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Key Terms and Concepts

Below are the core components that define the Flametree platform and how AI Agents operate.

🧠 AI Agent​

AI Agents are at the heart of Flametree — intelligent assistants that interact with customers, employees, and partners through natural conversation. They understand intent, answer questions, automate workflows, and even complete transactions using large language models.

Flametree includes several agent types, each built for a specific mode of interaction:

  • Text Agent — manages written conversations, keeps context, follows workflows, and integrates with tools.
    Best for support, lead qualification, and process automation.

  • Voice Agent — handles real-time phone calls with instant responses.
    Best for voice assistants and customer support.

  • Call Analytics Agent — analyzes recorded calls, extracts key data, and generates insights.
    Best for quality assurance and compliance.

Flametree agents are modular and easy to customize — enabling enterprises to automate text, voice, and analytics workflows without coding.

🧩 Tools and Skills​

Tools and Skills extend an agent’s capabilities — enabling external integrations and internal automation within Flametree.

Tools perform external operations such as sending messages, creating tickets, or querying APIs. Each tool is a single executable action, defined in YAML (plugin) and implemented in Python. Agents invoke tools contextually during conversations to interact with databases or third-party systems.

Skills define internal logic that runs automatically on workflow events — for example, when entering or leaving a state. They help structure conversations, enforce logic, and trigger background actions without manual control.

Tools vs. Skills

  • Tools — agent-driven actions, declared in available_tools. Example: “Create a ticket when a user reports an issue.”
  • Skills — system-driven automations, defined in init_skills or final_skills. Example: “Load customer data when conversation starts.”

🔗 Integrations​

Connections to external systems and services including communication channels (email, WhatsApp, web chat), AI models (LLM, STT/TTS, embedders), and external systems (CRMs, databases) that enable comprehensive AI agent functionality.

💬 Channels​

Communication pathways that connect AI agents to external messaging platforms and services (such as Telegram, WhatsApp, Email, Web Chat) enabling users to interact with AI agents through their preferred communication methods.

🔄 Workflow​

A Workflow defines how an AI Agent manages conversation and automation logic — how it moves between states, reacts to input, and performs actions. It follows a state machine model where each state represents a distinct step in the process, and transitions define how the agent proceeds.

States​

Each state defines what the agent does and what tools it can access. A state typically specifies:

  • Actions on entry and exit
  • Available tools or skills
  • Conditions for moving to another state

States can be conversational or operational depending on the workflow design.

Transitions​

Transitions control how the agent moves between states. They are triggered by user input, tool results, or internal logic. Each transition defines a trigger, an optional condition, and a target state.

Tools​

Within a state, the agent can use tools — built-in or custom — to perform external actions (for example, check data, send messages, call APIs). Tool outputs can be stored as observations that affect subsequent transitions.

📚 Knowledge Bases​

Knowledge Bases in Flametree are structured repositories of company information — documents, files, and data sources that AI Agents use to give accurate, context-aware answers. They help ensure every response is consistent, factual, and aligned with verified content. Flametree includes two knowledge systems, each built for a different type of interaction.

Fast Access Knowledge Base​

A lightweight reference for quick, factual replies. It holds short Q&A pairs, policy notes, and simple facts that agents can access instantly without searching. Best for common questions about hours, pricing, or policies.

Vector Knowledge Base​

An intelligent, searchable repository for complex information. It uses Retrieval-Augmented Generation (RAG) to locate and summarize relevant details from larger sources such as manuals or product documentation. Best for in-depth, document-based answers.

🎯 Campaigns​

Campaigns are automated outreach workflows that use AI agents to communicate with contacts across multiple stages. They combine structured communication steps, programmable logic, and contact data to support customer acquisition, retention, or debt collection.

Each campaign consists of three core layers — stages, strategies, and contacts. Stages define the communication flow, strategies control decision logic, and contacts store the data being processed.

Stages​

A stage is a single step in the communication sequence, using a specific channel such as email, WhatsApp, or phone. Each stage has its own agent configuration and message template. Typical flows start with an email, then follow up via WhatsApp, escalate to a phone call, and finish with a final reminder.

Strategies​

Strategies are Python scripts that define campaign logic — when to send messages, how to move between stages, and how to handle results. They contain the business rules that drive automation and decision-making.

Contacts​

Contacts are the campaign’s active records. Each includes identifiers (name, email, phone, IDs), imported parameters for personalization, and results collected during the process. This data allows campaigns to adapt dynamically based on prior interactions.

💬 Sessions​

A session is an individual conversation between a user and an AI agent that can be monitored, analyzed, and used to improve performance and quality.

Each session stores the full message history, parameters provided at the start, results collected during the interaction, and its current status (active or closed).

A session begins when a user starts a conversation or the agent reaches out, continues through the active exchange of messages, and ends when the interaction is completed. Once closed, session data becomes available for review and analysis.

👤 Identity​

User and tenant management system that controls access permissions, roles, and organizational settings within the Flametree platform.

🏢 Tenant​

An organizational account or workspace within Flametree that contains all AI agents, knowledge bases, integrations, and user access for a specific company or team. Each tenant has its own data isolation, billing, and user management. Users are assigned roles within a tenant (Tenant Admin, Tenant User, or Tenant Viewer) that determine their access permissions.