3.1.3. Prompt and Response Agents
Prompt and response agents are the simplest agent type — they receive a user input, process it using AI, and return a response. No multi-step workflows, no background monitoring, no persistent state between interactions. Each interaction is stateless and self-contained.
Despite their simplicity, prompt-and-response agents serve critical business functions. They're the right choice when the interaction pattern is straightforward: a user asks a question, the agent answers. FAQ bots, knowledge retrieval agents, and simple data lookup agents all fit this pattern.
Design Characteristics:
| Characteristic | Prompt-and-Response Behavior |
|---|---|
| Initiation | User sends a message |
| Duration | Single request-response cycle |
| State | Stateless (or minimal conversation context) |
| Decision-making | Response generation only |
| Human involvement | Every interaction is user-initiated |
| Completion | Response delivered = interaction complete |
When to Use Prompt-and-Response:
- The user needs a quick answer, not a workflow execution
- No downstream actions are required (no record updates, no approvals, no notifications)
- The interaction is self-contained — previous interactions don't inform the current one
- Response speed is critical — minimal orchestration overhead
When NOT to Use:
- The user's request requires multi-step processing ("process this invoice" = task agent)
- The interaction needs to persist state across messages within a conversation
- Actions need to be taken on external systems based on the response
- The scenario requires proactive behavior (= autonomous agent)
Design Considerations:
Prompt-and-response agents still require careful design:
- Knowledge sources must be configured to ground responses in accurate data
- System prompts define the agent's persona, boundaries, and response format
- Fallback behavior determines what happens when the agent can't answer (escalate to human, suggest related topics, or acknowledge the limitation)
- Output quality depends on prompt engineering — clear instructions, constraints, and formatting guidelines
Exam Trap: The exam may describe a scenario that sounds like it needs a task agent ("help the user find the right product") but is actually a prompt-and-response pattern — the agent recommends a product based on the user's description, and the user makes the decision. If no downstream action is automated, it's prompt-and-response.
Reflection Question: A company wants an agent that answers employee questions about company policies by referencing an internal knowledge base. No actions need to be taken — just answers. But employees also want to ask follow-up questions in the same conversation. Does this change the agent type? Why or why not?
