Opportunities › Error detection and debugging for autonomous agents › Proposed app
Slipwatch
Finds silent mistakes in AI agent runs, groups them, and turns them into tests
- Score
- 54
- confidence 59
- Evidence
- 8
- items on the page
- Rivals
- 2
- named in sources
- First version
- 60 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2023
- Emerging market · AI estimate
ScreensConcept
Desktop app: Teams investigate agent failures, review error clusters, examine traces, and evaluate fixes—analytical work that demands a full dashboard and keyboard-driven investigation. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Today's runs
Support agent v14212 flagged
About this appAI proposal
Slipwatch reads the traces your AI agents leave behind and flags runs that went wrong even though every call succeeded: the wrong button clicked, the wrong document cited, a step quietly skipped. It is built for teams running agents in production, such as in software development, legal and customer support.
Instead of storing traces nobody reads, Slipwatch checks each one with a small, purpose-built detector, aiming to keep the cost low enough to cover all production traffic rather than a sample. Similar failures are grouped, so you see ten problems, not ten thousand runs.
Pick a group, save its cases as an evaluation set, and re-run that set against a fix before release. The proposal includes a self-hosted open-source core and a managed cloud.
- Tagline
- Catch the mistakes that look like success.
- Voice
- PlainPreciseCalm
- Why this name
- A slip is a small error that goes unnoticed; a watch keeps an eye out. Two plain words joined, easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Silent failure detection
Checks the whole trace end to end and flags runs where calls succeeded but the outcome was wrong, such as a wrong click or a misquoted source.
No member yet
Every trace, not a sample
A small specialised detector is meant to keep per-trace cost low, so you can review all production runs instead of sampling.
No member yet
Error clusters
Similar failures are grouped with a plain-language label, a count and a trend, so you fix causes rather than single runs.
No member yet
Evaluation sets from real errors
Turn any cluster into a dataset in one step, with the trace, the flagged step and your verdict attached.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#14232BDeep Ink · primary and text#F6F3ECPaper · surface#E8A317Signal Amber · accent#2F7D5BFern · success#B3402FBrick · error flag
Typography
Aa
Slipwatch
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Catch the mistakes that look like success.
PricingAI proposal
The material gives no competitor prices. It only says the model is subscription by trace volume, with open-source and cloud versions. The $99 tier and its limit are a starting point to test.
Self-hosted
Free
- Open-source core
- Run on your own servers
- Community support
Team
$99 a month
- Managed cloud
- Up to 100k traces a month
- Clusters and eval sets
- CI release check
Scale
Contact us
- Volume-based per-trace pricing
- Higher limits
- Priority support
Would you pay Team · $99 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Partners and integrations. Ship SDK snippets and connectors for popular agent frameworks and CI tools, and list them where developers look for them. Open-source self-hosters can become the cloud's trial users.
- Do they become customers?
- Developers who find it through integrations are the buyers, so they can pay. Test whether self-hosted users convert to the cloud tier and whether CI checks pull in teammates.
- First customers
- Small engineering teams of 3 to 15 running a customer support agent in production, with a few thousand runs a day and no one reading traces.
- Where to reach them
- Hacker News and dev forums: Share the open-source core with a write-up of silent agent failures and real clusters found in public sample traces.
- Agent framework communities: Offer a free trace connector and answer debugging questions in community channels.
- Direct outreach: Message engineers who post about agent failures in production and offer a free analysis of one day of traces.
- First milestone
- Five teams send a week of production traces, and at least three pay for the cloud tier after a free trial.
Why people already pay for this
- VerifiedHacker News post received 58 points and 25 comments
- Public claimAI agents fail implicitly, with errors that would be most useful to catch when all calls complete successfully
- Public claimExisting agent observability solutions function primarily as data collection platforms without meaningful analysis
- Public claimCapable LLM-based trace analysis is expensive at scale; cheaper models lack quality
- AI inference
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Trace ingestionOpenTelemetry-compatible collector endpointTeams send agent traces in a standard format without rewriting their agents.
- This app · Detector model servingGPU or CPU inference host for a small modelRuns the specialised detector cheaply on every trace; Vercel does not host models.
RisksAI inference
- AIA small detector may not match large-model quality across different agents. The claim of comparable quality is an AI inference, not verified, and false flags would erode trust fast.
- DataTraces can hold customer, legal or support data. Buyers may demand self-hosting, redaction and retention controls before sending anything to a cloud.
- CommercialExisting observability tools could add error analysis, and an open-source core lets others host it. Differentiation has to hold up.
- TechnicalAgent frameworks log traces differently. Supporting many formats may take more work than the first version assumes.
- CommercialEvidence is a single Hacker News post of 58 points and 25 comments. Willingness to pay is not yet shown.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.