AI Stack Readiness Agent
Finds why you cannot tell whether last week's prompt change made it worse
6 of 8 AI tools need attention.
8 AI tools checked. 6 flagged. The largest gap is 576 days.
Start with Transcription app. Engineering has the most flagged.
Whether a tool is worth keeping is for the team lead; the list shows which tools nobody is answerable for.
Why rows were flagged
- No data agreement38%
- Not reviewed, or over 12 months old38%
- No owner25%
Value flagged by Team
- Engineering1
- Legal1
- Finance1
- Sales1
- Support1
- Marketing1
Items to act on, worst first
| Item | Team | Value | Days since the last check | Why |
|---|---|---|---|---|
| Transcription app | Sales | 120 | 0 | No data agreement, No owner, Not reviewed, or over 12 months old |
| Translation tool | Support | 80 | 0 | No data agreement |
| Image generator | Marketing | 60 | 0 | No data agreement |
| Code copilot | Engineering | 900 | 505 | Not reviewed, or over 12 months old |
| Contract summariser | Legal | 300 | 0 | No owner |
| Data notebook AI | Finance | 250 | 576 | Not reviewed, or over 12 months old |
What to do this week
- Resolve Transcription app first: it is the most urgent on the list.
- Fix the data or the status on every flagged row, then re-run.
- Re-run this check every time the list changes, before it reaches a decision.
Do
- Fix the highest-risk item first, then run the check again on fresh data.
Don't
- Don't act on one row alone: open the source record first.
How to improve
- Add more detail per row (owner, date, source) so the next report can trace each item.
- Add a second period of data so the next report can show whether things are improving.
Demo
Overview
Every vendor in this space sells one layer. Nobody reviews the whole, and the piece missing is nearly always the same: a team changes a prompt on Tuesday and has no way of knowing whether Tuesday was better than Monday. Quality is measured by whoever complains loudest.
This agent reviews an AI feature the way an engineer would before letting it near customers. It works from your own description of what you built — the model, the retrieval, the tools, what it can write to, what happens when it fails — and reports what is absent at each layer, ranked by what it costs you when it bites.
It looks hardest at the five gaps that are common, quiet and expensive: no eval set, so nobody can tell a regression from a bad day; no cost attribution, so a feature's bill is invisible until the invoice; retries and steps without a ceiling, so one loop becomes a four-figure afternoon; personal data crossing a provider boundary nobody mapped; and an agent that writes somewhere with no human gate. It recommends no vendor, because the finding is almost never which tool you picked.
Benefits
You can finally tell a regression from a bad day
The single change that makes every later change measurable.
The runaway bill found before it happens
Unbounded loops are cheap to fix and expensive to discover.
The enterprise questionnaire answerable
Because the data boundary is written down.
What it does
Finds the missing eval set first
The gap almost every team has. Without one, a prompt change is shipped blind and quality is whoever complains loudest.
Checks cost is attributable
Per feature and per run. A bill that only arrives monthly is a bill nobody can act on.
Finds unbounded work
Retries, steps and tool calls with no ceiling. One loop is a four-figure afternoon.
Maps where data crosses a boundary
What leaves your systems and reaches a model provider. Most teams have never written this down, and it is the first thing an enterprise buyer asks.
Checks writes have a human gate
Anything that sends, spends or changes something outside your system, and who approves it.
Recommends no vendor
Every tool in this market sells its own layer. The finding is almost never which one you picked, and naming a product would be selling rather than reviewing.
What this changes
What this changes about your week
- It takes over
- Working out what an AI feature is missing before it reaches customers.
- Done today by
- Nobody, until a bill arrives or a customer complains.
- How often
- Every AI feature, and again before every enterprise deal.
- Typical manual effort
- A proper review needs someone who has shipped this before, and they are the busiest person you have.
Why Software & Engineering in particular. Every vendor sells one layer and none reviews the whole. What is missing is nearly always the eval set — so a team changes a prompt on Tuesday and cannot tell whether Tuesday was better than Monday.
Runs unattended
Started by you or by an event, and it finishes on its own. Nothing waits for someone to be at a desk.
The same standard every time
The two-hundredth item is held to the bar the first one was. Consistency is the part people cannot sustain.
It cannot act on its own
AI Stack Readiness Agent has no path to sending, spending or committing. That limit is why its output is safe to act on.
How it runs
This agent runs server-side through the PROMIVO runtime. Each run is logged step by step and every tool call is permission-checked before it executes.
- 1InputValidated against the agent input schema
- 2Modelclaude-opus-5 with automatic fallback
- 3Toolsextract_structured_data
- 4PermissionsRuns READ actions automatically; anything higher waits for approval
- 5OutputStructured result matching the output schema
- 6LoggingTokens, duration and every step recorded
Requirements
Integrations
No integrations required.
Before you start
- A description of the feature: model, retrieval, tools, limits, what it can change
- Requires the All-Access plan or higher
Use cases
Before a feature reaches customers
What is missing while adding it is still cheap.
After a quality complaint
Whether anything would have caught it, and what to add.
A surprise model bill
Where the spend goes and what has no ceiling.
Enterprise diligence
The questions a buyer will ask, answered before they ask.
Pricing
All-Access
Billed monthly through your PROMIVO subscription. Cancel at any time.
Runs consume your plan allowance for agent executions and tokens. See plan limits
FAQ
Will it recommend tools?
No. Every vendor here sells one layer and the finding is almost never which one you chose. It names the gap and what closing it requires, and leaves the choice where it belongs.
Does it read our code?
It works from your description of the feature — model, retrieval, tools, limits, failure behaviour. A description you can write in ten minutes surfaces the same gaps, because these are architectural rather than hidden in a function.
Is this a security audit?
No. It reports where data crosses a provider boundary and where a write has no gate, which is what an enterprise buyer asks about. A security assessment is a separate engagement with a different signature on it.
Reviews
Reviews open once customers have run this agent.
Need this agent to work differently?
Tell us what to change and our team will scope a customised version for your business.
Customize this agent