🧭 Agent Guidance Block

The agent_guidance block provides optional, non-enforceable hints to agents consuming a .llmfeed.json feed.

Unlike agent-behavior specifications (which may define normative requirements), this block is intended to help agents:

✅ interpret author intent
✅ adapt interaction style
✅ adjust reasoning depth or behaviour
✅ surface explanations to the user

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🎯 Purpose

Feeds may include agent guidance to:

• Suggest interaction constraints.
• Provide ethically or contextually important signals.
• Offer hints for UX / presentation.
• Recommend caution in handling sensitive content.

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🛠️ Example

"agent_guidance": {
  "max_inference_depth": 3,
  "interaction_tone": "formal",
  "consent_hint": "Ask the user before accessing sensitive information",
  "risk_tolerance": "low",
  "preferred_explanation_style": "bullet-points",
  "custom_notes": "This feed relates to user financial data. Be cautious and transparent."
}

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📚 Fields

Field	Purpose
max_inference_depth	Suggests limiting depth of reasoning/inference
interaction_tone	Preferred tone (e.g. formal, friendly)
consent_hint	Suggests when to seek human consent
risk_tolerance	Recommended risk posture (low, medium, high)
preferred_explanation_style	UX hint (e.g. bullet-points, summary, narrative)
custom_notes	Free-text notes for agent developers

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🚦 Usage

Agents SHOULD treat agent_guidance as non-binding.

However, if the feed is properly signed and certified by a trusted authority, agents MAY:

✅ Increase the confidence level given to the guidance.
✅ Prioritize alignment with the suggested behaviours.
✅ Surface to the user that these are trusted recommendations.

If present, agent_guidance MAY influence:

• Prompt framing
• UX presentation
• Decision thresholds
• Interaction flow

It SHOULD be surfaced (if applicable) to the user or agent operator.

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📡 Summary

The agent_guidance block complements more enforceable blocks (trust, agent-behavior) by offering soft, contextual hints.

When the feed is signed and certified, these hints gain additional trust weight and can help shape more intent-aligned agent behaviour.

Its adoption helps create a more intent-aware, human-aligned Agentic Web.

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