Agents Overview

Understand AI agents in ThinkFleet — what they are, how they work, and when to use them.

5 min readAI Agents

Agents Overview

Agents are AI-powered assistants that can reason about tasks, use tools, and interact with users through natural conversation. While flows handle deterministic, event-driven automation, agents handle tasks that require judgment, planning, and dynamic decision-making.

What is an Agent?

An agent in ThinkFleet combines a large language model (LLM) with tools, memory, and a persona to create an assistant that can:

  • Converse with users in natural language
  • Reason about what actions to take
  • Use tools to interact with external services
  • Remember previous conversations and user preferences
  • Search a knowledge base for relevant information

Agents vs Flows

Feature Flows Agents
Trigger Events (webhook, schedule, app) User messages
Logic Deterministic, predefined steps LLM-driven, dynamic
Branching Explicit conditions AI decides based on context
Best for Predictable, repeatable tasks Open-ended, judgment-based tasks
Execution Runs to completion Iterative conversation

In practice, agents and flows complement each other. Agents can invoke flows as tools, and flows can trigger agent conversations.

Agent Types

ThinkFleet supports two agent types:

Chatbot

A Chatbot is a conversational assistant focused on answering questions and providing information. Chatbots are ideal for:

  • Customer support widgets
  • Internal knowledge assistants
  • FAQ bots
  • Document Q&A

Chatbots typically rely on a knowledge base and have limited tool access.

Agent

An Agent is a more capable assistant that can plan, use multiple tools, and take multi-step actions. Agents are ideal for:

  • Task automation (e.g., "Create a Jira ticket for this bug and notify the team")
  • Data analysis (e.g., "Pull last month's sales data and summarize trends")
  • Workflow orchestration (e.g., "Onboard this new customer across all our systems")
  • Multi-step research (e.g., "Find all open invoices over 90 days and draft follow-up emails")

How Agent Execution Works

When a user sends a message to an agent:

1. User message arrives
2. System prompt + tools + memory are assembled
3. LLM processes the message
4. If the LLM calls a tool:
   a. ThinkFleet executes the tool
   b. The result is sent back to the LLM
   c. The LLM may call more tools or generate a response
5. Final response is returned to the user
6. Conversation is stored in memory

This loop can repeat multiple times in a single turn. An agent might call 5-10 tools before formulating its final response, depending on the complexity of the request.

Tool Selection

The LLM automatically decides which tools to use based on:

  • The user's request
  • The tool descriptions and parameters
  • The system prompt instructions
  • Previous conversation context

You don't need to write branching logic or decision trees. The LLM handles routing.

Agent Components

System Prompt

The system prompt defines the agent's behavior, personality, and rules. It's the most important configuration for controlling agent quality.

You are a helpful customer support agent for Acme Corp.

Rules:
- Always greet the customer by name
- Check the knowledge base before answering product questions
- Escalate billing issues to the billing team
- Never share internal pricing formulas
- Be concise but thorough

Tools

Tools are the actions an agent can take. ThinkFleet supports multiple tool sources:

Source Description
Flow tools Your ThinkFleet flows, exposed as callable tools
Piece tools Individual piece actions (e.g., "Send Slack message")
Built-in MCP tools Pre-built services like email, web scraping, database queries
External MCP servers Third-party MCP-compatible tool servers

Knowledge Base

Agents can search uploaded documents to ground their responses in your data. See Knowledge Base Overview for details.

Memory

Each agent maintains per-user conversation history and can use semantic search to recall relevant past interactions. See Agent Memory for details.

Persona

A persona is a preset configuration that bundles a system prompt, tool selection, and behavioral profile. ThinkFleet includes 20+ system personas for common roles (Developer, QA, Accountant, Research, etc.).

Deployment Options

Chat Widget

Embed an agent as a chat widget on your website:

<script src="https://your-instance.thinkfleet.com/chat/widget.js"
  data-agent-id="agent_abc123"
  data-theme="light">
</script>

API

Interact with agents programmatically via the REST API:

curl -X POST https://your-instance.thinkfleet.com/api/v1/agents/{agentId}/chat \
  -H "Authorization: Bearer {apiKey}" \
  -H "Content-Type: application/json" \
  -d '{"message": "What are my open tickets?"}'

ThinkFleet Dashboard

Use agents directly from the ThinkFleet UI in the Tasks section.

Use Cases

Use Case Agent Configuration
Customer Support Knowledge base + ticketing tools + escalation flows
Sales Assistant CRM tools + email tools + knowledge base
DevOps Bot GitHub + Jira + monitoring tools + deployment flows
Data Analyst Database query tools + charting tools + export flows
HR Onboarding HRIS tools + email templates + checklist flows

Next Steps