AI is no longer confined to experiments and isolated productivity tasks. Adoption is rapidly broadening into consequential enterprise functions, with organizations expecting agents to increasingly coordinate work across applications, systems and business processes.
Getting AI into production is, for the most part, yesterday’s challenge. Now, the architecture of these workflows is changing too. Instead of having one AI tool complete one task, organizations are moving into an era where agents collaborate, call tools and applications, exchange context and hand work to one another.
So, what does it take to manage AI when individual agents become part of an interconnected agentic ecosystem? An upcoming survey report from OutSystems and KPMG explores that transition: 71% of respondents expect enterprise applications to evolve toward agents coordinating work across systems, teams and business processes or helping business users complete more complex, multi-step workflows.
Adoption gives way to orchestration
Isolated agents are typically evaluated on their own outputs. However, while that might validate their technical capability, deploying them in a production environment is another matter entirely, not least because most real-world workflows involve multiple applications, datasets and other agents. That introduces dependencies where agents may exchange context, invoke APIs or interact with enterprise applications and depend on outputs generated earlier in the chain.
Gonçalo Borrêga, SVP of Product, AI and AppDev at OutSystems, describes this as an orchestration challenge. “The higher the goal is, the more coordination effort is involved. That’s where we get into orchestration,” he says. “One error here can lead to 10 errors down the line.”
That transition is already visible. For instance, one global logistics organization is moving from an agent that analyzes application logs toward a multi-agent model where specialized agents collaborate and share context in real-time.
Complexity raises the stakes for control
As these systems become more interconnected, the challenge shifts from governing individual AI projects to maintaining control across the full workflow. More than two-thirds of survey respondents cite security, permissions and policy enforcement across agents as a leading orchestration hurdle.
The implication is that, while most organizations do have governance, that doesn’t necessarily mean their processes work operationally across the entire workflow, especially those involving coordination between multiple agents and other tools. “You still need to guarantee security, observability, accuracy and quality across all of those agents, and you need to coordinate and orchestrate that work across multiple platforms,” says Borrêga.
Some organizations are already taking a more structured approach. For instance, a major digital bank defined authorized AI tools and preconfigured workflows before letting teams deploy autonomous agents, thus reducing the security risks that come with fragmented adoption while keeping room for innovation.
Greater control shouldn’t mean less choice
The agentic enterprise will inevitably be heterogenous, with different agents, applications, models and data services all living on different platforms. Borrêga argues that simply adopting agentic AI is not a straight path to building an agentic enterprise when agents are being added to application and data landscapes that are already fragmented.
Organizations need that flexibility, not least because what might be the best tool for a given use case today might not be the same tomorrow. Therefore, they shouldn’t attempt to standardize everything on one vendor, but rather establish consistent engineering, orchestration and governance across the environment.
The goal is portfolio-wide visibility and a consistent control layer that applies policies across the environment while preserving freedom to choose the agents, tools, platforms and models best suited to each use case.
Business outcomes have become the real test
As enterprise AI matures, organizations are increasingly measuring success by impact on business outcomes, rather than narrow activity-based metrics. In the survey, 64% use error reduction to evaluate AI’s impact, while 60% measure compliance or risk reduction.
Borrêga challenges the narrow focus on productivity gains and instead shifts the focus to business outcomes: “The fact that you can just generate more text or more code doesn’t yield a good outcome by default.” Instead, the more relevant question is whether the newly redesigned agentic process meaningfully improves a business outcome, such as reducing costs, improving customer experience, reducing risk, or helping the organization respond to market pressures faster.
In light of the survey’s findings, it’s clear that the next stage of enterprise AI will be defined less by how many agents an organization has in operation and more by how effectively it connects them to trusted business systems. As applications themselves become increasingly agentic, organizations will need to combine orchestration with governance, enterprise context, technology choice and outcome-based measurement.
Dive deeper into the data with the complete 2026 OutSystems and CIO Dive survey report, coming soon.
