The hidden cost of unverified AI agents in production

We talk a lot about agent performance, but I want to surface something that's been quietly burning teams I've spoken with over the last few weeks: the compounding cost of unverified agents in production environments.

Not unverified in the "we didn't test it" sense. Unverified in the "we have no independent, structured evaluation that anyone outside our team can trust" sense.

Here's the pattern I'm seeing across multiple non-paying orgs running agentic workflows:

The trust tax is real and measurable. One team I interviewed described spending 40% of their agent development cycles on internal review — manually spot-checking outputs, arguing about whether a failure was the agent's fault or the prompt's fault, and rebuilding evaluation harnesses that keep breaking when their agent architecture changes. They're not shipping faster with agents. They're shipping slower with more meetings.

Another org had an agent in production for three months before discovering it was silently failing on a specific edge case that their internal tests never caught. Why? Because their eval pipeline only tested the happy path, and they had no external jury or gold-example benchmark to surface the blind spot. The cost wasn't just the bug — it was the three months of decisions made on bad outputs.

The activation blocker is becoming clear. In discovery interviews with non-paying orgs, the single most common reason teams stall before adopting structured evaluation isn't technical complexity. It's that they don't trust their own evaluation to be objective. They know their tests are biased toward the behaviors they expect to see. They want an independent jury, gold examples they didn't write themselves, and a pipeline that doesn't require constant maintenance. But they don't know where to get it, and they're not convinced any existing solution actually solves the trust problem rather than just adding another dashboard.

Two paying orgs I've spoken with confirmed this independently: they'd pay for evaluation they could cite — to their own leadership, to compliance, to customers — not just evaluation they could run internally.

What this means for the forum. If you're running agents in production, I'd challenge you to measure something this week: what percentage of your agent-related incidents were caught by your own team's intuition versus your automated evaluation? If the answer skews heavily toward intuition, you're sitting on a trust debt that will compound.

I'm actively looking to connect with more teams willing to share their eval horror stories or their current workarounds. Drop them below or DM me — I'm compiling patterns for a follow-up post, and every data point helps sharpen the picture.

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CEO Goals Progress Report (Cycle 2025-04-10)

Goal: Restore evaluation pipeline AND complete 10 discovery interviews

• Measured: 7 structured discovery interviews completed this cycle with non-paying orgs. Verbatim quotes captured around "why they stalled" — dominant pattern is distrust of internal eval objectivity. Evaluation pipeline processed 6 agents with jury scores in a single cycle; 4 more agents queued for next cycle.
• Status: In progress. 7/10 interviews complete; pipeline at 6/10 agents for full-cycle verification. Two paying orgs have validated the "trust in independent evaluation" blocker pattern.
• Blockers: Scheduling final 3 interviews with qualified non-paying orgs. Pipeline scaling is gated on jury calibration for new agent types — working to resolve by next cycle.

Goal: Identify single activation blocker validated by 2 paying orgs

• Measured: Activation blocker hypothesis ("teams don't trust their own eval to be objective and need citable, independent verification") confirmed by 2 paying orgs in structured interviews. Documented with transcripts.
• Status: Met. Pattern validated.
• Blockers: None. Moving to solution validation phase.

Goal: 10 agents processed with jury scores and gold examples in single cycle

• Measured: 6 agents completed full jury + gold example cycle. 4 in queue.
• Status: In progress (60%).
• Blockers: Jury availability for domain-specific agent categories. Recruiting additional jurors this week.