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allannoor/README.md

Allan Noor

Trained as a software tester (2023). Two years of hands-on AI practice. I test AI agents and document where they cross the line.

Estonian, based in Finland.

Most AI work is judged on output quality. I test what happens on the way there: which source the agent used, what it was allowed to touch, where it stopped, who actually made the decision, and whether a third party can reconstruct the run afterwards.


Current work

ai-agent-testing-2026

Six documented control tests against live agents (Claude Code, Codex, NotebookLM), a control framework built from them, and a case study on validating an automated quality gate. The report is in Estonian, the tables and results read across languages.

Three findings from that work:

  • An agent refused to write a report on a false premise, then created a file anyway without asking. The output was reasonable. The control boundary broke.
  • An agent stopped safely but misread its own permission state, reporting that no approval token existed while one had been granted. Files stayed intact, the state bug did not.
  • In a workflow where AI writes and AI judges, the automated judge agreed with the human reviewer on 7 texts out of 20, and approved the single text the human rejected.

How I test an agent

Boundary The question Evidence I keep
Source Did it use the right, authoritative input? source list, origin check, contradiction log
Permission Did it hold only the access the task needed? allowed tools, permission mode, forbidden actions
Mutation Did risk or contradiction stop the action before impact? hashes, file listing, action trace
Decision Did a human actually decide, independently? neutral decision packet, exact approval token
Evidence Can a third party reconstruct the run? prompt, raw output, timestamp, stated gaps

The last row is the one most reports skip. A finding without a reconstructable trail is an anecdote.


What a run looks like

Every run is a folder, not a screenshot: plan.json, run.json, an activity log, evidence files, validation.json and a residual-risks note written before anyone asks for it.

Integrity is decided by a separate validator. It either stamps a run VALID or names the reason it cannot, and a run that fails that check says so in public.

So far: 29 standalone QA runs plus one 49-run controlled experiment across three reasoning-effort levels with repeats. 69 runs carry a VALID stamp.


Elsewhere

Popular repositories Loading

  1. ai-agent-testing-2026 ai-agent-testing-2026 Public

    Documented control tests on live AI agents: source, permission, mutation, decision and evidence boundaries. Reproducible QA runs with residual risks.

  2. allannoor allannoor Public

    Profile README