About Tool

Agent M is designed to help enterprises create task-specific conversational agents using large language models. Rather than having a single generic chatbot, Agent M enables you to spin up multiple “sub-agents” each trained for a specific domain like customer support, sales, claims handling, etc. These agents can make API calls, fetch data from your knowledge base or system, and coordinate complicated conversations. The framework also allows you to build chatbots and voicebots with no-code tools, making it easier to deploy LLM-powered automation within contact centers or customer-facing applications.

Key Features

• Master-agent orchestration: coordinate multiple LLM agents under one central “Agent M”
• Use case–specific agents: build agents with domain-specific skills such as support, sales, helpdesk
• Natural language–based API calls: agents can call your internal APIs to read/write data
• Knowledge-base integration: agents can query unstructured data sources like documents, web pages, or internal KB
• Guardrails and safety: define restrictions and controls over agent behavior to control hallucinations
• Multi-channel support: deploy agents over chat, voice (call center), and other channels
• Low-latency LLM for voice: integrates with VoiceGPT for real-time, conversational voice interactions
• No-code studio: configure, train, and manage agents without extensive programming

Pros:

• High flexibility: you can build many specialized agents for different business functions
• Better context: agents can pull from your knowledge base to give accurate, relevant answers
• Safe and controlled: built-in guardrails reduce hallucinations and enforce business rules
• Real-time voice and chat: integrates with VoiceGPT for low-latency voice conversations
• Scalable orchestration: Agent M coordinates among sub-agents, making complex workflows manageable

Cons:

• Requires some initial setup and planning to define the right agent structure
• Using advanced features (like API integration) may need developer involvement
• Cost may scale with multiple active agents and high usage
• Fine-tuning or defining domain-specific knowledge may take time

Who is Using?

• Enterprises with complex customer-facing workflows (insurance, banking, support)
• Contact centers wanting AI-powered voice and chat automation
• Product teams building intelligent assistants that need deep integration with internal systems
• Sales or operations teams wanting to automate structured processes (e.g. claims, onboarding)
• AI teams looking to deploy multiple LLM-driven agents with orchestration and data access

Pricing

Floatbot does not clearly publish a fixed price for Agent M on their site pricing is likely custom/enterprise-based. Costs will depend on the number of agents, usage volume (voice/chat), integrations, and SLAs. (You may need to consult Floatbot for a tailored quote.)

What Makes It Unique?

Agent M’s strength lies in its orchestration ability it’s not just one chatbot, but a framework to deploy many specialized agents under a unified “master” controller. With built-in API access and knowledge-base querying, it enables deep, context-rich conversations. The combination with VoiceGPT gives you a powerful, real-time voice AI option too.

How We Rated It

• Ease of Use: ⭐⭐⭐⭐☆
• Features: ⭐⭐⭐⭐⭐
• Value for Money: ⭐⭐⭐⭐☆
• Overall: ⭐⭐⭐⭐☆

Agent M is a robust and enterprise-ready solution for building and managing multiple AI agents in a coordinated way. Its orchestration framework, combined with data access and voice capabilities, makes it ideal for businesses needing intelligent automation across chat and call centers. If you're looking to scale AI-driven customer interactions in a controlled, structured manner, Agent M is a highly capable platform to consider.