LiveAgent vs Hatz AI
Side-by-side trajectory, velocity, and editorial themes.
LiveAgent runs a heavy maintenance cadence while quietly wiring in AI-agent billing
LiveAgent ships frequent, dense point releases dominated by bug fixes, security hardening, and performance work across its ticketing, chat, and call surfaces. Underneath the maintenance stream, it is standing up the plumbing for AI agents: credit-pool provisioning, AI budgets and top-ups, recent LLM model support, and signed MCP download links so agents can reach ticket attachments. A parallel API v3-to-v4 transition is underway, with datetime standardization and relabeled API keys.
The direction is incremental on two tracks: keep grinding down a long bug and access-control backlog, and build the commercial and integration scaffolding for AI agents rather than a headline AI feature. Expect the v4 API to keep firming up and the AI budget/credit system to move from provisioning toward customer-facing usage. This is groundwork, not a pivot.
Next releases likely continue the fix-heavy cadence while extending AI-agent capabilities on top of the now-provisioned credit pools, and advancing the v4 API surface.
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.
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