Building agents that reach production: why the platform matters

Reasoning models are advancing, but production is still hard

Reasoning models have progressed quickly. Systems can now parse documents, write code, and make judgment calls that were once firmly in the realm of research. Yet despite these advances, most AI initiatives still struggle to reach production environments where consistency, governance, and reliability matter. In fact, a recent MIT report found that only a small fraction of AI projects make it into day-to-day operations.

Appearing on the mainstage at FUSION, our flagship 2025 customer event, Jerry Liu, founder of LlamaIndex, summarized the challenge well:

“The biggest barrier to AI adoption is your own ability to contextualize and workflow-engineer these models.”

In other words, the barrier to AI execution isn’t the models. It’s the operational fabric surrounding them: orchestration, observability, governance, integration, and the ability to move from experimental insights to dependable execution.

For automation and operations leaders evaluating where to build agentic workflows, the key consideration is no longer which platform produces the most impressive demo, but which one consistently supports the transition from prototype to production.

Agentic workflows need more than AI alone

Teams that reliably operationalize AI understand that real-world agentic applications weave together several modes of execution: deterministic logic, human judgment, and targeted AI reasoning.

Take a typical travel-approval workflow. A request is submitted through a deterministic form. An agent extracts policy details from complex documentation using AI-powered reasoning. A manager reviews and approves. Finance handles a final check. Travel is then booked using deterministic rules.

While the AI component is clearly essential in this process, it is only one segment in a larger operational chain. Without orchestration, monitoring, and governance for the entire flow, even sophisticated reasoning models remain confined to demonstrations rather than production.

General-purpose development platforms often provide strong building blocks for the reasoning segments. But sustained operational success requires an environment designed to connect AI reasoning with the wider business process—securely, observably, and with clear ownership of every step.

A platform built for agentic workflows

The UiPath Platform™ approaches agentic automation from a deep background in enterprise process execution. That heritage shapes how teams build, orchestrate, and operate AI-driven workflows today, especially when moving beyond early experiments into environments that demand predictability and oversight.

Orchestration across the end-to-end workflow

Modern agentic systems combine model calls, deterministic logic, human approvals, and system integrations. A unified orchestration layer brings these elements into a single operational flow, enabling teams to see where a process stands, how decisions were made, and what actions remain.

Instead of coordinating separate tools for each stage, orchestration occurs in one place. This reduces operational overhead, clarifies ownership, and supports more consistent execution.

End-to-end observability

When workflows span multiple decision layers—AI reasoning, deterministic logic, human interaction, and system calls—observability becomes central to reliability. The platform provides detailed execution traces that combine reasoning logs with deterministic process logs, enabling teams to see how an agent arrived at a decision and how the process progressed from one stage to the next.

Prompts, tool usage, handoffs between humans and automation, integration calls, and business logic paths all appear in the same trace. This level of visibility helps teams diagnose issues, improve agent behavior, and maintain confidence in decisions executed at scale.

Governance and the AI Trust Layer

Agentic systems operating in production require consistent guardrails. The UiPath AI Trust Layer provides centralized oversight for generative AI interactions, masking personally identifiable information before it reaches a model, enforcing policy choices, auditing usage, and managing cost controls.

Teams can run platform-provided models or bring their own, whether hosted privately, cloud-managed, or fine-tuned for specific domains. All of these inherit the same governance and controls, ensuring operational consistency regardless of model choice.

Enterprise integrations that support operational scale

Most agentic workflows touch core business systems—enterprise resource planning (ERP), client relationship management (CRM), document repositories, customer service systems, data platforms, and more. The platform includes a broad library of enterprise-grade integrations developed across many large-scale deployments. This allows agents to pull data from operational systems or drive actions within them without teams having to build and maintain custom connectors.

Reasoning over unstructured data

Many automations begin with unstructured inputs: PDFs, reports, or mixed content. Through direct integration with data orchestration frameworks such as LlamaIndex, the platform enables agents to reason over large volumes of unstructured material. Document-processing capabilities convert complex inputs into structured formats suitable for model consumption, ensuring agents can work with real-world documents and not just neatly formatted samples.

Open and flexible model choices

Model performance evolves quickly. Teams often choose different models for different tasks—one for structured reasoning, another for long-context analysis, another for voice or multimodal interactions, and sometimes domain-specific models for regulated or sensitive work.

The platform is designed to support this flexibility. Agents can call multiple models within the same workflow, and teams can select the right model for each step without restructuring their process. This helps organizations adapt as the landscape changes, maintaining continuity even when new model options appear or performance shifts.

Deep ecosystem interoperability

To support this flexibility, the platform integrates deeply with leading AI model providers, cloud services, enterprise software systems, and open-source agent frameworks—without locking users into any particular vendor.

This includes bidirectional agent interactions with conversational platforms, orchestration connections to enterprise data clouds, support for open agent frameworks and evaluation tools, and compatibility with model gateways that allow teams to incorporate privately hosted or fine-tuned models. The platform’s open design enables organizations to evolve their model and tool choices over time while maintaining consistent governance and operational practices.

Tools to test, evaluate, and improve agents

Building agents is relatively straightforward. Deploying agents that operate reliably in production requires rigorous testing, evaluation, and refinement. The platform includes capabilities built specifically to support this operational lifecycle.

Teams can simulate agent behavior using synthetic data or mock tools, which is especially useful when real systems are not yet ready or when testing edge cases that could produce unwanted live transactions. These simulations appear clearly in run histories, making it easy to separate them from real execution data.

Evaluation sets allow teams to measure agent performance across different scenarios. Both deterministic and LLM-based evaluators are available, and teams can create custom evaluators aligned with their business context. Prebuilt evaluators assess output correctness, step-by-step trajectory coherence, and other factors that influence reliability.

An agent health score synthesizes prompt quality, tooling setup, schema design, and evaluation coverage to indicate readiness for production. Recommendations generated by the Agent Optimizer highlight where improvements will have the most impact, helping teams focus their refinement efforts effectively.

Deployment flexibility for real-world requirements

Organizations operate in a variety of environments. Some run fully in the cloud. Others have strict data residency requirements, regulated environments, or infrastructure that must remain air-gapped.

The platform supports all of these scenarios: cloud deployments, on-premises installations, Linux-based environments, bare-metal servers, and Kubernetes clusters including AKS, EKS, and OpenShift. In air-gapped environments, the full platform can run without internet access. Recent updates include enhanced IPv6 support, dual-stack networking, expanded disaster recovery options, and support for multiple instances within a single Kubernetes cluster.

This flexibility ensures that agentic workflows can adapt to the realities of each organization’s infrastructure and compliance needs.

Bridging low code and pro code for modern teams

Reasoning models are reshaping how automations are built. Increasingly, non-technical users can describe what they need in natural language, and systems can generate an initial workflow. This broadens who can participate in building automations while increasing the need for a platform that supports both rapid creation and rigorous operationalization.

The UiPath Platform™ has long supported both ends of this spectrum. Low-code tools provide speed and accessibility, while pro-code capabilities ensure that developers can implement complex logic, integrate deeply with systems, and manage the full lifecycle of production automations. AI-assisted building now accelerates the initial workflow creation, with developers refining and extending the automation as it moves toward production.

Because both approaches sit on the same foundation, organizations avoid the fragmentation that often arises between experimentation and operational deployment.

Getting started: from individuals to enterprise teams

Whether someone is learning, building for a small team, or guiding a large-scale rollout, the platform supports a consistent path from early experimentation to sustained production operations.

Individuals can start with the free Community Edition, which includes daily LLM usage and access to comprehensive learning resources through the UiPath Academy. Because learning occurs on the same platform used in enterprise settings, skills transfer directly to real projects.

Small teams can take advantage of template libraries and a large community of practitioners sharing best practices. As needs grow, the environment scales with them, avoiding the need for disruptive migrations later.

Enterprise teams running proof of concepts benefit from having governance and compliance baked in from the start. This makes it easier to show stakeholders exactly how an experimental agent can move into a governed, observable, production-grade workflow.

Organizations consolidating fragmented AI experiments often reach a point where orchestration, observability, and operational stability matter more than isolated demos. The platform brings these capabilities together, and teams can help organizations transition smoothly and design workflows built for production from day one.

Why skills built here matter

The UiPath Platform is used by thousands of organizations globally, including many of the world’s largest enterprises. As a result, professionals who learn to design and operate workflows here gain skills directly applicable to real-world environments.

For automation and operations leaders, this means that investing in platform expertise strengthens both organizational capability and talent readiness at the same time.

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