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Agent M
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Chatbots (327)

Agent M Verified Tool

Floatbot Agent M provides AI-agent capabilities for conversational or customer workflows. Teams should curate sources, test accuracy and privacy and provide human escalation for consequential cases.

Last Update: August 20, 2026

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Starting price Free + from $99/yr

Tool Information

Floatbot Agent M provides AI-agent capabilities for conversational or customer workflows. Teams should curate sources, test accuracy and privacy and provide human escalation for consequential cases.

Use authorized, non-sensitive inputs and minimum permissions. Configure privacy, retention, sharing, disclosure, accessibility, export, moderation and spending controls. Test representative cases, verify facts, calculations, citations, code and generated media, preserve originals and require accountable human approval before publication or action.

A free option is available and paid access starts from $99 per year. Agents, conversations, integrations, renewal and taxes vary.

AI output may be inaccurate, biased, derivative, insecure or misleading. Review consent, copyright, training and retention terms, renewals, refunds, platform rules and applicable law. Health, education, security, employment and customer-facing workflows require qualified human review.

F.A.Q (3)

Floatbot Agent M provides AI-agent capabilities for conversational or customer workflows. Teams should curate sources, test accuracy and privacy and provide human escalation for consequential cases.

Verified pricing: Free + from $99/yr. A free option is available and paid access starts from $99 per year. Agents, conversations, integrations, renewal and taxes vary.

Use authorized, non-sensitive inputs and minimum permissions. Configure privacy, retention, sharing, disclosure, accessibility, export, moderation and spending controls. Test representative cases, verify facts, calculations, citations, code and generated media, preserve originals and require accountable human approval before publication or action.

Pros and Cons

Pros

  • Builds enterprise AI agents for voice; chat; and agent assist
  • Supports multi-agent orchestration for complex conversational processes
  • Provides a no-code studio for creating and training agents
  • Includes predefined skills for FAQs; scheduling; ordering; tickets; and CRM access
  • Allows teams to create custom skills for specialized workflows
  • Connects agents to CRMs; ERPs; and custom applications through APIs
  • Supports retrieval-augmented cognitive search over internal knowledge
  • Works with GPT; Claude; Gemini; Llama; and custom enterprise models
  • Deploys across chat; voice; SMS; email; and more than 15 digital channels
  • Includes session management and memory for longer conversations
  • Offers guardrails; PII redaction; and behavior controls
  • Provides a service terminal for testing and debugging before launch
  • Supports secure access controls and encrypted enterprise deployments
  • Can escalate or assist human agents with suggestions and summaries
  • Offers both low-code convenience and pro-code extension options
  • Supports regulated deployment requirements including SOC 2; HIPAA; and GDPR controls

Cons

  • Pricing is not published and requires sales contact
  • Enterprise implementation can require substantial workflow design
  • No-code setup does not eliminate integration and data preparation work
  • Multi-agent orchestration introduces debugging complexity
  • Voice and omnichannel deployments need careful latency testing
  • Claims about eliminating hallucinations should be independently validated
  • RAG accuracy depends on source quality and retrieval configuration
  • Custom API actions can create security and permission risks
  • Model-provider fees and channel costs may apply separately
  • Regulated organizations still need their own compliance assessment
  • Production guardrails require continuous testing and maintenance
  • The broad feature set may be excessive for simple chatbot needs
  • Human handoff rules must be designed for ambiguous or high-risk cases
  • Public independent review coverage is limited for Agent M specifically
  • Teams may face vendor dependence around orchestration and channel tooling
  • Monitoring many agents and integrations adds operational overhead

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