Presented by OutSystems
After two years of flashy AI demos, rushed agent prototypes, and breathless predictions, enterprise technology leaders are striking a more pragmatic tone in 2026. At a recent webinar hosted by OutSystems, a panel of program managers and enterprise practitioners said the most impactful work is focusing on what’s happening right now, the practical work of artificial intelligence. and iteration, while integrating agents into systems they’ve built over decades.
Business leaders increasingly focus on the fundamentals. The priority is to use new AI technologies
accelerate productivity, improve delivery and achieve measurable business results.
Three elements form this work:
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Moving from AI agent prototypes to agent systems that deliver measurable ROI in production
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The growing role of enterprise platforms in securely managing, orchestrating and scaling AI agents
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The rise of the general developer and enterprise architect as the most valuable technical profiles in the era 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. It was ultimately about 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 enterprise is best done with a single platform that manages development, replication and deployment. That’s where capabilities like the Agent Workbench on the OutSystems platform are important, says Rajkiran Vajreshwari, senior manager of application development at Thermo Fisher Scientific. It provides the infrastructure to learn, iterate, and manage agents at scale.
His team at Thermo Fisher has moved away from single-task AI assistants in customer service to a team of coordinated specialized agents using the workbench. When a support case arrives, the triage assistant classifies the request and dynamically routes it to the correct specialist agent, whether it’s a goal and priority agent, a product context agent, a troubleshooting agent, or a compliance agent.
"You don’t have to think about what and how it will work. All pre-set," he explained. "Each agent has a narrow role and clear safeguards. They are accurate and verifiable.”
Managing Shadow AI Risks
A new category of risk arises when AI makes it possible for anyone in a company to create production-level code without IT oversight. Basically, it’s an uncontrollable shadow AI. These homegrown products are prone to hallucinations, data leaks, policy violations, model drift, and agents who commit acts that are never officially approved.
OutSystems CPTO Luis Blando said there are three things leading organizations must do to stay ahead of risk.
"Give users safety bars. Whether you like it or not, they will use artificial intelligence. “Companies that seem to be moving forward are using AI to drive AI across their entire portfolio,” he explained. “That’s the difference between shadow AI chaos and enterprise scale.”
Eric Kavanagh, CEO of The Bloor Group, noted that management requires a layered set of disciplines, including protecting data, monitoring models for drift, and making thoughtful choices about where to integrate AI into existing business processes.
“Companies don’t have to create these controls manually," he added. "Many of these guards and arms are made on platforms like OutSystems.
Why is the real orchestration problem models and platforms?
Much of the early excitement about enterprise AI focused on choosing the right broad language model. Now the harder challenge and the more enduring source of value is orchestration. This includes routing tasks, coordinating workflows, managing execution, and integrating AI into existing enterprise systems.
Scott Finkle, vice president of development at McConkey Auction Group, noted that LLMs, as impressive as they are, are pieces of a complex workflow, not end-to-end solutions. Organizations must be prepared to exchange hotly between Gemini, ChatGPT, Claude, and whatever comes next without rebuilding the agent system around it.
A platform with orchestration capabilities makes this possible. It manages the lifecycle, provides visibility, and even ensures that processes run reliably while AI manages the reasoning layer above.
“AI and models change, workflows may change, but the orchestration remains the same," Finkle said. "In this way, we will extract value from artificial intelligence.”
The economics of enterprise AI investment
Security, compliance, governance and platform-level AI capabilities will all require greater investment in 2026, especially as AI moves into mainstream workflows such as finance and supply chain. Businesses should prioritize incremental wins rather than expecting large, immediate gains.
“We focus on major hits," Finkle said. "The main way to do this is to put something into production and influence it. Large investments in pilot projects that do not translate into production do not save any money. It won’t happen overnight, but over time we will see huge savings.”
There is still disagreement about how businesses should approach AI change. Some start from scratch and reimagine every process. Others, especially those with billions of dollars of internally outdated existing infrastructure, want AI integrated into their systems. They want agent systems to reuse data, APIs, and proven processes while accelerating delivery. The agent platform approach caters to both camps, especially the latter. Organizations can deploy agents where they add clear value while maintaining the integrity of defined, deterministic workflows.
The rise of the enterprise architect and general developer
As AI accelerates code generation, bottlenecks in software delivery are eliminated. In its place is an award for systems thinking. It’s the ability to understand the broader enterprise architecture, break down complex business problems, and reason about how AI can integrate with existing infrastructure. Kavanagh specifically singled out enterprise architects as the professionals best positioned to take advantage of this moment.
“We are entering the very interesting age of the generalist," he explained. "The better you know your enterprise architecture and your business architecture and how they fit together, the better off you will be. “
“The result is faster delivery with fewer interruptions and fewer errors," 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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