Workato vs Tigris
Side-by-side trajectory, velocity, and editorial themes.
Workato is becoming the MCP-server vendor for enterprise SaaS — agents call Workato, Workato calls everything else.
Workato's release stream centers on two simultaneous bets. First, a fast cadence of MCP Servers — Dropbox, Freshdesk, Excel, OneDrive, ZoomInfo, Outlook Contacts, and more — turning Workato's connector library into a uniform MCP-accessible surface for agent tools. Second, enterprise control-plane work: RBAC 2.0 with environment- and project-scoped roles, an API Edge Gateway that runs inside the customer's own infrastructure, Developer Portal SSO, and a new China data center for in-region data residency. Community and platform connector updates continue at monthly cadence underneath.
Workato is positioning itself as the integration substrate that agents talk to, not just the iPaaS that humans configure. The MCP server cadence is the clearest signal: every connector that ships as MCP makes Workato a default tool provider for any agent framework, while the connector library itself becomes a moat. In parallel, the enterprise control-plane work — edge gateway, RBAC 2.0, China DC — is plainly aimed at regulated-industry deals where AI-driven integration is otherwise gated by compliance.
Expect MCP coverage to widen across the remaining marquee SaaS connectors (Salesforce, ServiceNow, Workday in MCP form) and a formal 'Workato as agent backbone' positioning at the next user conference. The Edge Gateway is likely to spawn an Edge-deployable MCP runtime as the natural next step for regulated buyers.
Tigris turns its object store into agent infrastructure with Agent Kit, agent-shell, and durable global streams.
Tigris's release stream is a sustained product-marketing push around AI-agent storage primitives. Agent Kit landed as a TypeScript SDK exposing bucket forks, workspaces, checkpoints, and event coordination. agent-shell put a virtual bash environment with persistent storage in front of those primitives. Durable global streams via S2 Lite extended the object store into a streaming substrate suitable for per-agent reasoning traces. Around the launches, case studies and tutorials (Basic Memory, the $10 self-updating knowledge base) make the pitch concrete.
Tigris is staking a position that the right substrate for AI agents is not a database, vector store, or queue — it is a globally-distributed, fork-able object store. Each blog and SDK in this batch reinforces that thesis from a different angle: storage as message queue, fork-per-agent sandboxing, storage-protected agent containment, streams for reasoning traces. The competitive map being drawn includes R2, S3 Express, Backblaze, and the agent-runtime vendors (Modal, E2B), not other databases.
Expect a managed Vector or Lance-index surface on top of buckets to compete more directly with Turbopuffer and Pinecone, and a Python counterpart to the @tigrisdata/agent-shell TypeScript runtime to widen the agent-developer surface area.
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