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When a contact asks something you did not anticipate, a scripted Flow has no path to follow. AI Agents solve that problem by replacing the explicit script with a language model that reads the contact’s message, reasons about it, and generates a contextually appropriate reply. This page explains how Agents work, how to configure one, and how to integrate it with the rest of Fliqr AI.

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Overview

An AI Agent is a runtime configuration that wraps a language model with:
  1. A system prompt — instructions that define the agent’s role, constraints, and output format.
  2. A knowledge base — documents, URLs, or text the model can retrieve to answer factual questions.
  3. Custom User Fields (CUFs) — per-contact data the model reads and writes during the conversation.
  4. Function calls (tools) — actions the model can invoke, such as looking up an order or updating a field.
  5. A fallback Flow — the Flow that takes over when the Agent cannot handle a request.
Unlike a Flow, an Agent does not branch on conditions you pre-define. It decides at runtime how to respond, which makes it capable of handling requests you never explicitly programmed.

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Supported Models

Fliqr AI supports the following models. Each has different latency, capability, and cost characteristics: You can switch the model per Agent without changing the system prompt. Use a lighter model (GPT-4o mini, Gemini 1.5 Flash) during development to reduce token costs, then evaluate a more capable model before going live.

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Agent Components

The system prompt is the primary lever for controlling Agent behavior. A well-structured prompt follows this pattern:
  1. Role — state who the agent is and what company it represents.
  2. Constraints — what the agent must not do (e.g., “never discuss competitor pricing,” “always respond in the user’s language”).
  3. Output format — how responses should be structured.
Example (plain-text response):
Example (structured JSON response with quick replies):
Instructing the Agent to return structured JSON lets Fliqr AI render the response as quick-reply buttons, cards, or carousels — without a Flow.

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Agent vs. Flow: When to Use Each

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How to Create an Agent

[SCREENSHOT: AI Agent editor showing system prompt, model selector, and knowledge base tabs]
1

Navigate to AI Agents

In the left sidebar, click AI Agents, then click Create New Agent.
2

Name your Agent and select a model

Give the Agent a descriptive name (e.g., “Support Agent — Tier 1”). Select the LLM model from the dropdown. If you are unsure, start with GPT-4o mini or Gemini 1.5 Flash.
3

Write the system prompt

In the System Prompt tab, write instructions following the role → constraints → output format pattern. Be explicit: the model follows your instructions literally, so vague prompts produce inconsistent results.
4

Add a knowledge base (optional)

Switch to the Knowledge Base tab. Click Add Source and provide a URL, upload a file, or paste text. Fliqr AI indexes the content and makes it available to the Agent automatically.
5

Configure functions (optional)

In the Functions tab, enable any built-in functions you need or add a custom HTTP function with its JSON Schema. Write a clear description for each so the model knows when to invoke it.
6

Test in the playground

Click Open Playground. Send test messages that represent real user inputs. Verify that the Agent responds within your constraints, uses the correct language, and invokes functions at the right moments.
7

Assign or trigger the Agent

To make the Agent the default responder for a channel, go to Settings → Channels, select the channel, and set this Agent as the Default Agent. To trigger it from a specific point inside a Flow, use the AI Agent block and select this Agent by name.

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Structured JSON Output

Agents can return structured JSON responses that Fliqr AI renders as interactive UI elements — quick-reply buttons, cards, carousels, and image galleries. To enable this, instruct the Agent in the system prompt to respond in a specific JSON schema. This approach lets a single Agent drive rich, interactive conversations without a Flow, while still benefiting from the model’s ability to handle any input.
Agents consume tokens with every message exchange. A long conversation with a large knowledge base can accumulate significant token usage. Monitor your consumption in Settings → AI Usage and set monthly budget alerts to avoid unexpected charges.
Start your system prompt with the agent’s role, then add constraints, then specify the output format. This order mirrors how a human would brief a new team member and produces the most consistent results.

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What’s next

AI Agents overview

Nested guides for setup, tools, multimodal, and handover.

Create an Agent

Step-by-step: provider, prompt, files, and activation.

Functions & MCP

Call APIs and attach MCP servers for live data.

Flows

Combine scripted Flows with AI Agents.