AI-Loops Readiness — find your position

Skill — Component maturity

01.When AI handles a repetitive task for you (a client email, ticket triage, extracting data from a document) — what's your starting point?

I open a chat and write a prompt from scratch every time, a little differently depending on my mood I keep proven prompts in a notes file/doc and copy them in by hand I have versioned, named components (prompts/skills) in a repo that I assemble tasks from Components are versioned, have an input/output contract, and changing one doesn't break the rest (I test for that)

Skill — Component maturity

02.If in six months a new person had to take over your AI work — what do they get?

Nothing tangible — the knowledge lives in my head and my 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 do you react when a provider ships a new model (the next GPT/Claude/Gemini version)?

I switch and hope it's better — I have no way to tell the difference I test by hand on a few examples I happen to remember I have a fixed set of cases I run the old and new model on and compare I have a golden dataset of real cases and metrics; swapping the model = running evals that tell me 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?

I don't track cost or cache anything — I fire it off and pay whatever it costs, from scratch every time I roughly know it 'gets expensive' and sometimes cap the input by hand, but with no system I have a deliberate budget/limit per task and reuse earlier results wherever I can I 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?

I dump everything into one prompt and trust the model to sort out the order itself I steer it by hand in conversation, step by step, copying results between steps I have a script/workflow that calls the model in fixed steps, but the model can wander off the plan Control flow is deterministic — I 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?

I give it access and let it run autonomously, trusting it'll choose well Actions are possible, but I paste/approve 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?

I usually find out because the final result is weird — and I start over Sometimes I catch it by hand, but I have no mechanism — it depends on whether I'm watching I 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 you 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 I find the previous chat and paste the same thing Yes for the key cases — I've saved the inputs, prompt versions and results Yes — I 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 I assume it's fine I read it and eyeball it, if I have time and if I remember I 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 I 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 do you catch it?

Probably only when the client complains — I have no way to catch it earlier When I happen to look through the results and something feels off I have spot quality checks / a periodic sample review I 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?

I fix that one case by hand and move on I tack something onto the prompt hoping it helps — without checking whether it broke others I 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?

I take the first decent one / whichever subjectively sounds best I review by hand and pick on instinct, if there aren't too many variants I have scoring criteria and filter some out automatically before picking by hand I 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 for you (a client email, ticket triage, extracting data from a document) — what's your starting point?

02.If in six months a new person had to take over your AI work — what do they get?

03.How do you 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?

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?

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

08.Can you 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 do you 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?

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