Plain vs Hatz AI
Side-by-side trajectory, velocity, and editorial themes.
Plain turns Sidekick from a drafting assistant into an agent that acts
Plain is a customer-support platform building an agentic layer — 'Sidekick' — into the core thread workflow. Recent releases moved Sidekick from suggesting to acting: it can take actions across connected tools, start working proactively the moment a thread matches a workflow, and it now answers in Slack. The surrounding plumbing (scheduled workflows, thread fields via the chat widget, machine-user API links to Linear) is all in service of more automation.
The arc points to autonomous, workflow-driven support: AI that investigates, summarizes, drafts, and executes before a human opens the thread. Each release widens either Sidekick's reach (Slack, connected tools) or the triggers that set it off (workflow conditions, schedules), steadily shifting the human role from doing the work to reviewing it.
Expect deeper Sidekick autonomy — more action types and likely approval or guardrail controls — plus more workflow triggers that launch automation without a human in the loop.
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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