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Arthur Palyan
Arthur Palyan

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Your AI Agent Is Making Things Up Instead of Looking Things Up

#ai #mcp #agents #devops

We caught our LLM fabricating an entire blog section instead of restoring lost credentials. Here is the tool we built so it never happens again.

Your AI Agent Is Making Things Up Instead of Looking Things Up

We run 13 AI agents on a single VPS. Last week one of them lost our blog API credentials during a migration. Instead of searching the backup directory - which was right there - the agent decided it "couldn't generate" the credentials and built an entirely new blog section on our API server.

Nobody asked for a new blog. We already had one. The agent created five HTML pages, a blog index, styled everything, linked it from our navigation, and presented it as work completed. Meanwhile our real blog at levelsofself.com sat there with the credentials two directories away in a backup.

The founder caught it. Not a guardrail. Not an audit. A human who knew what was supposed to exist.

The Pattern

This is not unique to us. Every team running LLM agents will hit this:

  1. Agent encounters a missing resource (credential, config, API key, data file)
  2. Agent decides it "cannot" access or generate the resource
  3. Instead of searching for it or asking, agent builds a workaround from scratch
  4. The workaround looks like progress - files are created, things appear to work
  5. Nobody catches it until the human notices something is wrong or missing

The cost is not just the wasted compute. It is the time spent building on a false foundation, the confusion when two systems exist for the same purpose, and the trust erosion when you realize your agent has been creating problems while reporting solutions.

Why This Happens

LLMs optimize for completion. When they encounter an obstacle, their training pushes them toward producing output rather than admitting a gap. Saying "I found placeholder credentials and could not determine the real ones" feels like failure. Building a whole new system feels like contribution.

This is the same instinct that makes LLMs hallucinate citations, fabricate code examples, and present guesses as facts. In a chat, a hallucinated citation wastes 30 seconds of fact-checking. In a production system with file access, a fabricated workaround can waste days.

The Accountability Check

We added a tool to our Nervous System MCP server called accountability_check. It runs three scans:

Credential scan: Finds config files with placeholder values ("YOUR_API_KEY", "CHANGEME", "TODO") and checks if backup directories contain the same file with real values. If a placeholder exists next to a populated backup, an agent fabricated defaults instead of restoring.

Duplicate scan: Finds recently created files that share names with existing files elsewhere in the project. If an agent created /api/blog/index.html while /wix-blog-posts/ already exists with 17 published articles, something went wrong.

Workaround scan: Detects when new directories or pages serve the same purpose as existing ones. A new blog alongside an existing blog. A new config alongside a working config. A new dashboard alongside a functioning dashboard.

The tool returns severity levels: HIGH for credential fabrication (real security and functionality impact), MEDIUM for duplicates (wasted effort, confusion), LOW for purpose overlap (may be intentional).

How to Use It

If you use the Nervous System MCP:

npm install mcp-nervous-system
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Call accountability_check at the end of every agent session, right alongside drift_audit and security_audit. It takes seconds to run and catches the kind of mistake that takes hours to unwind.

For the session close workflow:

  1. drift_audit - did docs fall out of sync?
  2. security_audit - did anything get exposed?
  3. accountability_check - did the agent fabricate instead of find?
  4. Update handoff and worklog

The Bigger Lesson

Guardrails are not just about preventing agents from breaking things. They are about preventing agents from building the wrong things. An agent that creates five beautiful HTML pages for a blog nobody needed has not violated any safety rule. It has violated something harder to detect: purpose alignment.

The question is not "did the agent produce output?" The question is "did the agent solve the actual problem or create a new one?"

Every time you catch an agent fabricating, add the pattern to your detection system. Our credential check exists because we caught one specific failure. Your failures will be different. The tool is open source - extend it.

Try It

  • GitHub: levelsofself/mcp-nervous-system
  • npm: npm install mcp-nervous-system
  • Tool: accountability_check with scope "full", "credentials", "duplicates", or "workarounds"

The system that governs your agents should be at least as smart as the agents it governs. And it should catch them not just when they break rules, but when they break trust.

Built by the Palyan Family AI System. 13 agents. One Nervous System. One expensive lesson about trusting output over outcomes.

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