Presented by OutSystems
After two years of flashy AI demonstrations, rushed agent prototypes, and exciting predictions, enterprise technology leaders are taking a more pragmatic tone in 2026. In a recent webinar hosted by OutSystems, a panel of software executives and business professionals argued that the most consequential AI work being done now focuses on the practical questions of governance, orchestration, and iteration, along with integrating agents into the systems they’ve spent decades building.
Business leaders are increasingly focusing on fundamentals. The priority is to use new AI technologies
to accelerate productivity, improve delivery and produce measurable business results.
Three elements shape this work:
-
Moving from AI agent prototypes to agent systems that deliver measurable ROI in production
-
The growing role of enterprise platforms in securely governing, orchestrating, and scaling AI agents
-
The rise of the generalist developer and enterprise architect as the most valuable technical profiles in an era of AI-generated code
In this context, the panel discussed governance frameworks, the economics of enterprise AI, and the limits of large language models without orchestration. Ultimately, the conversation focused on how leading organizations are building multi-agent systems based on existing business data and workflows.
Agents in the real world.
Enabling agents to work in production across the enterprise is best achieved with a unified platform that handles development, iteration, and deployment. And that’s where capabilities like Agent Workbench on the OutSystems platform matter, said Rajkiran Vajreshwari, senior manager of application development at Thermo Fisher Scientific. Provides the infrastructure to learn, iterate, and govern agents at scale.
His team at Thermo Fisher has moved from single-task AI assistants in customer service to creating a coordinated team of specialized agents using the workbench. When a support case arrives, a triage assistant classifies the request and dynamically routes it to the appropriate specialized agent, whether it be an intent and priority agent, a product context agent, a troubleshooting agent, or a compliance agent.
"We don’t have to think about what will work and how. Everything is predesigned," he explained. "Each agent has a limited role and clear barriers. They remain accurate and auditable.”
Governing AI risks in the shadows
A new category of risk arises when AI makes it possible for anyone in a company to generate production-level code without IT oversight. Basically, it is an ungoverned shadow AI. These homegrown products are prone to hallucinations, data leaks, policy violations, model drift, and agents taking actions that were never formally approved.
To get ahead of risk, leading organizations must do three things, said Luis Blando, CPTO at OutSystems.
"Offer users security barriers. They are going to use AI whether you like it or not. The companies that seem to be moving forward are using AI to control it across their entire portfolio,” he explained. “That’s the difference between shadow AI chaos and enterprise-level scale.”
Eric Kavanagh, CEO of The Bloor Group, noted that governance requires a layered set of disciplines that include protecting data, monitoring models for drift, and making deliberate decisions about where AI connects to existing business processes.
“Companies do not have to create these controls manually," he added. "“Many of those guardrails and levers are built into platforms like OutSystems.”
Why the real challenge of orchestration is models versus platforms
Much of the initial excitement around enterprise AI focused on selecting the right large language model. Now the most difficult challenge and the most lasting source of value is orchestration. This includes routing tasks, coordinating workflows, governing execution, and integrating AI into existing business systems.
Scott Finkle, vice president of development at McConkey Auction Group, noted that LLMs, as impressive as they are, are parts of complex workflows, not end-all solutions. Organizations should be prepared to hot-swap between Gemini, ChatGPT, Claude, and whatever comes next without having to rebuild the agent system around them.
A platform with orchestration capabilities makes this possible. It manages the lifecycle, provides visibility, and ensures processes run reliably, even when AI handles the higher reasoning layer.
“AI and models change, workflows may change, but the orchestration remains the same." Finkle said. "“This is how we are going to extract value from AI.”
The economics of business investment in AI
Platform-level security, compliance, governance and AI capabilities will require greater investment in 2026, particularly as AI is incorporated into core workflows such as finance and supply chain. Companies should favor incremental profits rather than expecting large, immediate profits.
“We are focusing on the hits," Finkle said. "The way it counts is by putting something into production and having it have an impact. Large investments in pilot projects that do not come to fruition do not save money. “It won’t happen overnight, but I think over time we will see huge savings.”
There is still a divide in how companies approach AI transformation. Some start from scratch and reinvent every process. Others, especially those with billions of dollars of existing infrastructure that are depreciating internally, want AI to be integrated with their systems. They want agent systems to reuse data, APIs, and proven processes while speeding up delivery. The agent platform approach serves both fields, but particularly the latter. Organizations can deploy agents where they add clear value while preserving the integrity of established deterministic workflows.
The rise of the enterprise architect and generalist developer
As AI accelerates code generation, bottlenecks in software delivery are dissolving. Instead, systems thinking is prioritized. This is the ability to understand the broader enterprise architecture, decompose complex business problems, and reason about how AI integrates with existing infrastructure. Kavanagh specifically pointed to enterprise architects as the professionals best positioned to take advantage of this moment.
“We are entering a very interesting era of the generalist," he explained. "The better you know your enterprise architecture and business architecture and how those things align, the better off you’ll be. “
“The result is faster delivery with fewer interruptions and fewer errors." Kavanaugh said. "You can focus on non-repetitive tasks. It is a benefit for the developer, for the company and for the entire IT organization.”
Sponsored articles are content produced by a company that pays to publish or has a business relationship with VentureBeat, and are always clearly marked. For more information, contact sales@venturebeat.com.
