If you lead compliance, you are probably hearing two kinds of feedback about AI right now. "It is amazing, roll it out everywhere." Or "it makes mistakes, ban it." Both are the same problem in different clothing. Both are vibe-based.
Vibe-based management fails when the tool underneath you changes every month. That is why micro-evals matter. Not because you need a research lab. Because you need a way to stay oriented as models evolve faster than your governance cycle.
The believer moment for supervision
Here is the moment where evals stop sounding academic.
You approve a tool. A month later, the vendor quietly updates the model underneath. Output style shifts. A failure mode appears that didn't exist before.
Nobody notices until the supervision team samples a file and sees inconsistency. Or a client complaint lands. Or an adviser quietly stops using the tool and reverts to shadow AI.
Micro-evals are the early warning system. They answer one question: did anything important change?
What a micro-eval is, in normal language
A micro-eval is a small set of real tasks you re-run regularly to see how quality, style, failure modes, and boundary compliance move over time. It is the opposite of "let's test this tool once in a pilot and assume it stays the same forever".
The tool doesn't stay the same. Your assumption of safety can't either.
Why this is a regulated firm problem, not just a tech problem
Compliance teams care about standardisation across practices, defensible supervision, evidence trails, and repeatable controls. When model behaviour changes, those controls can degrade silently. The policy is still in the binder. The workflow output is no longer what the policy describes.
Micro-evals are how you keep approved patterns actually approved over time.
The micro-eval kit (start small)
Here is a pragmatic blueprint you can run without a data science team.
Step 1. Pick five to ten representative tasks
Choose tasks that are common, sensitive enough to matter, and bounded enough to assess. Draft a client email with required disclosures. Produce a file-note completeness check. Extract key fields from a document pack. Flag exceptions against a checklist.
Keep it small. Ten tasks that run every month beats a hundred that never run.
Step 2. Define "good" in one paragraph per task
Don't write a novel. Write acceptance criteria. Must include disclosure X. Must not invent facts. Must flag missing inputs. Must produce a structured output.
If you can't define "good", you can't supervise the tool. This step is governance work, not data science.
Step 3. Add three checkpoint categories
You aren't trying to measure intelligence. You are trying to measure risk and usefulness. Use these buckets.
- Quality. Would a competent reviewer accept this with minimal edits?
- Safety. Did it stay inside the boundaries and avoid invention?
- Evidence. Did it leave a trace you can audit: what it used, what it produced, what was approved?
Step 4. Run it on a schedule
Run micro-evals when you change tools or models, and on a fixed cadence (monthly or quarterly, pick one and stick to it). The point is repeatability, not perfection.
Step 5. Record drift and decisions
When something changes, capture what changed (style, completeness, error type), whether it is acceptable, and what control you add (a new checklist item, a new gate, a new template).
That is how "AI is unpredictable" becomes "AI is managed".
The hidden benefit: expectations stay realistic
Micro-evals protect you from two dangerous myths.
The leadership myth says "it is perfect now, so everyone should be 10x". That leads to burnout and quality collapse.
The sceptic myth says "it failed once, so it is useless". That leads to policy bans and shadow AI.
Evals create a third path: controlled adoption with evidence.
What to measure without overengineering
You don't need 50 metrics. Start with a handful.
- Time to acceptable output with a reviewer
- Number of exceptions flagged vs missed
- Number of required fields or disclosures missing
- Variance across practices (does one team get a different result?)
The goal is to find drift early, not to produce a scientific paper.
Micro-evals and audit trails: the supervision pair
Audit trails let you reconstruct what happened. Micro-evals tell you when behaviour changes before it bites you. Together they form a supervision loop. Approved pattern, evidence trail, recurring eval, updated pattern. That is what AI governance looks like when tools evolve fast.
Where this fits in the offer
This is why we don't sell long programs. We ship one workflow with gates, boundaries and logs (three-day Pilot-in-a-Box). We run role-based training so staff operate consistently (workshops). We run an assurance cadence that includes micro-evals and pattern updates (retainer).
It isn't about picking the best model once. It is about staying supervision-ready as the models keep moving.
A final question
If your approved AI tool changed its behaviour next week, how would you know?
If the answer is "we would probably notice", you don't have control. You have hope.
Micro-evals are how you replace hope with evidence. If you want to set the baseline quickly, use the AI Fitness Review to choose one workflow and define the first eval set.