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First Conversation

After creating and configuring an agent, the first conversation validates whether it’s ready for real users — before sharing the public link, installing the widget, or wiring it into an external integration.

The simplest place to test is the Hub (/hub/chat), the organization’s internal chat environment. There you talk to the agent like a user would, and the responses use the model and tools you configured.

Empty Hub with agent selector

On the Hub home screen you see:

  • Composer in the center for the first question.
  • Agent selector right below the composer — pick which agent in the organization will answer.
  • AI model selector — overrides the agent’s default model for this conversation only (handy to compare Claude vs. Gemini without editing the agent).
  • “Or talk to an agent” list below the composer, with agent cards to jump straight into one of them.
  1. Type a simple question and send (Enter).
  2. Observe the first reply: did the agent follow the prompt’s tone? Did it use a tool? Did it show knowledge of the base you connected?
  3. Ask follow-up questions to test context retention:
    • “Can you explain better?”
    • “What if the customer is on the annual plan?”
    • “What are the next steps?”
  4. Push the limits: ask for something out of scope and see if the agent refuses or hallucinates.

What to look for in a good first conversation

Section titled “What to look for in a good first conversation”
  • Clear answer following the configured tone.
  • Clarifying questions when data is missing, instead of guessing.
  • Correct tool usage: if the agent has Search Knowledge Base, it should consult before inventing; if it has Generate Image, it should use it when it makes sense.
  • Transparency when it doesn’t know — preferable to fabrication.
  • No out-of-scope promises (“I guarantee”, “for sure” when there’s no basis).
  • Proportional length — the agent shouldn’t reply with five paragraphs to a simple question.

If any of these signals shows up, the agent isn’t ready:

  • Generic answers, not applying context.
  • Invented information (dates, values, sources that don’t exist).
  • Ignores the knowledge base and improvises.
  • Calls the wrong tool (or none when it should).
  • Excessively long or verbose.
  • Doesn’t understand simple questions inside the agent’s domain.

Go back to the agent editor (/admin/agents/:id/edit) and tweak prompt, tools, model, or knowledge base. Then open a new conversation in the Hub (not the same one, to avoid bias from old context) and try again.

Once the agent passes the Hub test, hook it up to a real channel: