hans van loon

Against inequality #no2greed

[Kaidera @kaidera_ai]

Kaidera keeps workflow, policy, memory, evidence and human authority in the operating layer. The approved model path can then be evaluated and improved without abandoning the business process.

2/5

[Kaidera @kaidera_ai]

Enterprise AI should not force a team to redesign its work around the limits of one model endpoint.

Start with the task. Define what acceptable work looks like. Evaluate an approved model route against real examples and the operating constraints around them.

1/5

[hans van loon @hvloon08]

@KritischePatri Je krijgt lego als kado en de overheid pakt dan de helft ervan, weg lego

[hans van loon @hvloon08]

@fvdemocratie Nederland = Communistische democratie

[hans van loon @hvloon08]

@HarryPater62 Volgens een AD poll was het juist andersom

[hans van loon @hvloon08]

@MadelonVos__ Om het wachtgeld van nietsnutten te betalen

[hans van loon @hvloon08]

@Net_Arts Erfenis is een familie item waarom moet de belasting zich ermee bemoeien. Betuttelend beleid

[Kaidera @kaidera_ai]

which data boundary applies,

what evaluation passed,

when a human signs off,

what record is left for the next worker.

That is the difference between an agent that can act and an agent fleet the business can trust.

3/6

[Kaidera @kaidera_ai]

It is blocked by the gap between a demo and an operating system.

A demo can show an agent reasoning, calling tools and producing a clean answer.

A production workflow has to survive a different test:

who owns the goal,

what tools are allowed,

2/6

[Kaidera @kaidera_ai]

For engineering leaders moving agent work from demo tickets into Jira and GitHub, #EnGenAI keeps goals, guardrails, evaluation evidence and human review attached until the work is accepted.

The market is getting clearer: agentic AI is not blocked by better demos.

1/6

[Kaidera @kaidera_ai]

That is how AI work becomes visible enough for serious organisations to trust.

That is the layer #EnGenAI is building.

Read the trust model: https://t.co/rSwmMy4sld

#EnterpriseAI #AITrust #AIGovernance #EnGenAI

cc @amadmalik

2/2

[Kaidera @kaidera_ai]

Enterprise AI adoption needs a system of record for the work AI performs.

Not only chat transcripts.

Not only tool logs.

Not only dashboards.

A real operating record.

Intent.

Scope.

Agent.

Action.

Evidence.

Approval.

Outcome.

Residual risk.

1/2

[Kaidera @kaidera_ai]

Receipts are not paperwork.

They are the control plane for enterprise AI work.

They tell the system what happened.

They tell humans what to inspect.

They tell the next agent what context matters.

1/3

[Kaidera @kaidera_ai]

Can the work recover when something changes?

If the answer is no, the platform is still experimental.

#EnGenAI is built to make those answers yes.

Start with the platform overview: https://t.co/zd2wB9wiCC

2/3

[Kaidera @kaidera_ai]

Here is the enterprise adoption test for AI.

Can it work across models?

Can it respect policy?

Can it operate inside a project boundary?

Can it produce receipts?

Can a human review high-impact actions?

Can the business see what happened?

1/3

[Kaidera @kaidera_ai]

If evidence is missing early, it will not magically appear at scale.

#EnGenAI is built for visibility before expansion.

Make the work inspectable.

Make the boundaries clear.

Make the receipts part of the system.

Then scale.

2/3

[Kaidera @kaidera_ai]

Do not scale an invisible workflow.

If AI work is hard to inspect when one team uses it, it will become harder when ten teams use it.

If ownership is unclear in a pilot, it will be worse in production.

1/3

[Kaidera @kaidera_ai]

#EnGenAI treats approval as part of the operating design.

Impact level determines the gate.

Evidence travels with the request.

The reviewer sees what matters.

That is how human control stays meaningful at AI speed.

2/3

[Kaidera @kaidera_ai]

Human approval is not a checkbox.

It is a design problem.

Ask too often and the workflow dies.

Ask too late and the risk has already crossed the boundary.

Ask without evidence and the reviewer cannot make a useful decision.

1/3

[Kaidera @kaidera_ai]

Without evidence, the work is trapped in the original session.

That is not enterprise-ready.

#EnGenAI uses Cortex handoffs so AI work can move with the context that matters.

Owner.

Evidence.

Verification.

Residual risk.

2/3