Lectotype Governance before inference

Agents are eager. That is the problem.

Left to direct themselves, AI agents plan, search, retry and plan again. Each lap costs tokens, and each wrong assumption rides along to the next step.

Nobody budgeted for step fourteen. Nobody approved it either.

The published numbers agree.

Chasing sources that did not exist

Duplicated work and excessive delegation in early agent trials

Anthropic early trials, Jun 2025

More tokens for multi-agent work than for chat, in Anthropic's reported experience

Anthropic engineering, Jun 2025

Token variation between runs of the same coding task

Bai et al., Apr 2026 preprint

Errors compound and spend is unpredictable when the model directs the workflow. The fix is structural, not a better prompt.

Put the limits where they can be enforced.

When the model directs the work
With Lectotype
Repeated planning and retries
Defined scope and bounded model calls
Assumptions carried into later steps
Admitted evidence and external checks
Authority left in model instructions
Permissions enforced in the workflow
Token spend that nobody can forecast
Code does the orchestration; tokens go to language

Policy, math and orchestration move into governed code. The model gets a bounded job and a record of what it did.

Your data never changes hands.

Your controlled environment versioned
Source data
Rules and relationships
Corrections
Outputs and audit record
Your operating knowledge, secured and versioned on your side
Out: approved context and placeholders
Back: output, checked before real values are restored
Selected
model
Language only

The model never receives your source records, only what the rules approve. Your data, rules and records stay under your control.

Keep your rules. Change your model.

Lectotype control layer stays the same
  • Rules and permissions
  • Evidence and calculations
  • Prompt and output requirements
  • Versioned records
Enterprise model APIApproved hosted endpoint
Open-weight modelControlled local deployment
Each configuration validated against the same business requirements

Models change every quarter. Your governance should not have to.

Eight standards. One control layer.

SSovereignty
TTraceability
AAssurance
NNative Governance
DDeployment Safety
AAuthoritative
RRegional Compliance
DDependable Governance

Together they spell the STANDARD framework. Every workflow on Lectotype is designed, deployed and audited against it.

A lectotype is the specimen everything else is measured against. We built the same idea for enterprise AI.

LECTOTYPE · CHOSEN REFERENCE STANDARD L

Your data. Your rules. Your choice of model.

The standard against which your AI is measured. Bring the AI workflow that worries you most. We will show you exactly where the controls go.

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Every enterprise needs a type.

ChosenClarityConfirmed