Agentic training & control systems

Train agents to behave predictably in production. Predictable agents.
Lower cost.
Production-ready.

Krok Core is an upstream training + constraint layer that reduces drift, retries, and token waste in generative systems — especially agents.

Upstream training + constraints
Reduce agent drift
Cut retries & token waste

See how it works Talk to Krok-AI

Designed for production agents — not chat demos.

Model-agnostic Lower variance Fewer reprompts Lower token burn Production-first

The problem

Most teams didn’t fail at choosing a model. They failed at training agent behavior. Prompts and guardrails don’t scale into stable execution.

“More context” becomes “more chaos.”
Quality becomes a moving target.
Cost climbs through retries and long chains of thought.
Ops teams carry the burden with brittle patches.

What we do

We help teams train generative agents upstream so they behave consistently downstream.

Stabilize outputs (lower variance)
Reduce retries and loopiness
Collapse token burn
Increase task completion reliability

Krok Core (the engine)

An upstream constraint + training layer that narrows the solution space early — before the agent burns tokens exploring dead ends.

Model-agnostic by design

We don’t ask you to replace your stack. Krok Core complements your current model/tooling choices.

Production over theatre

The goal isn’t a clever demo. It’s predictable behavior, measurable outcomes, and lower operating cost.

How it works (high level)

Downstream-heavy agents try to “reason their way out” of uncertainty. Krok Core pushes structure upstream: constrain first, then execute.

Define success

Pick one narrow workflow where failure is visible and measurable.

Constrain upstream

Reduce ambiguity early with structured choices, checks, and routing.

Train behavior

Repeatable patterns that reduce drift, retries, and variance.

Measure & iterate

Before/after metrics: completion, retries, cost, and consistency.

Where it helps first

Teams that already deployed AI and now need it to behave:

Customer support copilots & resolution agents
Ops agents (triage, routing, checklists, escalation)
Sales research / lead enrichment workflows
Internal knowledge assistants that must stay consistent
Education / safety-sensitive assistants with strict tone + rules

If your agent “works sometimes” but costs too much or drifts — this is the exact layer we focus on.

Pilots

The fastest way to prove this is a short pilot on one workflow. We focus on measurable improvements: fewer retries, lower token burn, higher completion, lower variance.

Scope 1 workflow / 1 agent loop

Metrics Retries, completion rate, cost, consistency

Output A repeatable training pattern your team can extend

Want to compare notes?

If you’re deploying agents or production chat and seeing drift, retries, or runaway cost, reach out and tell me what’s breaking.

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