Design, Connect, and Orchestrate Governed Enterprise AI Agents
Protrak AI Agent Studio brings business data, enterprise knowledge, AI models, and approved actions together to create complete agentic business experiences.
Build agents that understand enterprise context and turn intelligence into business action.
AI Agent Studio capabilities
Orchestrate Work. Connect Tools. Ground Answers.
Document Intelligence
Convert engineering PDF drawings into structured Protrak records with PDF Drawing Extractor.
Agentic Workflow Designer
Compose multi-step agent workflows on a visual drag-and-drop canvas.
MCP Server
Connect Protrak AI to external MCP tools, or make governed Protrak tools available to external AI clients.
Knowledge Base Search
Ground agents in governed enterprise knowledge from documents, business content, and Knowledge Graph retrieval.
Core components
The Building Blocks of an Enterprise Agent
Agents
Conversational or background AI units operating within user or system context.
Workflows
Visual orchestration of user, LLM, retrieval, tool, router, render, and agent tasks.
Agent Skills
Reusable business instructions, context, and process knowledge for repeatable tasks.
Agentic Tools
Schema-bound, deterministic capabilities for Protrak, API, database, utility, and external service actions.
MCP Servers
Connections that expose MCP-compatible external capabilities as discoverable tools.
Knowledge Base
Governed enterprise knowledge grounded in documents, business content, and Knowledge Graph retrieval.
Models
Configurable AI models for reasoning, embedding, vision, transcription, and generation.
Document Intelligence
Convert engineering PDF drawings into structured Protrak records with PDF Drawing Extractor.
- Extract drawing metadata, dimensions, tolerances, and engineering characteristics, then map them to Protrak attributes.
- Review structured data and mapping validation while retaining the connection to each source drawing region.
Agentic Workflow Designer
Compose multi-step agent workflows on a visual drag-and-drop canvas.
- Combine user interaction, LLM reasoning, retrieval, tool execution, conditional routing, rendering, and agent invocation.
- Use reusable templates as a starting point for repeatable agent designs.
MCP Server
Connect Protrak AI to external MCP tools, or make governed Protrak tools available to external AI clients.
Explore MCP Server- Protrak AI → External MCP Servers: discover external tools and combine them with Protrak actions and Agent Skills.
- External AI → Protrak MCP Server: let MCP-compatible clients retrieve Protrak data and perform permitted actions through intentionally exposed tools.
Knowledge Base Search
Ground agents in governed enterprise knowledge from documents, business content, and Knowledge Graph retrieval.
- Support document-driven knowledge assistance and contextual enterprise search.
- Control access to knowledge as part of the agent's governed execution context.
LLM-Provider Agnostic by Design
A unified model abstraction lets organizations configure supported services such as OpenAI, Azure OpenAI, Anthropic Claude, or supported locally hosted models according to architecture, security, and workload requirements.
Multi-tool agent architecture
Combine built-in Protrak tools, reusable libraries, external services, MCP tools, utilities, and custom tools. Agents bring AI reasoning together with deterministic actions.
Expose the Context Each Task Needs
Establish a host connection and selectively expose relevant metadata. Include or exclude specific fields and define alternative business names so agents understand organizational terminology.
Protect sensitive information
Exclude or mask sensitive and unnecessary fields before sending information to the model, reducing unnecessary exposure while preserving the context required for the request.
Secure, Multi-Tenant by Design
Role-aware execution context
Tenant-isolated runtime storage
Permission-aware business actions
Controlled access to tools and knowledge
System and application guardrails
Content safety and data-protection controls
From Prompt to Governed Business Outcome
A typical agent experience can follow this flow. The exact combination depends on the agent and its task.
- 1User Request
- 2Business Context
- 3Knowledge
- 4AI Reasoning
- 5Skill
- 6Tool Execution
- 7Validation
- 8Business Action
- 9Rendered Result
A knowledge assistant may use retrieval and an LLM; a business agent may combine knowledge, skills, and Protrak tools; a cross-system process may add MCP tools; and complex scenarios may coordinate multiple agents.
Build AI into the Application Experience
Connect background agents to widgets and application actions through APIs. A user supplies a requirement, the agent produces configured structured output, and the result returns through the application experience.
Enterprise localization
Support multilingual users and content so common agent workflows and knowledge resources can serve teams across languages and regions.
Enterprise Experiences Powered by Agents
- Contextual enterprise search
- AI-powered analytics
- Guided business transactions
- Configuration assistance
- Customer and service support
- Quality and compliance reviews
- Document-driven knowledge assistance
- Automated operational processing
- Decision support
- Cross-system task execution
- Embedded AI application features
Turn Enterprise Intelligence into Governed Action
Bring agents, workflows, knowledge, skills, tools, MCP connections, and models together—with governance defining what each agent may access and execute.