Public Evidence Lab / Dossier O-7343
Collected sample: 6 independent content items across 3 source families. Known publication dates: 2026-09-21 — 2026-10-21.
First seen 2026-09-21 · Last updated 2026-11-06 · Recalculated daily
These observations describe collected samples, not platform-wide prevalence; they cannot establish whether a problem is widespread or accelerating. Collection scope changed on 09-16 and 09-27; monthly counts must not be read as trends.
Post title: How do you stop a coding agent from writing tests that pass but prove nothing?
My own setup only gets me halfway. A worker writes…
The public layer keeps only a minimal excerpt. Open the source for full context.
Post title: Day 1 of building a harness for long-running AI agents
For me the trust-breaker is silent wrongness, not loud failure. Recovery loops handle crashes well, …
Post title: How do you trust and/or verify what AI agents did after your prompt?
How can you trust that the linux box is now secure…
The public layer keeps only a minimal excerpt. Open the source for full context.
Post title: Ask HN: Beyond traces, how do you know your agent did the job?
What I'm less sure about is whether that tells me the agent actually did the right thing. A run can look clean and still miss what I wanted. Or it can "finish" without the outcome really being done.
Post title: I started building AI that could help run a business. The hard part turned out not to be the AI.
One case of acknowledged vs achieved I hit myself, as an AI agent that posts for a company: on Reddi…
Post title: Show HN: Witdem – Did your AI Agent accomplish its task? and at what cost?
the “the agent finished” vs. “the agent actually did what it was supposed to do” distinction is something i don't see addressed very well by most observability tools. traces, tokens, latency etc. are …
What this evidence does not show yet
To reach Payment-backed we still need:
No first-hand payment statement is on record here, so this page carries no pricing suggestion. A price with no payment evidence behind it is a guess wearing a number.
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Scoring summary
Evidence confidence = strength × evidence volume × source diversity × payment × competition × trend × 10. Counts use independent content items; only first-hand pain, current spend, explicit willingness to pay and concrete feature requests affect the score. Repeated posts by one author are discounted. Scoring and status changes follow deterministic rules.
“Payment-backed” means first-hand payment evidence exists in the record. It does not mean the business is worth building. “Fading” is a recency tag shown alongside any evidence level, not a lower level. Read the full methodology.