It supports YAML based automations, real-time event handling, and deep integrations with apps, APIs, and MCP tools. The key differentiator of AI Builder is its ability to integrate legacy systems and custom connectors using YAML—up to 10× faster than traditional development—into the AI Marketplace of Apps, enabling teams to rapidly bootstrap agents and unlock high-value automation across the enterprise.
Business Context
For large enterprises, scaling Generative AI means more than deploying chatbots — it requires orchestrating multiple agents, tools, and processes across secure environments. AI Builder enables this orchestration by:- Bridging AI agents with corporate data and APIs.
- Automating repetitive tasks through low-code workflows.
- Coordinating complex, multi-agent interactions securely.
- Maintaining compliance and observability throughout automation pipelines.
Key Capabilities
Design, automate, and monitor AI-driven workflows using YAML.
Trigger events between agents, tools, or applications securely within the same workspace.
Trigger events between agents, tools, or applications securely within the same workspace.
Integrate and orchestrate Apps (like Jira, CRM, Crawler, or Agents) to connect your AI agents to real enterprise systems.
Build multi-agent scenarios where multiple agents collaborate or delegate tasks to one another in a governed environment.
Leverage webhooks, APIs, and event listeners to synchronize AI activity with internal systems such as CRM, ITSM, or ERP.
Learning Journey
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Video 1 — Website-to-Agent Automation Template
Objective of the use case:
Learn how to use the Website-to-Agent template to automatically crawl a website and inject its content into an AI Knowledge base.What you’ll see:
- Duplicating a ready-to-use AI Builder workspace.
- Connecting your AI Knowledge agent using its Agent ID and API key.
- Configuring the Knowledge Client app and the Crawler app.
- Setting a target website URL and launching a crawl operation.
- Monitoring the crawling status dashboard and viewing extracted page content.
- Observing pages being converted into documents in AI Knowledge.
- Verifying source integrity and testing retrieval in chat.
- Reviewing automation YAML logic (event listeners and functions).
- Understanding how the builder keeps documents up to date automatically.
The Website-to-Agent template demonstrates how AI Builder automates the ingestion of external data into a governed AI Knowledge workspace — ensuring continuous updates and consistency.
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Video 2 — Multi-Agent Jira Integration Template
Objective of the use case:
Learn how to use the Multi-Agent Jira template to connect Prisme.ai agents with Jira for ticket automation and project synchronization.What you’ll see:
- Configuring the Jira API App with OAuth credentials, project key, and site ID.
- Running the automation
init_agentto create a new agent dedicated to Jira operations. - Using AI Knowledge to store Jira issue types automatically.
- Inspecting the automation logic that triggers document creation for each issue type.
- Creating custom tools to interact directly with Jira APIs.
- Updating automations and verifying synchronization in the activity feed.
- Chatting with the AI to create Jira tickets automatically.
- Verifying ticket creation in Jira and viewing the automation logs.
- Understanding event chaining between “init agent” → “update knowledge base” → “execute Jira API”.
- Cleaning or reinitializing knowledge bases with utility automations.
This use case illustrates how AI Builder connects enterprise systems like Jira with generative agents, enabling multi-agent automation and secure operational AI at scale.
Practical Applications
| Department | Use Case | Description |
|---|---|---|
| IT / Operations | Data Pipeline Automation | Automate ingestion and updates from web or internal sources into AI agents. |
| Customer Support | Ticket Management | Integrate AI assistants with Jira or ServiceNow for end-to-end case handling. |
| Marketing | Content Aggregation | Crawl and consolidate website content to feed AI-powered assistants. |
| Engineering | Knowledge Synchronization | Keep internal product documentation updated automatically. |
| Governance & Risk | Workflow Traceability | Monitor agent activity, automation triggers, and data movement for audits. |
Key Takeaways
- Centralized orchestration layer for connecting agents, data, and applications.
- Low-code YAML automations to create event-driven AI workflows.
- Multi-agent and multi-app integration for complex enterprise scenarios.
- Compliant, traceable automation aligned with corporate governance.
- Extensible foundation for building advanced AI pipelines and real-time processes.