The latest report from OutSystems for you;
OutSystems, a leading Agentic Systems Platform, has released new research in collaboration with CIO Dive and KPMG highlighting a disconnect between enterprise confidence in AI agent governance and actual operational readiness. The report, Engineering and Orchestrating Agentic Systems for Control and Business Impact, found 82% of executives believe their organisations can keep pace with governing, integrating, and monitoring AI agents as they move into production. However, only 26% say they are well prepared to resolve conflicts across agents, systems, and automated workflows.
Agentic AI in the enterprise is reaching an inflection point. As development tools become more accessible and economic pressure intensifies, organisations are shifting from experimentation toward more deliberate, enterprise-wide deployment. Over the next five years, 71% of executives expect applications to coordinate work across systems, teams, and business processes or support more complex, multi-step workflows. This year’s report examines the capabilities enterprises need to engineer, govern, and orchestrate trusted agentic systems that deliver meaningful business outcomes.
“82% of executives say they can keep pace with governing AI agents, yet only 26% say they can resolve conflicts between them,” said Tiago Azevedo, CIO of OutSystems. “That gap is where the real work happens. Having put agents to work across the business, I’ve learned that safety comes from building control into the platform, not reviewing it afterward. Enterprises that enforce policy while agents run will move faster, because they won’t have to stop and ask if they can trust what they built.”
While the report, based on a survey of over 850 IT decision-makers, reveals a gap between governance confidence and operational readiness, enterprise leaders are not starting from scratch: 83% rely on existing IT, security or compliance processes, specialised reviews, or centralised policies and monitoring to oversee AI agents that can take autonomous action across applications or workflows. Still, oversight remains largely human-led and procedural, with just 5% having automated controls, audit trails, and formal human review points in place. The challenge now is translating those governance policies into repeatable runtime controls that can be applied wherever agents operate.
The report also found that as agentic AI adoption expands, the challenge is shifting from building individual agents to managing a complex ecosystem of models, applications, data, and third-party tools. Rather than consolidating around a single vendor, organisations are prioritising flexibility and interoperability. 74% cite integration with existing applications and systems as a priority, 63% point to agent orchestration across systems, and 62% value the ability to switch models, vendors, and platforms over time.
European enterprises face greater concerns about AI costs and reliability
These challenges are particularly pronounced in Europe, where 40% of organisations expect to become more reliant on strategic partners or managed services as AI costs increase, compared with 31% in North America and 26% in Asia-Pacific. European enterprises also express greater concerns about agent reliability, with 49% citing inaccurate, unreliable or unexpected agent behaviour as a major orchestration challenge, compared with 41% in North America and 35% in Asia-Pacific.
Additional key findings from the report include:
- Trust in agentic AI hinges on reliability across entire workflows: Organisations are placing greater emphasis on testing complete agentic systems, not just individual agents, including agent-to-agent interactions, exception handling, conflicting actions, and rollback mechanisms. Eighty-one percent of executives consider testing, evaluating, and validating agent behaviour very or extremely important when selecting AI platforms or tools.
- Enterprise context is a prerequisite for trustworthy AI: More than one-third of respondents (35%) cite data consistency and context sharing as a major orchestration hurdle, underscoring the need for agents to operate using governed enterprise data, application states, and business processes rather than generic model knowledge alone.
- Organisations measure agentic AI by business outcomes: Executives increasingly evaluate success based on operational impact, with error reduction (64%) emerging as the leading impact metric, followed by compliance and risk reduction, employee productivity, and cost reduction. Yet only 12% say agentic AI has accelerated the launch of new products, services, or customer experiences, suggesting that while organisations are already realising operational gains, broader business transformation remains in its early stages.
“Assurance for agentic systems must be engineered from day one—you cannot simply inspect it after the fact,” said Michael Harper, Managing Director at KPMG. “Traditional logging and procedural governance were built for static software, not dynamic AI. To bridge the gap between high confidence and true operational readiness, enterprises need the ability to execute policy-as-code and runtime guardrails. That gives businesses the autonomy to innovate while keeping end-to-end workflows secure, reliable, and focused on business outcomes.”
Building on the launch of the Agentic Systems Platform earlier this year, OutSystems is expanding the platform with new capabilities designed to meet the needs of today’s agentic enterprises. As organisations move from AI experimentation to enterprise-scale deployment, the OutSystems Agentic Systems Platform provides a unified approach to building, orchestrating, and governing trusted agentic systems across complex technology environments.
To view the full report and learn more about OutSystems, visit the dedicated page here.
Be the first to comment