During the session, we will discuss specific examples of AI Agents across four key categories: 

• Case Management: Discover how AI Agents can support case management in business scenarios such as Service Requests, Incident Management, or Complaint Management, improving operational efficiency and the user experience. 

• Regulatory and Compliance: Explore how AI Agents can support regulatory and compliance processes by reviewing documents and policies, identifying risks, validating information, and generating findings to facilitate decision-making. 

• Unstructured Data Insights: Learn how AI Agents can extract, interpret, classify, validate, and summarize information from documents, emails, tables, and attachments, turning unstructured data into actionable information to improve decision-making and trigger the next steps within business processes. 

• Content Generation: Discover how AI Agents can use historical information within Bizagi to generate or suggest responses, helping your teams reduce manual work and maintain consistency in the information generated. 

Don’t miss this opportunity to learn how Bizagi AI Agents can be applied to real-world scenarios to transform the way organizations improve their business solutions. Join us to discover how these use cases can help accelerate AI adoption and move toward smarter, more practical, and value-driven automation. 

Register now! 

Vibe coding focuses on quickly generating pieces of software like screens, code, or workflows from prompts. 

Bizagi operates at the solution level. Instead of producing isolated components, it captures business intent and turns it into a governed, executable enterprise solution that runs directly on the Bizagi platform, enriched with built-in AI capabilities such as AI Agents, Ask Ada, AI Workers, and Enterprise Knowledge. 

A useful analogy is this: assembling a desk from a predesigned kit doesn’t make you a carpenter. You do end up with a desk, but not one designed for long-term durability. In the same way, generating components doesn’t create an enterprise solution. Bizagi is designed to deliver complete, resilient business solutions and not just individual parts. 

Bizagi scope 

Bizagi enables organizations to transform what the business wants to achieve into a working enterprise solution. Instead of asking users to design technical components or write code, Bizagi allows them to define business intent: how work should flow, how decisions are made, who participates, and how the system behaves. 

That intent is executed directly within the Bizagi platform, which combines process orchestration, Case and event-driven orchestration, embedded AI capabilities (such as AI Agents for autonomous or assisted task execution, Ask Ada for contextual, conversational interaction with processes and data, AI Workers for task automation at scale and Enterprise Knowledge to ground AI decisions in trusted business information). 

The outcome is not code, but a running enterprise solution with governance, traceability, and operational control built in by design.  

This approach differs from vibe coding tools, which generate components that must later be assembled, deployed, secured, and operated. Bizagi operates at the level of business solutions and orchestration, while vibe coding operates at the level of code artifacts and isolated experiences. 

Fundamental difference: code generation vs. solution execution 

The main difference between vibe coding and Bizagi is what is being created. 

Vibe coding focuses on generating code from prompts. AI produces interfaces, services, or workflows quickly, but these remain technical pieces. Business behavior emerges later from how those pieces are assembled and maintained. 

Bizagi focuses on executing business solutions, where processes, rules, data, and AI-driven decisions are explicitly defined and governed. 

With Bizagi, business behavior is not hidden in code: it is modeled, visible, and enforced at runtime. AI is embedded within this model, not layered on top of disconnected components. 

Deployment and operational responsibility 

In a vibe coding approach, generation is only the beginning. What follows is a series of responsibilities that fall entirely on the customer. Once components are created, teams must handle deployment, configure infrastructure and environments, secure the system, and ensure proper monitoring and observability. The initial speed of generation quickly gives way to the ongoing effort of making those components reliable, secure, and operational in real conditions. 

Bizagi takes a fundamentally different approach. Solutions are not generated as artifacts that need to be deployed; they are executed directly on an enterprise-ready platform. This means that organizations do not need to assemble or maintain the underlying infrastructure to run their solutions. 

This consistency also extends to AI capabilities. Features such as AI Agents and AI Workers are not external services that require separate deployment or lifecycle management. They operate within the same governed runtime as processes, rules, and data, ensuring that everything runs as part of a unified system rather than a collection of independent components. 

Security: configuration vs. platform capability 

In vibe coding, security is constructed. Teams must explicitly define authentication, authorization, secret management, and auditing mechanisms. Each decision introduces potential risk, and any oversight can create vulnerabilities that may only become visible in production. 

In Bizagi, security is not something that needs to be assembled. It is a native capability of the platform, built into how solutions are defined and executed. Access to data, roles, responsibilities, and actions are consistently enforced at runtime, without requiring teams to design security from scratch. 

This unified model also governs AI: AI Agents operate strictly within defined roles and permissions, ensuring they act only within authorized boundaries. Enterprise Knowledge guarantees that AI relies on controlled, approved data sources. Ask Ada interactions are equally governed, responding based on user access rights and the context of the process. 

The result is a secure model that is centralized, consistent, and aligned with the business, rather than dependent on fragmented technical implementations. 

Evolving the solution 

Enterprise systems are not static; they evolve continuously as business needs change. In Bizagi, this evolution is controlled and predictable. Processes, rules, and AI behaviors can be versioned in a way that preserves continuity. Running cases continue under their original definitions, while new cases adopt updated versions, ensuring that change does not disrupt ongoing operations. 

AI-driven behaviors remain tied to governed logic and approved knowledge sources, which means they evolve in alignment with the system rather than independently. 

In vibe coding, evolution is handled at a purely technical level. Changes require redeployment, may involve data migrations, and often introduce operational risk. The system itself does not inherently recognize the concept of a “running process” versus a “new one,” making it harder to implement change without unintended consequences. 

Final thoughts 

At its core, the distinction between vibe coding and Bizagi comes down to what is being delivered. Vibe coding produces components: pieces that can eventually be assembled into a solution if enough effort is invested in integrating and managing them. 

Bizagi delivers enterprise solutions by design. Process orchestration, data consistency, governance, and embedded AI capabilities are combined into a single, unified system. The outcome is not a set of applications, but a cohesive operational model that the business can rely on. 

Rather than viewing these approaches as mutually exclusive, organizations can combine them strategically. Bizagi can serve as the enterprise orchestration backbone, providing governance, consistency, and integrated AI capabilities, while vibe coding can be used to create highly customized user interfaces when needed. 

This approach is particularly valuable when there is a strong requirement for complete control over the user experience. In those cases, vibe coding brings flexibility and speed, while Bizagi ensures correctness, traceability, and operational stability. 

For details on creating and using MCP, see: MCP Servers. 

What Is an MCP Server 

An MCP Server exposes services called tools. Each tool performs a specific action, such as calculating a weighted average, generating a chart, or scheduling a meeting in Google Calendar. What makes MCP unique is that these tools are described in natural language, so the AI Agent can understand what each tool does and decide which one to use based on the prompt. 

This approach is similar in spirit to APIs, but MCP is designed specifically for AI. It allows the agent to read the descriptions of available tools, understand the input parameters, and execute the right action without complex coding. 

Why MCP Servers Matter 

By connecting to MCP Servers, Bizagi AI Agents can extend their capabilities beyond Bizagi’s data. For example, an agent can analyze supplier evaluations stored in Bizagi, then use an MCP tool to generate a radar chart for visual comparison. Or it can schedule a meeting in Google Calendar after selecting the top suppliers for an RFP. These integrations make AI-driven processes more dynamic and practical. 

Examples of MCP in Action 

Imagine you have an agent that evaluates suppliers for a procurement process. After analyzing historical data using Bizagi Data Knowledge, the agent needs to present the results visually. By connecting to an MCP Server that offers a “Generate Radar Chart” tool, the agent can create a chart and return the URL to display it in the process form. 

Another example is scheduling. If your agent selects the top three suppliers for an RFP, it can use an MCP tool to create a meeting in Google Calendar automatically, saving time and reducing manual work. 

Best Practices 

When using MCP Servers, always review the tool descriptions and input parameters to ensure they match your process needs. Test the connection in Bizagi Studio before deploying, and use clear prompts so the agent understands what action to perform. If you want to see how the agent interacts with MCP tools, use the Test Prompt option in Studio to validate the behavior before going live. 

Final thoughts 

MCP Servers open a new world of possibilities for Bizagi AI Agents. They allow your agents to interact with external systems, perform calculations, generate charts, and even schedule meetings—all through natural language instructions. By configuring MCP Servers and testing them in Bizagi Studio, you can make your AI-powered processes smarter, faster, and more connected than ever.

For many Bizagi customers, the starting point of their AI adoption journey is still unclear: where can we begin to generate real, measurable value with Bizagi?

This article is the first in a new series titled “Where to start your AI Adoption journey with Bizagi?”. Throughout this series, we will explore how Bizagi’s AI capabilities—Ask Ada, AI Workers, and AI Agents—can be adopted progressively, responsibly, and with clear business outcomes.

We begin with Ask Ada, Bizagi’s conversational AI experience, and the fastest way to unlock the value of your business data.

Ask Ada: An Early Win on Your AI Journey

Ask Ada is designed to give business users instant access to insights hidden in their processes and data—without technical barriers, custom reports, or constant dependency on IT. That is why Ask Ada represents a natural first step in Bizagi’s AI adoption journey.

Ask Ada enables organizations to build confidence with AI through everyday questions, real operational answers, and visible ROI. Most importantly, it allows you to start small while involving the right people and processes from day one.

A successful Ask Ada rollout typically evolves in three waves.

First Wave: Prove Value Quickly with the Right Process

The goal of the first wave is simple: deliver a visible and credible win.

Start with:

• One business process that already has clean, well‑governed data.

• Minimum viable Data Domains, clearly defined and easy to understand.

• A focused group of users with frequent information needs.

The ideal roles to enable early include:

• Operations Analysts
  These users constantly query process data. Ask Ada can replace dozens of weekly IT requests by allowing them to ask questions directly and get immediate answers. This group often shows the fastest ROI.

• Department Managers
  Managers need real‑time visibility into process status without waiting for scheduled reports. With Ask Ada, they gain autonomy to check performance, bottlenecks, and workload on demand—before critical meetings or decisions.

This first wave is about speed, clarity, and confidence. One well‑chosen process can do more for adoption than an ambitious, multi‑process rollout that no one actively uses.

Second Wave: Expand Reach and Context

Once Ask Ada has proven its value, the second wave focuses on scaling impact.

At this stage, organizations typically:

• Expand Ask Ada to three to five additional processes.

• Activate the Knowledge Base, integrating internal documents such as policies, procedures, and guidelines.

• Refine Data Domains based on real user feedback.

New roles become especially relevant:

• Compliance and Risk Teams
  These teams need fast access to regulatory information, internal policies, and historical cases. By combining process data with curated Knowledge Base content, Ask Ada becomes a trusted assistant for informed and compliant decision‑making.

• Customer Service and CX Teams
  Customer‑facing teams frequently search for case status and customer history. Ask Ada reduces time spent navigating systems, accelerates responses, and minimizes escalations—all while keeping answers consistent and accurate.

This wave transforms Ask Ada from a reporting alternative into a daily operational assistant across multiple teams.

Third Wave: Executive Decisions Without Intermediaries

In the third wave, Ask Ada becomes part of strategic and executive workflows.

Key activities include:

• Integrating Ask Ada into executive decision flows.

• Continuously refining Data Domains together with business users.

• Elevating Ask Ada from insight discovery to decision enablement.

The primary audience here is the C‑suite and senior leadership. Executives no longer need technical training or complex dashboards. Instead, they leverage Ask Ada‑driven queries to fuel executive dashboards and decision moments—with clarity, speed, and confidence.

The value is simple but powerful: visibility without intermediaries, especially when decisions matter most.

Three Non‑Negotiable Principles for Success

Across all three waves, organizations that succeed with Ask Ada consistently apply three principles:

1. Start with a “photogenic” process
   The pilot does not have to be the most critical process—it must be the one with clean data, a clear domain, and users who will immediately see value. A fast, visible win beats a perfect implementation that no one adopts.

2. Champions are your most important asset
   Champions are not random early adopters. They are credible voices within their teams, capable of communicating value and influencing decision‑makers. Investing in their time, recognition, and feedback is what turns a successful pilot into organic expansion.

3. Data governance is a prerequisite, not a consequence
   Ask Ada is only as strong as the Data Domains that power it. Inconsistent data or poorly defined access will create distrust from day one. That is why data quality should be audited before enabling Ask Ada—not after.

Measuring ROI from Day One

One of the advantages of starting your AI journey with Ask Ada is that ROI can be measured early and clearly, and organizations can track tangible impact across three key dimensions:

Recovered Time — Business Users

Ask Ada gives business users direct access to answers that previously required manual analysis, report requests, or system navigation. Operations analysts, managers, and customer‑facing teams recover hours every week by asking questions in natural language and getting immediate, trusted responses.

IT Capacity Released

By reducing ad‑hoc reporting requests and data queries, Ask Ada frees IT teams from repetitive tasks. This released capacity can be reinvested in higher‑value initiatives such as platform optimization, governance, and advanced automation—without increasing headcount.

Faster, Better Decisions

Ask Ada accelerates decision‑making by removing intermediaries between data and decision‑makers. Whether it’s a manager preparing for a meeting or an executive evaluating a critical situation, real‑time insights lead to quicker, more confident decisions with measurable business impact.

Together, these three metrics form a practical and defensible ROI model that resonates with both operational teams and leadership.

Start Smart. Start with Ask Ada.

Your AI adoption journey does not need to start with disruption or complexity. With Ask Ada, you can begin by empowering users, building trust in AI, and maximizing the value of the data you already have.

In the next articles of this series, we will explore how AI Workers and AI Agents extend this foundation into execution and autonomy.

In Bizagi, some AI capabilities operate asynchronously or perform complex processing behind the scenes. Understanding this behavior helps set expectations and avoids misinterpreting normal AI processing time as a performance problem.

AI is working, not waiting

AI systems analyze context, process information, apply reasoning to generate structured outputs. Depending on the task, this may involve multiple steps such as reading data, classifying information, generating embeddings, or producing structured responses. All of this takes time.

Two common examples where this is especially visible are Enterprise Knowledge vectorization and AI Workers execution.

Example 1: Enterprise Knowledge vectorization

When you create, update, and especially deploy Enterprise Knowledge, the knowledge is not available immediately. Before it can be used by AI Agents or AI Workers, the content must go through a vectorization phase.

During this phase, the AI reads and analyzes the source content, classifies and segments the information and generates embeddings for retrieval and reasoning.

This process runs asynchronously and may take time depending on factors such as data volume, number of segments defined in Studio, and system load. While this happens, the Knowledge may appear as “in progress” or “not available”. This is expected. The AI is actively working to prepare the information so it can be used reliably and accurately once processing is complete.

Example 2: AI Workers execution time

AI Workers may take additional time to complete their execution, especially when they are not managed by the Scheduler. In these cases, the AI Worker runs directly as part of the user’s interaction with the task.

When this happens, the AI Worker is analyzing the form and field descriptions, building context from available data and generating structured outputs.

Forms with many fields, rich descriptions or large contextual data naturally require more processing. During this time, the AI Worker is not idle, it is actively generating accurate information before populating the form fields. When opening a form with an AI Worker not managed by the scheduler, that takes two to three minutes to load, is not a performance issue: it is normal AI behavior.

Final thoughts

In both scenarios, the key point is the same: this is not a performance degradation or system issue. It is the normal time required for AI to analyze information and produce quality results.

By understanding that AI needs processing time, especially for complex or data-rich scenarios, you can better interpret system behavior and trust that the AI is doing exactly what it’s designed to do: working intelligently, not instantly.

During this session, we will cover the following topics: 

• AI Usage Monitoring – Agents, Workers, and Ask Ada: Learn how to track and analyze how AI capabilities are being used across the organization, gaining insights into adoption, interaction patterns, and business impact. 

• AI Accuracy & User Feedback: Discover how feedback signals and accuracy metrics help assess AI Worker readiness, identifying which AI Workers are prepared for greater autonomy and which require further refinement. 

• AI Performance Management – SLAs, Retries, and Timings: Understand how to monitor AI performance through service-level agreements, execution timings, and retry mechanisms to ensure consistent, reliable, and resilient AI-driven process execution. 

Don’t miss this opportunity to learn how Bizagi’s AI monitoring capabilities help organizations responsibly scale AI, improve trust and transparency, and maximize performance across AI-powered processes. Join us to see how these capabilities support the next stage of AI-driven automation—moving from experimentation to operational excellence. 

Register now!