Hatz AI vs Re:amaze
Side-by-side trajectory, velocity, and editorial themes.
Hatz turns its MSP AI platform into an agent-composition and phone-automation system.
Hatz AI is an MSP-oriented AI workspace: a governed model selector plus agents, workflows, integrations, and AI phone agents, sold through managed-service-provider tenancy. Recent releases push hard on two fronts: making phone agents a real front-line call system (routing, warm transfer, caller memory, business hours, post-call workflows) and making agents composable inside workflows. Model breadth keeps expanding, with Sonnet 5 and seven new LLMs added to the selector.
The direction is from a chat-with-models tool toward an automation platform where saved agents are reusable building blocks and phone agents replace human triage. Governance is a throughline: role-based model, integration, and tool controls, tenant templates, and usage budgets all deepen the MSP multi-tenant control plane. Model selection is increasingly abstracted behind Auto-LLM.
Expect further phone-agent autonomy and more agent-as-step composition across workflows, with continued MSP governance controls and ongoing additions to the model roster.
Re:amaze matures its AI support agent with testing and visibility tools
Re:amaze is a customer-support helpdesk centering its roadmap on its AI Agent. Genuine product posts — multichannel AI Agent across email and SMS, smarter intent detection, and a new set of AI-agent visibility and testing tools — sit interleaved with SEO blog content like help-center writing tips and Prime Day prep. The product is steadily hardening an AI support agent it launched in January 2026.
The arc is consistent: launch the AI Agent, then make it broad and trustworthy. Re:amaze has moved from clearer conversation states to sharper intent detection, to email and SMS coverage, and now to observability and testing so teams can see and validate how the agent behaves before handing it real volume. The recurring blog question — how much support AI should handle — mirrors where the product is steering customers.
Expect continued AI-Agent depth: more channels, deeper analytics on agent performance, and controls governing how much volume teams delegate to automation.
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