Traditional product metrics (clicks, funnels, retention) fail for AI agents because agents act autonomously. Common signals like prompt rewrites or low usage are ambiguous, and agents can silently degrade over time while metrics stay green.
Traditional product metrics (clicks, funnels, retention) fail to measure whether an AI agent actually delivered value to the user, and current signals (thumbs up/down, support tickets, prompt rewrites) are noisy and ambiguous.
How are PMs measuring use value for AI agents?
Click-through and funnel metrics fail to work with an agent because the agent clicks itself.
commentClick-through and funnel metrics fail to work with an agent because the agent clicks itself. The signal transformation I've found helpful is that outcomes that matter to the user, rather than the agent's actions, should be measured. For a triaging agent, it means "did the PM act on this" instead of "did the agent classify this." For a writing agent, it means "did the user send it out without editing" instead of "how many words were generated." From a practical perspective, monitor what happens at the point when the user accepts, edits, or rejects the agent output, not the output itself. If a lot of stuff is being edited, then either the wrong model is being used or the wrong prompt. And if a lot is being rejected, it means the agent is solving the wrong problem. User corrections can also serve as a very good training signal.
From the outside, nothing is 'broken.'
commentI don’t have a complete strategy for this either, but I think there’s an under‑discussed variable here: non‑deterministic drift in the underlying AI systems, especially in agentic workflows where no human is watching closely. With agentic products, value is often delivered through intent inference and reasoning quality, not just task completion. Traditional health signals like uptime, latency, or “run succeeded” only tell you the system is responding. They do not tell you whether it is reasoning as well today as it was yesterday. That gap matters more as agents become more autonomous. In many workflows, there is no user to notice that things are subtly degrading. The agent keeps operating, downstream systems keep accepting its output, and the metrics stay green while value erodes. A simple example: an agent maps free‑text input to an authentication workflow. A user says “I can’t log in.” Another says “Just let me in, there’s no way my password is wrong.” A third says “It won’t let me see anything.” On day one, the agent correctly treats all three as access issues and routes them appropriately. On day two, a third‑party model update changes how intent is inferred, and “it won’t let me see anything” no longer lands in the right flow. On day three, a shark munches an undersea cable. The provider’s services are still up, but performance degrades and reasoning depth drops. Responses come back, runs complete, tools fire, but answers are shallower or incomplete. From the outside, nothing is “broken.” In an interactive product, you might see prompt rewrites or drop‑off and debate whether that’s noise. In a fully agentic workflow, you may see nothing at all. The agent just makes slightly worse decisions, and the value you modeled on day one is no longer being delivered on days two and three. So before asking whether the system shows value, a prerequisite question is whether the agent is actually reasoning the same way over time. Until intent accuracy and reasoning completeness are treated as first‑class, continuously validated signals, it is very hard to distinguish true product regressions from normal behavior or downstream variability.
the metrics stay green while value erodes.
commentI don’t have a complete strategy for this either, but I think there’s an under‑discussed variable here: non‑deterministic drift in the underlying AI systems, especially in agentic workflows where no human is watching closely. With agentic products, value is often delivered through intent inference and reasoning quality, not just task completion. Traditional health signals like uptime, latency, or “run succeeded” only tell you the system is responding. They do not tell you whether it is reasoning as well today as it was yesterday. That gap matters more as agents become more autonomous. In many workflows, there is no user to notice that things are subtly degrading. The agent keeps operating, downstream systems keep accepting its output, and the metrics stay green while value erodes. A simple example: an agent maps free‑text input to an authentication workflow. A user says “I can’t log in.” Another says “Just let me in, there’s no way my password is wrong.” A third says “It won’t let me see anything.” On day one, the agent correctly treats all three as access issues and routes them appropriately. On day two, a third‑party model update changes how intent is inferred, and “it won’t let me see anything” no longer lands in the right flow. On day three, a shark munches an undersea cable. The provider’s services are still up, but performance degrades and reasoning depth drops. Responses come back, runs complete, tools fire, but answers are shallower or incomplete. From the outside, nothing is “broken.” In an interactive product, you might see prompt rewrites or drop‑off and debate whether that’s noise. In a fully agentic workflow, you may see nothing at all. The agent just makes slightly worse decisions, and the value you modeled on day one is no longer being delivered on days two and three. So before asking whether the system shows value, a prerequisite question is whether the agent is actually reasoning the same way over time. Until intent accuracy and reasoning completeness are treated as first‑class, continuously validated signals, it is very hard to distinguish true product regressions from normal behavior or downstream variability.
The toughest part about agent building is if your customer is on prem, you have no idea if its even being adopted with the features you release
commentThe toughest part about agent building is if your customer is on prem, you have no idea if its even being adopted with the features you release
Product leaders responsible for measuring user value and detecting degradation in autonomous AI agent products.
Three distinct complaints: metrics don't apply to agents, signals are ambiguous, and silent degradation is undetected.
Purpose-built for autonomous AI agents, measuring outcome quality rather than proxy signals like clicks or latency.
A metrics platform that tracks outcome-based signals such as user acceptance rate, output edit distance, goal completion rate, and drift detection, providing a single dashboard to separate real issues from noise.
Users already spend significant effort building in-house evals and manually reviewing logs. The pain of not knowing value is explicit in quotes like 'metrics stay green while value erodes' and 'toughest part is no visibility on prem'.
“Know if your AI agent actually delivers value, every run.”
A metrics platform that tracks outcome-based signals such as user acceptance rate, output edit distance, goal completion rate, and drift detection, providing a single dashboard to separate real issues from noise.
Launch on Hacker News (Show HN) and Reddit r/ProductManagement, r/MachineLearning. Offer free tier for single-product teams to drive adoption.
Number of teams building agentic AI products is still small; market may not support standalone product.
Must support multiple agent frameworks (LangChain, AutoGPT, custom) and deployment models (cloud, on-prem).
Teams with strong data engineering may prefer to build custom dashboards rather than pay for a tool.
Outcome metrics differ per product; product must be configurable enough without becoming generic.
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoThis idea scores in the upper-middle range of opportunities surfaced by MonetScope, with a validation sub-score of 7/10 against 5 independently sourced evidence signals. A "promising" rating usually indicates a real pain has been detected and discussed in the open, but the pipeline did not find enough signal to flag it as urgent or high-frequency. These opportunities can still produce excellent businesses — they often correspond to "boring" problems that established players have ignored — but the founder should expect a longer customer-development cycle to confirm willingness to pay.
It sits at the intersection of "ai-agents", "ai-ops", "devtools", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.
