Orchestration

Presented by SAP

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.

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.

AI agents often struggle to assess their own progress and decide what to do next. They can waste compute resources following failed approaches, or they might overlook promising results or stop before completing a task. These problems become more costly as agents take on longer and more complex workflows.

A system that centralizes those guidelines, identifies deviations and allows employees to correct the results could make AI generation more practical for campaigns requiring large numbers of creative assets.

Those findings suggest unresolved gaps in the systems enterprises rely on to judge agent reliability.

While Google supplies its own Gemini models, the agent can already use Anthropic's Claude models, with support for additional proprietary and open-weight AI models planned.

The strategic signal is that Atlassian is deepening its OpenAI relationship — with real dollars behind it — while deliberately keeping its platform model-agnostic, with Anthropic and others very much in the mix.