Presented by OutSystems
After two years of flashy AI demos, hasty agent prototypes, and breathless predictions, enterprise technology leaders are adopting a more pragmatic tone in 2026. In a recent webinar hosted by OutSystems, a panel of software executives and enterprise practitioners made the case that the most consequential AI work happening now is focused on practical matters of governance, orchestration, and iteration, as well as integrating agents into the systems they have spent decades building.
Enterprise leaders are increasingly focused on the fundamentals. The priority is to use new AI technologies
To accelerate productivity, improve delivery and generate measurable business results.
Three elements shape this work:
- Move from AI agent prototypes to agentic systems that deliver measurable ROI in production
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The growing role of enterprise platforms in securely operating, orchestrating, and scaling AI agents
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The rise of the generalist developer and enterprise architect as the most valuable technical profiles in the age of AI-generated code
Against this backdrop, the panel discussed governance frameworks, the economics of enterprise AI, and the limitations of large language models without orchestration. The conversation ultimately focused on how leading organizations are building multi-agent systems based on existing enterprise data and workflows.
agents in the real world
Enabling agents to work in production across the entire enterprise is best accomplished with a unified platform that handles development, iteration, and deployment. And that’s where capabilities like Agent Workbench in the OutSystems platform matter, said Rajkiran Vajreshwari, senior manager of app development at Thermo Fisher Scientific. It provides the infrastructure for agents to learn, replicate, and govern at scale.
His team at Thermo Fisher has moved from single-task AI assistants in customer service to building a coordinated team of specialized agents using Workspace. When a support case comes in, a triage assistant categorizes the request and dynamically routes it to the right expert agent, whether it’s an intent and prioritization agent, a product reference agent, a troubleshooting agent, or a compliance agent.
"We don’t need to think about what will work and how. It’s all pre-made," he explained. "Each agent has a narrow role and clear guardrails. They remain accurate and audible.”
Controlling the risks of shadow AI
A new category of risk emerges when AI makes it possible for anyone in a company to generate production-level code without IT oversight. Basically, it’s uncontrolled shadow AI. These homemade products are prone to hallucinations, data leaks, policy violations, model drift, and agents taking actions that were never formally approved.
Luis Blando, CPTO at OutSystems, said there are three things leading organizations need to do to get ahead of risk.
"Provide users with guardrails. Whether you like it or not, they are going to use AI. The companies that seem to be moving forward are using AI to take control across their entire portfolio,” he explained. “This shadow AI is the difference between chaos and enterprise-grade scale.”
Eric Kavanagh, CEO of The Bloor Group, said governance requires a layered set of topics that include securing data, monitoring models for drift, and making deliberate choices about where AI connects to existing business processes.
“Companies don’t need to create these controls manually," He added. "A lot of these handrails and levers are baked into platforms like OutSystems.
Why is the real orchestration challenge model vs platform?
Much of the early excitement around enterprise AI focused on selecting the right large language model. Now the toughest challenge and far more durable source of value is orchestration. This includes routing tasks, coordinating workflows, controlling execution, and integrating AI into existing enterprise systems.
Scott Finkel, vice president of development at McConkey Auction Group, said that LLMs, although impressive, are pieces of a complex workflow, not the ultimate solution. Organizations must be prepared to hot-swap between Gemini, ChatGPT, the cloud, and whatever emerges next, without having to rebuild agentive systems around it.
A platform with orchestration capabilities makes this possible. It manages the lifecycle, provides visibility, and ensures processes execute reliably, even as AI handles the logic layer on top.
“AI and models change, workflows may change, but orchestration remains the same," Finkel said. "That’s how we’re going to extract value from AI.”
Economics of Enterprise AI Investment
Security, compliance, governance and platform-level AI capabilities will all command greater investment in 2026, especially as AI moves into core workflows such as finance and supply chain. Enterprises should favor incremental wins rather than expecting big, immediate gains.
“We’re focusing on base hits," Finkel said. "The way it matters is by getting something into production and having an impact. Large investments in pilot projects that don’t make it into production don’t save any money. “It’s not going to happen overnight, but I think over time we’ll see tremendous savings.”
There is still a divide in how enterprises are approaching AI transformation. Some people start from scratch and reimagine every process. Others, especially those who have billions of dollars of domestic depreciation in existing infrastructure, want AI integrated with their systems. They want agentic systems to reuse data, APIs, and proven processes while accelerating delivery. The agent platform approach serves both camps, but especially the latter. Organizations can deploy such 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 creation, barriers to software delivery are falling away. Instead there is a premium on systems thinking. It is the ability to understand broader enterprise architecture, decompose complex business problems, and reason about how AI integrates with existing infrastructure. Cavanagh specifically pointed to enterprise architects as the professionals who are best positioned to capitalize on this moment.
“We are entering a very interesting era of the generalist," he explained. "The better you know your enterprise architecture and your business architecture and how these things align, the better off you will be. ”
“The result is faster delivery with fewer interruptions and fewer bugs," Kavanaugh said. "You can focus on non-repetitive tasks. This is a benefit for the developer, the business, and the entire IT organization.”
Watch the entire webinar here.
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