If you sit in enterprise boardrooms today, the pressure weighing on CIOs comes down to two things: Velocity and ROI. Boards expect a mature agentic AI strategy and a definitive timeline for deploying autonomous systems that deliver measurable business impact.
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How enterprises coordinate and scale AI in production.
In multi-agent systems, agents can be individually correct and still produce a wrong result when they work together. One agent’s output becomes another agent’s context. Information can be lost or misinterpreted during a handoff, shared state can drift, and agents can get stuck in loops or deadlocks. When several agents rely on the same underlying model, they may even reinforce the same mistakes rather than catch them.
The hardware and platforms underneath enterprise AI.
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Enterprise tech coverage through the lens of AI adoption.
Cohere is going after two common enterprise agent problems with North 2, the new version of its enterprise agent platform. It adds user quotas, rate limits, organization-wide caps and a redesigned agent harness. According to Cohere, North 2 uses memory to keep an agent's context across sessions and gives teams a shared library of knowledge and assets to draw on. It also runs in the cloud, on premises or fully air-gapped.
Our ITSM platform never lacked data, but it hasn’t always been easy to understand what the data was telling us and what actions we should take next, despite all the time spent building catalog items, interpreting data and usage licenses, and maintaining CMDB health.
