Pushwoosh vs Gumloop
Side-by-side trajectory, velocity, and editorial themes.
Pushwoosh ships an MCP server and AI-powered segments — agents can now run the platform.
Two AI moves anchor the recent stream: a ManyMoney AI MCP server that lets Claude Desktop, Cursor, or Windsurf drive a Pushwoosh project end-to-end, and AI-powered segmentation built around natural-language prompts. Around them, Pushwoosh added Telegram as a Customer Journey channel, passkey sign-in, marketing-vs-transactional message typing, resend-to-non-openers, journey change history, custom tracking domains, and a redesigned billing page.
Pushwoosh is doing two things in parallel — making the marketing surface AI-operable from outside the product (MCP) and inside it (NL segments) — while filling out the omnichannel orchestration story with Telegram, transactional toggles, and email-side conveniences. The platform is positioning itself as a backend that humans, internal automations, and external agents all act on equally.
Expect more MCP tool surfaces (campaign creation, journey publishing, analytics queries) plus AI assistance inside the journey builder itself — auto-design a journey from a goal description. Telegram is likely to be followed by additional regional channels like LINE or RCS to round out omnichannel.
Gumloop turns into an MCP control plane: host, proxy, gate, and audit every agent-to-app call.
The headline move is MCP Hosting, Proxying, App Rules & Activity — customers can host their own MCP servers, proxy external ones, set policy-driven app rules, and watch the resulting activity, with Enterprise data drains to S3 or BigQuery as the audit substrate. Around it, the weekly cadence is dense: incognito mode for agent chats, Shared With Me and Organization views for collaboration, per-app account selection, a partner program for referrals, and Gmail triggers extended to any label.
Gumloop is repositioning from an AI-workflow builder into an enterprise MCP runtime — hosting, governance, and observability on top of the agent layer. Each recent release reinforces that thesis: credential pinning per MCP tool, plain-English app policies, audit-log filters, SCIM team/role sync. The bet is that the bottleneck for agent adoption is not capability but control.
Expect Enterprise data drains to extend to common SIEM destinations (Splunk, Datadog) and the App Policies surface to add policy-as-code authoring alongside the plain-English mode.
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