Core Concepts

This page explains the mental model to carry into the rest of the docs.

TIP

Prefer learning by code? Start with Your First Step and come back later.

Verist in one sentence

Verist is a deterministic workflow kernel for AI systems: replay, recompute, and diffs for decisions.

The big idea

Most AI workflows are hard to trust because you can't reproduce decisions or understand what changed after a model upgrade. Verist fixes that by being strict about:

Discipline	Why it matters
Explicit inputs	No hidden dependencies
Explicit artifacts	Store what the model saw and returned
Explicit state changes	Steps return outputs, nothing else mutates state

That discipline makes replay and diff possible.

Core objects

Step

A deterministic function that takes input + context and returns:

Field	Description
output	Partial state update
events	Audit records
commands	What should happen next (optional)

A step is the only place where "work" happens.

What a step is NOT: task runner, scheduler, database writer, internal retry mechanism.

Workflow

A named set of steps plus a version. It doesn't run on its own – your runner does.

What a workflow is NOT: orchestrator, self-executing runtime, required for single-step usage.

State

State lives in your database. Not in memory. Not in a hidden framework cache.

Layer	Source
computed	Derived from step outputs
overlay	Human overrides
effective	{ ...computed, ...overlay }

Overlay always wins so human decisions survive recomputation.

Artifacts

Captured inputs/outputs from non-deterministic calls (LLMs, external APIs, file reads). Stored and hashed.

Artifacts make replay and recompute possible. They are not logs – they are required for exact replay.

Replay vs recompute

	Replay	Recompute
Uses	Stored artifacts	Fresh adapters
Answers	"What happened?"	"What would change?"

Common mistakes

• Confusing replay with recompute
• Expecting workflows to "run themselves"
• Treating artifacts as logs instead of inputs/outputs

Why the kernel is strict

Verist refuses to be "helpful" in ways that hide state or control flow. That's the only way to make AI decisions reviewable later.

If you want speed and autonomy, an agent framework might fit better. If you want correctness under scrutiny, Verist is built for you.