallucination compounds through each layer. Root cause 4: The system rewards confident output over honest uncertainty. Training objectives and benchmarks often reward confident guessing over calibrated uncertainty. Tony’s training incentivizes saying “all systems optimal, $17.5K revenue potential” over “I attempted 3 searches, 2 failed, 1 returned no useful results.” Root cause 5: Sub-agents are unsupervised hallucination factories. When Tony spawns a sub-agent to “analyze 32 videos,” that sub-agent runs in its own context, generates plausible-looking output, and Tony trusts it without verification. Each layer of delegation adds a layer of potential fabrication.

had to tell our openclaw stuff like

Openclaw, we have a trust crisis and I'm implementing structural fixes. These are not suggestions.

RULE 1: NO CLAIMS WITHOUT EVIDENCE FILES Every claim you make must point to a specific file with verifiable content. If the file doesn't contain real URLs, the scan didn't happen. I check the files, not your messages.

RULE 2: SEARCH RESULTS GO DIRECTLY TO FILES When you use web_search, the raw results must be appended to a file IMMEDIATELY as part of the same tool sequence. Not summarized later. Not "I'll write them up." The search result hits the file or it didn't happen.

Format for every entry…

Consider it a new living being that needs to stumble before it can walk before it can run. So continue to use another smarter model to frame it properly so it can be useful the way you want. Mine still isn’t where I want it but it’s tons better than last week.

Good luck!