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MyAi is a powerful AI platform, but like all AI systems, its outputs depend on the quality of context it operates within. Understanding when to trust its results — and when to verify — is essential for responsible use. This page is designed for all users, but especially for teams evaluating MyAi for enterprise deployment.

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The Trust Equation

The reliability of MyAi outputs is directly proportional to the quality of the context you provide. Three factors drive trust:

Dimension Structure

Well-scoped Dimensions with clear boundaries produce more focused, accurate results. A Dimension that tries to cover everything will produce vague answers.

Canvas & Artifact Quality

The more structured and complete your Artifacts are — Templates with defined fields, Canvases with clear data sources — the more MyAi has to work with.

Skill & Function Precision

Skills that are well-defined and narrowly scoped give MyAi clear capabilities. Overly broad Skills increase the chance of unexpected behavior.
In short: the better you structure your Dimensions, the more you can trust what MyAi produces.

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What MyAi Is Good At

MyAi excels in scenarios where:
  • Context is well-defined. When a Dimension has clear boundaries, relevant Artifacts, and appropriate Skills, MyAi can reason effectively within that scope.
  • Tasks are structured. Template-driven data capture, Canvas generation, and Workflow execution benefit from MyAi’s ability to follow defined patterns.
  • Multiple systems need coordination. MyAi’s strength is orchestrating across tools (CRM, ERP, PLM, databases) that don’t natively talk to each other.
  • Tribal knowledge needs scaling. Codifying expertise into Dimensions and Skills allows MyAi to apply that knowledge consistently.
  • Repetitive processes need automation. Workflows that chain structured steps are highly reliable once tested and validated.

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Known Limitations

Like all large language model-based systems, MyAi’s natural language responses may vary between runs. The same prompt can produce slightly different phrasing or structure. For critical outputs, use Templates and structured Artifacts to enforce consistency rather than relying solely on free-form generation.
MyAi can generate incorrect summaries, misinterpret ambiguous requests, or surface data that doesn’t fully answer the question. This is especially true when:
  • The Dimension lacks sufficient context or Artifacts
  • The question spans multiple Dimensions that aren’t connected
  • The underlying data source has quality issues
Always verify critical outputs — especially financial figures, compliance-related content, and data used for external reporting.
When MyAi pulls data from external systems via API Client or SQL Client, the accuracy depends entirely on the source system. MyAi does not validate the correctness of external data — it surfaces and transforms what it receives.
Data retrieved via API calls or webhook triggers reflects the state of the source system at the time of the request. If your Workflow runs on a schedule, the data may be minutes or hours old depending on the trigger frequency.
When MyAi chains multiple steps — pulling data, transforming it, making decisions, and taking actions — each step introduces a small margin for error. For high-stakes multi-step Workflows, build in checkpoints where a human reviews intermediate results before the process continues.

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When Human Review Is Required

Not every MyAi output needs manual verification. Use this framework to decide:
ScenarioRecommendation
Generating a draft Canvas or report for internal reviewLow risk — review before sharing externally
Executing a tested, validated WorkflowLow risk — monitor via Work Order audit trails
Summarizing data from a single, trusted sourceMedium risk — spot-check key figures
Making decisions that affect customers, compliance, or financesHigh risk — always require human approval
Generating content for external communicationHigh risk — always review before sending
First-time Workflow execution on live dataHigh risk — run in test mode first
MyAi is designed to augment human judgment, not replace it. For any process where an error could have legal, financial, or safety consequences, build human review into the Workflow as a required step.

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How to Improve Reliability

1

Scope Dimensions tightly

A Dimension scoped to “Manufacturing Quality” will outperform one scoped to “Everything in the plant.” Narrow context produces better answers.
2

Invest in structured Artifacts

Templates with well-defined fields, Canvases with clear data sources, and Functions with explicit inputs/outputs all improve consistency.
3

Use Skills deliberately

Only assign the Skills a Dimension actually needs. A Dimension with access to every tool in the system is harder for the AI to reason about effectively.
4

Validate Workflows before going live

Run new Workflows with test data. Review the Work Order audit trail to verify each step produced the expected result.
5

Monitor and iterate

Review Work Orders regularly. If you notice recurring issues — incorrect summaries, missed data, unexpected behavior — refine the Dimension’s context, Skills, or Workflow logic.

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Observability: Work Orders as Your Audit Trail

Every task in MyAi — whether initiated by a user, triggered by a Workflow, or invoked by an external system — is tracked as a Work Order. Work Orders provide:
  • Full execution history — Every step, every tool call, every input and output.
  • Initiator and context — Who or what started the task, and in which Dimension.
  • Status and outcomes — Whether the task completed, failed, or requires review.
MyAi Activity dashboard showing Work Order counts and trends
MyAi Work Orders list showing status, summaries, attunements, and sources
This audit trail is your primary mechanism for verifying that MyAi is behaving as expected. Quality teams can use it for compliance monitoring, and IT teams can use it for system observability.