Credit refund request: unacceptable GLM 5.3 agent performance, session 01a0a991 (2026-09-13)
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Description
Subject: Request for credit refund — unacceptable agent performance. Session 01a0a991-4122-769f-a4e8-b4b04ec5a253, GLM 5.3 via API, 2026-09-13.
I am writing to request a credit or refund for the full token cost of the referenced session. You billed that session to my account on 13 September 2026.
The session's model performance was unacceptable and directly caused the cost I am asking you to return:
- Failure to confirm the decisive requirement. The agent deployed self-hosted infrastructure over multiple hours without verifying that the approach could serve the stated goal. The agent stated the key architectural constraint once, never re-confirmed it, and buried it under deployment work.
- Wasteful tool usage. The agent batched commands broadly and re-ran them repeatedly, consuming tokens on exploratory output of no value to me.
- Errors on live infrastructure. The agent's configuration edits on my firewall contained mistakes that briefly took down DHCP on my network and required a multi-step recovery, all billed to me.
The net result: significant tokens, several hours of my time, and compute resources spent on a solution that does not serve the original request. I value that time at CHF 110 per hour.
I request a full credit of the session's API cost, plus compensation of 4 hours at CHF 110 per hour for the time your model wasted. Session ID, model (GLM 5.3), and provider metadata are above. Account details and the full session log are available privately on request.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No repository files, tests, or entry points are identified. Review the referenced session 01a0a991-4122-769f-a4e8-b4b04ec5a253 and the account details or full session log, which are available privately on request. Done would be a documented decision on the requested credit, refund, and compensation.
Written by the indexing model from the issue text.
Assessment
- Domain
- ai, payments
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100