Miro's overall agentability score of 39/100 on the homepage indicates significant barriers for autonomous AI agents attempting to parse and navigate the site. The pricing page performs moderately better at 50/100, while documentation sits at 36/100, suggesting inconsistent machine-readiness across key user journeys.

The audit reveals two critical weaknesses: transparency scored 0/100, meaning agents receive no confidence indicators, source citations, or audit trails to validate outputs, and shadow UI avoidance scored just 15/100, indicating heavy reliance on dynamic elements that obscure functionality from automated systems. Defaults (26/100) and chunking (34/100) also lag, while control (73/100) and status (65/100) represent relative strengths.

These scores suggest that while Miro provides reasonable agent control over interactions, the site lacks the structured metadata, semantic clarity, and explainability features necessary for reliable autonomous operation.

To improve agentability, Miro should prioritize the following structural and transparency enhancements:

• Implement JSON-LD structured data using schema.org vocabularies to describe primary entities on each page, enabling agents to reliably extract product information, pricing, and feature details.
• Establish a single

  per page and maintain contiguous heading hierarchies without skipped levels, allowing agents to construct accurate content outlines.

• Add confidence indicators and source references to generated outputs, such as search results or recommendation features, so agents can assess reliability and trace provenance.
• Pre-fill form inputs with sensible defaults wherever reasonable values exist, reducing the decision burden on agents during onboarding or configuration workflows.
• Rephrase section headings as direct questions (e.g., "How do I cancel?" instead of "Cancellation") to improve semantic clarity for natural language processing.
• Expose an activity log or "why this result" mechanism in machine-readable format (such as a JSON event stream) to provide agents with an auditable record of system actions and recommendations.