Skill — Component maturity

01.When AI handles a repetitive task at your company (a client email, ticket triage, extracting data from a document) — what's the starting point on your team?

Everyone opens a chat and writes a prompt from scratch every time, a little differently depending on their mood The team keeps proven prompts in a notes file/doc and copies them in by hand You have versioned, named components (prompts/skills) in a repo that you assemble tasks from Components are versioned, have an input/output contract, and changing one doesn't break the rest (it's tested)

Skill — Component maturity

02.If in six months a new person had to take over AI work from someone on the team — what do they get?

Nothing tangible — the knowledge lives in people's heads and their chat history A handful of sample prompts and a verbal ‘do roughly this’ Documented, named blocks with notes on when to use which A working, versioned set of components + tests that prove it reproduced the result correctly

Skill — Component maturity

03.How does your team react when a provider ships a new model (the next GPT/Claude/Gemini version)?

You switch and hope it's better — there's no way to tell the difference Someone tests by hand on a few examples they happen to remember You have a fixed set of cases you run the old and new model on and compare You have a golden dataset of real cases and metrics; swapping the model = running evals that tell you better/worse and where

Skill — Component maturity

04.When AI has a series of similar tasks (200 documents, a batch of replies), how do you stay on top of cost and reuse?

Nobody tracks cost or caches anything — you fire it off and pay whatever it costs, from scratch every time You roughly know it ‘gets expensive’ and someone sometimes caps the input by hand, but with no system You have a deliberate budget/limit per task and reuse earlier results wherever you can You have a results cache, token-budget control, and model selection matched to step difficulty — cost measured and optimized per process

Workflow — Execution control

05.A task takes several steps (fetch data → analyze → decide → take action). Who holds control over the order and the transitions on your team?

You dump everything into one prompt and trust the model to sort out the order itself Someone steers it by hand in conversation, step by step, copying results between steps You have a script/workflow that calls the model in fixed steps, but the model can wander off the plan Control flow is deterministic — you define the steps, gates and conditions; the model does the cognitive work inside a step, it doesn't pick the route

Workflow — Execution control

06.When AI calls a tool/API (send an email, write to the database, make a payment, change a record) — how is it fenced in at your company?

You give it access and let it run autonomously, trusting it'll choose well Actions are possible, but someone pastes/approves them by hand before anything runs Tools have input validation and limited permissions; critical actions require confirmation (HITL) Every action has deterministic guardrails: parameter validation, an allowlist, limits, idempotency and a logged trail — the agent can't leave the envelope

Workflow — Execution control

07.When an AI process falls over mid-run (bad format from a tool, truncated context, a loop, a timeout) — what happens at your company?

You usually find out because the final result is weird — and you start over Sometimes someone catches it by hand, but there's no mechanism — it depends on who's watching You have error handling and retries at the step level, some state is logged Every step has explicit failure handling, fallbacks and recoverable state; a trace shows where and why it broke before the result moves on

Workflow — Execution control

08.Can your team reproduce the same AI result from a week ago (same input → the same, checkable output)?

No — it comes out a little different every time and that's how it's meant to be More or less, if someone finds the previous chat and pastes the same thing Yes for the key cases — you've saved the inputs, prompt versions and results Yes — you version prompts, models and parameters, and log traces; every result is reproducible and auditable

Loop — Closure / verification

09.How do you know the AI output is good — before it reaches the client/production?

It looks good / the model said ‘done’ / status = OK — so you assume it's fine Someone reads it and eyeballs it, if they have time and if they remember You have a checklist or automated checks for some of the criteria on important cases The output passes through a closed verification loop (deterministic checks + an LLM-as-judge calibrated to a human) before you call it ‘done’

Loop — Closure / verification

10.Your agent returns ‘success’, but quietly produces worse quality (semantically close but actually wrong results, drift, regression). How fast does your team catch it?

Probably only when the client complains — there's no way to catch it earlier When someone happens to look through the results and something feels off You have spot quality checks / a periodic sample review You have continuous output-quality monitoring (not just latency/cost): metrics, drift alerts and a watchdog that catches silent failure almost instantly

Loop — Closure / verification

11.When AI makes a mistake in a specific way one time (misclassifies a document type) — what do you do so it doesn't happen again?

Someone fixes that one case by hand and you move on Someone tacks something onto the prompt hoping it helps — without checking whether it broke others You add a rule/fix and test it on a few cases Every real failure becomes a test case in the golden dataset; the fix has to pass regression before it ships

Loop — Closure / verification

12.When AI generates many variants/candidates (ideas, answers, code variants) — how do you pick the one for production?

You take the first decent one / whichever subjectively sounds best Someone reviews by hand and picks on instinct, if there aren't too many variants You have scoring criteria and filter some out automatically before picking by hand You have an automated selection loop (ranking/judge-panel) that surfaces the few best out of hundreds of variants by measured criteria

AI-Loops Readiness.

01.When AI handles a repetitive task at your company (a client email, ticket triage, extracting data from a document) — what's the starting point on your team?

02.If in six months a new person had to take over AI work from someone on the team — what do they get?

03.How does your team react when a provider ships a new model (the next GPT/Claude/Gemini version)?

04.When AI has a series of similar tasks (200 documents, a batch of replies), how do you stay on top of cost and reuse?

05.A task takes several steps (fetch data → analyze → decide → take action). Who holds control over the order and the transitions on your team?

06.When AI calls a tool/API (send an email, write to the database, make a payment, change a record) — how is it fenced in at your company?

07.When an AI process falls over mid-run (bad format from a tool, truncated context, a loop, a timeout) — what happens at your company?

08.Can your team reproduce the same AI result from a week ago (same input → the same, checkable output)?

09.How do you know the AI output is good — before it reaches the client/production?

10.Your agent returns ‘success’, but quietly produces worse quality (semantically close but actually wrong results, drift, regression). How fast does your team catch it?

11.When AI makes a mistake in a specific way one time (misclassifies a document type) — what do you do so it doesn't happen again?

12.When AI generates many variants/candidates (ideas, answers, code variants) — how do you pick the one for production?

Unlock your result