Most people think agentic AI is reasoning.
They’re overthinking it.
It’s really just structured guessing, at speed.
Agents can split work into steps.
They can write plans.
They can call tools.
But planning isn’t judgment.
And prediction isn’t accountability.
That gap is fine for content.
“Probably right” is usually good enough.
It fails fast in finance, law, and compliance.
Because your risk is not the wrong answer.
Your risk is an answer you can’t explain.
Here’s the shift I’ve seen work in real systems.
Let agents coordinate the messy parts.
Then defer the final decision to something deterministic.
Think rules plus curated knowledge.
Think versioned logic.
Think audit logs.
Think receipts.
A simple example.
An agent flags a transaction as “likely suspicious.”
That’s a guess.
The deterministic layer must answer.
Which rule triggered.
Which data fields matched.
Which policy version applied.
What evidence was stored.
↓ A practical framework you can implement.
↳ Use agents for retrieval, summarizing, and triage.
↳ Use deterministic rules for approvals, rejections, and thresholds.
↳ Store every input, rule, and output for replay.
↳ Require citations for every claim.
You don’t need an AI that “thinks.”
You need a system that can prove.
Where would “probably right” be unacceptable in your org?
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