Agent Islands: When Every Department Builds Its Own AI and No One Sees the Whole Picture

Imagine customer service built an agent that triages tickets. Finance built an agent that summarizes invoices. HR built an agent that screens resumes. All three work. None of them talk to each other, and no one knows what happens when something goes wrong. This illustrative scenario captures the coordination problem Camunda calls “agent islands.”

The data behind the pattern

Camunda’s 2026 report draws on a commissioned survey of 1,150 decision-makers and architects at large organizations. The survey covered the US, UK, France, and Germany in autumn 2025. Its published findings say 71% reported agent use, while 48% reported siloed agents. Separately, it reports that 11% of agentic use cases reached production in the preceding year.

These figures have different denominators: 11% is a share of use cases, not organizations or agent users. This is a vendor-commissioned survey, not a census. It points to coordination challenges but does not establish that governance is the sole cause of stalled pilots; model quality, integration, cost, and skills also matter.

Why “one more pilot” does not fix it

The problem with agent islands is not that each one fails to work on its own. The problem surfaces when you try to scale:

• Reliability at scale. An agent can choose different valid paths on repeated runs. The process still needs clear acceptance criteria, reliable records, and defined exception handling across those paths.
• Compliance complexity. Isolated agents are difficult to govern. Shared approval steps, access policies, and escalation paths can help teams apply their documentation and oversight requirements consistently. Orchestration alone does not establish compliance.
• Operational fragmentation. When every department builds independently, no one sees the full picture - which agent accesses which data, who is accountable when something fails, and how to avoid duplicated effort between teams.

A better model alone will not resolve unclear ownership or disconnected handoffs. Those need organizational decisions as well as technical work.

The path to consolidation: orchestration before expansion

The recommended approach breaks down into four principles:

1. An agent is a process participant, not a standalone tool. Instead of the agent “deciding everything,” it performs a defined step inside a process that can be observed and paused.
2. Governance infrastructure built in from the start - approvals, audit trails, and escalation paths - not something bolted on after usage has already scaled.
3. A platform model. A central team manages access to language models and the orchestration layer; business teams that own specific processes build on top of it. This can reduce duplicated infrastructure. Portability still depends on the interfaces, data, and contracts you choose.
4. Systematic human checkpoints - human-in-the-loop before expanding AI authority, not after an incident.

The practical priority is to make the existing agents work together before adding more of them.

What this means for your organization

If you already have two or three agentic AI pilots running in different departments, this is the moment to pause and ask: who sees the whole picture? Is there a shared policy for data access? How is an authorized handoff between HR and finance handled without exposing unrelated personal data?

The practical answer is usually not “shut down the pilots,” but to move them onto a shared orchestration layer - one that lets each department keep building, but within a single framework of governance, documentation, and control. We covered this architecture in depth - the separation between agent and process engine, and the four layers that keep an agent on a safe track - in AI Agents Need a Conductor. That post addresses the technical side of the same problem; this one focuses on the organizational side: how to build the structure that lets the technology work at scale.

This is exactly the work we do at NG Workshop with Israeli organizations: not building one more isolated agent, but connecting what already exists into processes that can be managed, measured, and scaled.

Sources

• Stop Building Agent Islands. Start Orchestrating

Summary

• Camunda’s survey describes widespread experimentation and limited production use; those measures have different denominators and do not prove a single cause.
• Agent islands create reliability, compliance, and coordination problems that worsen as usage scales.
• The fix: turn agents into process participants under shared governance, before expanding further.

Have a few AI pilots running in different departments? Let’s build them a shared orchestration layer