Cohere's North Automations Lets Employees Build Multi-Step AI Workflows Without Code
Cohere's North platform gains automated multi-step workflow orchestration, letting any employee build governed AI pipelines without writing code
- Cohere launches North Automations, enabling any employee to build multi-step AI workflows using plain language -- no coding required.
- Per-step model routing lets teams assign different AI models to different workflow steps, balancing cost and performance across complex pipelines.
- Governance-first design includes human-in-the-loop approval gates, token usage monitoring, versioning, and audit-ready logs for enterprise compliance.
- Integrates with Gmail, Slack, Salesforce, Outlook, Linear, and any MCP server, plus Cohere's own SDK for custom tool connections.
- Available immediately to all existing North customers; targets a $550B agent orchestration market opportunity identified by Gartner.
- Cohere hit $240M ARR in 2025 with 50%+ quarter-over-quarter growth and a $7B valuation, with a potential IPO on the horizon.
Cohere has launched North Automations, a new layer on top of its enterprise AI platform that lets employees build and run multi-step automated workflows using plain language, no engineering background required. The feature is available immediately to all existing North customers, and it represents Cohere's clearest move from model provider to owner of the full enterprise AI stack.
The coordination problem
The pitch for AI agents has always been compelling on paper: autonomous software that can reason, take action, and hand off tasks across systems. Inside most enterprises, the reality has been messier. Deployments tend to produce isolated, task-specific bots that handle one narrow job well but cannot coordinate across teams or systems.
Three structural problems drive this gap. First, one-off agents solve narrow tasks rather than end-to-end workflows that reflect how employees actually work. Second, fragmented deployments make it nearly impossible to govern AI usage at scale. Third, running every step of a workflow through the same frontier model inflates costs fast, making per-step model routing a practical necessity rather than a nice-to-have.
Gartner analysts frame this as a structural problem. "Current AI agent implementations are mostly built with individual, task-specific agents that provide incremental benefits, but create a value gap for enterprise-scale AI adoption," write research analysts Anushree Verma and Alastair Woolcock in the Agentic AI Hype Cycle 2026 report. Gartner pegs the agent orchestration opportunity at