Why Visual Agent Design is an essential element of Enterprise GenAI?
It is no coincidence that OpenAI and Google have launched visual AI agent builders. They are moving into a space where simple conversation with an AI assistant is no longer enough. Companies need AI agents that operate within controlled procedures, execute tasks according to established processes, and deliver measurable business value.
TL;DR
The end of the era of simple chats – it’s time for controlled AI processes.
The market is shifting from “Q&A” style assistants to workflow-based AI agents that guarantee predictability, determinism, and real ROI. While Google and OpenAI offer visual builders in the public cloud, Extentum AI delivers the same technology through a secure on-premises model. This is a critical solution for regulated sectors, enabling the deployment of complex AI processes up to 30x faster and cheaper than traditional development methods, all while maintaining full data sovereignty.
What Enterprise AI deployment challenges are OpenAI and Google addressing?
OpenAI, with AgentKit, and Google, via Vertex AI Agent Builder, have introduced visual tools for designing AI agents to the market. This is not just a UI change; it is a response to real-world problems that enterprise companies face when deploying GenAI:
- Low value of AI assistant outputs: Simple AI assistants based on a single prompt and attached files often perform well in Q&A but fail when faced with complex logic and multi-stage decision-making.
- Lack of unification and repeatability: General-purpose AI agents lack consistency, leading to low business value. While autonomous “agentic AI” can produce spectacular results, every run might follow a different path—which is unacceptable for processes requiring an audit trail.
- High cost of building complex GenAI solutions: Developing custom AI agents that meet high business standards (quality, determinism, repeatability) using traditional dev teams and AI specialists often takes months, carries high risk, and involves budgets reaching into the hundreds of thousands.
Organizations that started with simple AI assistants (operating in a “question-answer” model without defined process logic) quickly realize that this is insufficient for complex processes, business accountability, and AI security. This creates a need to move from “chatting” with a model to designing controlled AI agents embedded in the broader context of company processes and data.
Three categories of solutions: where is the real value?
Currently, the market offers three categories of AI solutions, but only the third provides real value in terms of executing business processes:
- AI Assistants: Typically based on a single complex prompt and attached knowledge files. They load a document and provide chat-style answers. Easy to create, but they fail at complex procedures—results are unstable and non-repeatable.
- Autonomous AI Agents: Operate independently (e.g., AI Agent mode in ChatGPT). They plan steps, choose tools, and perform actions (sending emails, modifying entries, updating tasks) on their own. They offer great flexibility, but for Enterprise AI, the lack of determinism and the risk of hallucinations limit their use in regulated areas.
- Workflow-based AI Agents: These fill the gap between the two above. They operate according to predefined Standard Operating Procedures (SOPs) with a pre-designed task flow graph and a dose of autonomy (intelligence) within set boundaries. Unlike the other two, they are part of a broader process, ensuring high determinism, repeatability, predictability, and high business value.
Why are OpenAI and Google betting on Visual Builders?
Until now, the highly sought-after Workflow-based AI Agents required teams of developers, engineers, and often AI scientists. This meant massive budgets and long implementation cycles, preventing agile experimentation. The failure rate for such deployments climbed as high as 80-90%.
Visual AI agent and LLM process builders are the answer to this problem.
Common features of these approaches:
- Visual Workflow / Flow Canvas: Building an agent using blocks and arrows instead of just code.
- Data and Tool Integration: Connecting to external data sources (e.g., Gmail, Drive, Jira for Google; various tools for OpenAI) and the ability to plug in internal systems as “tools.”
- Multi-stage Agent Creation: Instead of one prompt, a process consists of multiple steps, conditions, and sub-agents that can be designed and tested iteratively.
- Human-in-the-loop: Visual processes allow for easy integration of humans as decision-makers or validators at key steps.
What role does Extentum AI play in this new landscape?
First and foremost, Google and OpenAI are public clouds. For companies in regulated sectors (banks, investment funds, insurance, energy, telecommunications, healthcare, pharma, airlines, etc.), using them is problematic due to restrictions regarding data security, data residency, and ICT risk control.
Extentum AI, on the other hand, is fully sovereign, Cloud Agnostic, and offers a full on-premises solution. The platform is installed locally; therefore, data is processed on the client’s private infrastructure and is not shared with any third parties. This eliminates the legal and operational risks of cloud processing and ensures full compliance with GDPR, trade secrets, and industry-specific regulations.
| Security Aspect | Extentum AI | OpenAI AgentKit | Google Vertex AI Agent Builder |
| Deployment | On-premises / Full local, Cloud-agnostic | OpenAI Public Cloud | Google Public Cloud (Gemini Enterprise) |
| Data Control | Full sovereignty; no data sharing | Data processed in OpenAI cloud | Data in Google cloud |
| Compliance | GDPR, trade secrets, regulated sectors | Restricted in regulated sectors | Restricted in regulated sectors |
| Legal/Op Risk | Minimal – data stays in-house | High in regulated areas | High in regulated areas |
Furthermore, while OpenAI AgentKit or Vertex AI Agent Builder require developer skills, Extentum AI offers visual process orchestration accessible to business teams. The Visual Agent Builder allows business users to quickly validate hypotheses in a secure Sandbox environment. This is a massive improvement, considering that the same implementation by a dev team could take months.
In short: using Extentum, one person can achieve in a few hours what previously cost hundreds of thousands. By breaking logic into stages, you achieve a higher level of determinism—even on open-source models—within your own on-premises or private cloud environment.
Four practical benefits of Agent Builder in Extentum.AI
- Security-First Architecture: Full data sovereignty and regulatory compliance via deployment inside isolated client infrastructure.
- Deterministic, Auditable AI Processes: Consistency and auditability through SOP-driven workflows that guarantee results even in complex processes.
- Radical Reduction in Time and Cost: Agent preparation is 10-30x faster than typical development work.
- AI Democratization: Business teams can build complex AI agents and intelligent processes without burdening IT departments.
What does this mean for Enterprise GenAI Adoption?
Visual AI agent builders—both cloud-based and those developed by Extentum—lower the barrier to entry, moving AI transformation from “single chat experiments” to “scalable, repeatable business processes.”
For large organizations, this means they can:
- Stop relying solely on simple AI assistants;
- Build their own deterministic AI agents that comply with processes and regulations;
- Move faster and more safely from POC to production with a real impact on ROI and risk;
- (For regulated sectors) Gain independence from the public cloud and deploy GenAI agents in a fully controlled environment.
Want to see how workflow-based agents can automate your company’s processes?
