Agentic Determinism. Probably.

A deterministic control plane for AI. The model suggests. The system decides.

What it is

AI doesn't act alone. It operates inside governed infrastructure.

01

Governed execution

Models can invoke tools, but never directly. Every command is inspected, policies are enforced, and everything is logged. If it ran, it can be replayed.

02

Forensic recall

Similarity is not identity. Not something close to what happened, but the receipt: the exact message, the exact state, the exact execution context.

03

Human authority

A person stays in the loop with real authority: pause execution, inject new context, redirect to a different model, override a decision before it runs, and resume cleanly.

The substrate

One common tongue for every model.

Everything GetPlane stores or sends is a Knowledge Content Object (NCO, patent pending): one structured, machine-readable form, optimized for AI systems to consume.

Every part of the platform produces and consumes them as its common currency, and an object can contain other objects: a conversation is made of messages, each in the same form. So anything that can read one can read the work of every other part.

At rest

Stored, as a complete, machine-readable record of everything the platform knows and has done.

In motion

Sent, as every message crossing the control plane, routed, checked against rules and recorded as it moves.

Agent documentation benchmark

Half the time.Half the cost.

The same off-the-shelf coding agent, on the same task, with its documentation served from the GetPlane control plane as structured knowledge objects instead of the open web. Same tested result.

Confirmed on a second task the format was never tuned on: half the time and half the cost again, 7 runs per setup.

Read the benchmark →
54%
less time to a tested solution6.1 min vs 13.2 min, medians
52%
lower cost per accepted solution$1.33 vs $2.77
3/3
runs passed every hidden testno internet access required
Why enterprise AI stalls

When trust cannot be assumed,it must be engineered.

Enterprise AI has a paradox at its center: organizations are investing because the technology is useful, yet remain reluctant to give authority to increasingly autonomous systems. The limiting question is changing from "What can AI do?" to "What are we willing to let it do?"

Read the paper →

447 endpoints·9 modules·382 consecutive build days

Get in touch

Talk to us.

Questions, pilots, or a closer look at the platform. Write directly:

dennis@getplane.ai