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steerlane.ai

The control layer for production AI

Know what changed.Revalidate only what matters.

Steerlane traces AI changes against approved baselines to identify affected controls, preserve valid evidence and focus review where it is needed.

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Understand the change Trace its impact Focus revalidation Support the decision

STEERLANE / CHANGE IMPACT

Sample data

Credit Review Assistant

Approved baseline v2 → Candidate v3

Revalidation required

Changed

04

Mapped

03

Unknown

01

Semantic changes

Component · primary_model
Approved → Candidate · Approved model Candidate model
Impact · Revalidate
Component · credit_data_permission
Approved → Candidate · Read Read + write
Impact · Revalidate
Component · system_prompt
Approved → Candidate · Approved prompt Revised prompt
Impact · Revalidate
Component · document_lookup
Approved → Candidate · Not present New tool
Impact · Unknown / review
Preserved

Unaffected evidence can remain valid when its dependencies are unchanged.

Needs human review

An unmapped change is never assumed safe or approved.

Explore change impact →

One connected control layer

Control the change, not just the checklist.

Technical state, affected assurance and the next governed decision stay connected across the AI lifecycle.

01 / 05

AI estate & state

Distinguish intended, declared, deployed, observed and approved AI system state.

Five-state visibility →
02 / 05

Change intelligence

Compare candidates to their approved baselines and trace meaningful technical change.

Semantic impact →
03 / 05

Assurance & evidence

Preserve proof that still holds. Focus evaluation and review on affected controls.

Targeted revalidation →
04 / 05

Cross-agent dependencies

See where shared tools, permissions and data sources create wider change impact.

Shared dependency paths →
05 / 05

Governed decisions

Keep reviewer action, unresolved uncertainty and decision history explicit.

Accountable approval →

Inspectable reasoning

Trace the impact of one change.

Choose a sample component to inspect which assurance path needs attention and which evidence can remain valid.

STEERLANE / ASSURANCE PROOF PATH / AUTHORED SYNTHETIC FIXTURE

Credit Review Assistant / v2 → v3

Inspect an assurance impact path

Select one proposed component change. Follow it to the affected control, evidence, next action and reviewer.

Illustrative product scenario

01 / Select a changed component

02 / Explain the proof path

primary_modelREVALIDATE
  1. 01Affected control

    model_quality_threshold

  2. 02Bound evidence

    Model-quality benchmark result

  3. 03Required action

    Rerun the model-quality evaluation against the candidate configuration.

  4. 04Review owner (illustrative)

    AI platform / assurance owner

The model used by the candidate differs from the approved reference. The baseline evaluation alone cannot establish that the new model meets the same requirement.

Decision remains pending

An impact result is not an approval. All mappings and statuses here are authored examples, not live connector outputs.

Human decision required

Unknown dependencies remain visible rather than silently classified as unaffected.

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Cross-agent impact

The change rarely stops at one agent.

A shared model, tool or permission can affect several AI systems. Trace dependencies, find affected controls and bring the right evidence to review.

Sample scenario

Cross-agent change intelligence

Shared Customer Records MCP

Sample data · cross-agent scenario

Stage 1 of 5: AI estate.

Import and normalize

Different agent stacks, one governed perspective.

Three fictional agents share an MCP capability. A qualified integration would retain native provenance, source freshness and explicit unsupported fields before mapping a dependency.

Copilot Studio / Agent 365

Customer Support Agent

Shared dependency: Customer Records MCP

Illustrative source only

LangGraph / LangSmith

Claims Review Agent

Shared dependency: Customer Records MCP

Illustrative source only

Custom / MCP

Research Assistant

Shared dependency: Customer Records MCP

Illustrative source only

All agents, controls and results are fictional · No live vendors or decisions

Integration scope and source availability are confirmed during evaluation.

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Built for enterprise teams

One decision context. Different responsibilities.

Engineering needs impact. Reviewers need assurance. Both need to refer to the same governed state.

AI platform leaders

Which agent changes put the estate at risk?

See version drift, dependency paths and the assurance work behind production decisions.

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Governance & technology risk

What can reviewers actually rely on?

Inspect an approved reference, valid evidence, unresolved changes and accountable reviewer handoffs.

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Design-partner pilot

Start with a change your team already has to approve.

Select one representative AI system, its approval baseline and a meaningful change. Define the evidence and decision outcomes that would make a pilot useful.

Scope, integrations, data handling and acceptance criteria are confirmed before any evaluation.

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