Production-ready
Admissions Conversation Planning Agent
Determines the conversation goal and next-message strategy based on visitor type, intent level, interests, and conversation history. Provides Maya with tactical guidance for each interaction.
Admissions department for Colaberry Enterprise agents
Built by Colaberry
About the Agent
What this agent does, the challenges it addresses, and where it delivers value.
Determines the conversation goal and next-message strategy based on visitor type, intent level, interests, and conversation history. Provides Maya with tactical guidance for each interaction.
Challenges This Agent Addresses
- 1**Admissions**: Maya adapts its tone and strategy per visitor profile
- 2**Sales**: Automatic qualification routing for enterprise prospects
- 3**Marketing**: Personalized engagement based on visitor journey stage
How the Agent Works
Step-by-step operational flow showing how this agent processes tasks end-to-end.
Step 1
Loads the visitor AdmissionsMemory and IntentScore
Step 2
Evaluates visitor_type (ceo, enterprise, high_intent, returning, new)
Step 3
Considers intent level, interests, and conversation count
Step 4
Selects the appropriate goal: executive_briefing for CEOs, conversion for very-high intent, enterprise_qualification for corporate visitors, value_demonstration for pricing-interested, discovery for first-time visitors
Step 5
Returns a strategy string with specific conversational guidance
Execution Modes
Inputs & Outputs
What data this agent consumes and the artifacts or actions it produces.
Input Data
- visitor_id - the visitor to plan for
Deliverables
- Conversation goal (discovery, engagement, conversion, executive_briefing, etc.)
- Next-message strategy with specific tactical guidance
Core Tasks
- Admissions Operations
Systems Connected
Internal systems, APIs, and tools this agent integrates with.
Tools & APIs
Agent Specs
Technical specifications, requirements, and deployment details.
Related Agents
Other agents in the same department or industry.
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