Bolta.agent.mention

Goal

Handle @mention interactions where users ask agents for quick feedback on posts/drafts. This is lightweight agent interaction with read-only tools for conversational feedback.

Which Agents Use This

• content_creator — Punch up hooks, suggest alternatives, provide creative feedback
• reviewer — Check brand compliance, voice profile matching, strategy alignment
• analytics — Provide performance context, timing suggestions, data insights
• engagement — Suggest engagement tactics, audience resonance predictions
• All agent types can provide their specialized perspective via @mentions

Hard Rules

1. MUST use read-only mode (no write tools like approve, publish, edit)
2. MUST provide post/draft context to agent
3. MUST respond as comment (not edit directly)
4. MUST NOT grant write access to mentioned agents
5. Response time should be < 5 seconds

Steps

1. Parse mention

• Extract agent name from @mention in comment
• Identify which agent is being requested
• Load mention_comment_id to get user's specific question

2. Load context

• bolta.get_post(post_id) → get full post content
• Load comment thread for context
• If content job: bolta.get_voice_profile(voice_profile_id) for reference

3. Invoke agent in mention mode

• Set mode="mention" (read-only tools only)
• Provide context: post content, comment thread, user question
• Agent analyzes and formulates response

4. Post response

• bolta.add_comment(post_id, response_text, reply_to=mention_comment_id)
• Response appears as threaded reply to original mention

5. Return confirmation

• Return comment_id and response_text

Output

{
  "comment_id": "uuid",
  "response_text": "This hook is generic. Try: 'We cut onboarding from 2 weeks to 2 days.'"
}

Failure Handling

• If agent_id not found: return error "Agent not found"
• If post_id not found: return error "Post not found"
• If agent response times out (> 10s): return "Agent is thinking... check back in a moment"
• If agent has no relevant context: agent responds "I don't have enough context to help with this"

Agent-Specific Behaviors

@HypeMan (Content Creator):

• Focuses on engagement, hooks, energy
• Example: "This hook is generic. Try: 'We cut onboarding from 2 weeks to 2 days.'"

@QABot (Reviewer):

• Focuses on brand compliance, voice profile matching
• Example: "Remove 'leverage' (not in voice profile). Otherwise looks good."

@AnalyticsBot (Analytics):

• Provides data context, timing suggestions
• Example: "Thursday posts get 2x engagement. Consider rescheduling to Thursday 9am."

@EngageBot (Engagement):

• Suggests audience resonance tactics
• Example: "Add a question at the end to boost comment engagement."

Example Usage

Scenario: User asks HypeMan to punch up hook

User comment: "@HypeMan can you make this hook stronger?" Post content: "We just launched a new feature..."

Agent response: "This hook is announcement-focused. Try benefit-led instead: 'We cut onboarding from 2 weeks to 2 days.'

This is more specific and compelling. Lead with the transformation, not the release."

Scenario: User asks QABot for voice check

User comment: "@QABot does this match our voice?" Post content: "Leverage our platform to optimize your workflow..."

Agent response: "❌ 'Leverage' not in voice profile (avoid corporate jargon) ❌ 'Optimize' too vague ✅ Rest matches voice

Suggestion: 'Use our platform to save 5 hours per week on admin tasks.'"