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What is a Project?

A Project is a complete, deployable AI system in Triform. It’s a repository that holds all the components (Agents, Flows, Actions) needed to accomplish a specific goal.

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Project structure

Projects contain: Actions — Python functions for deterministic logic
Agents — LLM-powered components with tools
Flows — Orchestration graphs connecting components
Sub-Projects — Nested Projects for modularity
Variables — Configuration accessible to all components
Triggers — What starts executions Example structure: 
Project: Customer Support System
├── Flows
│   ├── ticket_triage
│   └── response_generation
├── Agents
│   ├── sentiment_analyzer
│   └── response_writer
├── Actions
│   ├── fetch_ticket
│   ├── update_status
│   └── send_notification
└── Project Variables
    ├── API_KEYS
    └── CONFIG

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Creating Projects

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Via UI

  1. Click Home in Top Bar
  2. Click New Project
  3. Name it and add a description
  4. Click Create

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Via Triton

Ask: “Create a new Project for processing customer feedback” Triton will create the structure, ask clarifying questions, and suggest initial components.

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Working with Projects

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Opening and navigating

  1. Navigate to Home
  2. Click your Project to open on Canvas
  3. Double-click nodes to drill into Flows, Agents, or Actions
  4. Use breadcrumbs at top to track your location

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Building in a Project

Via Triton:
  • “Add a validation Flow to this Project”
  • “Create an Action that calls the Stripe API”
Via UI:
  • Right-click Canvas → Add Node
  • Choose component type, configure, and connect

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Project lifecycle

1. Design — Plan inputs, outputs, and data flow
2. Build — Create Actions, configure Agents, build Flows
3. Integrate — Wire components together
4. Test — Validate with sample payloads
5. Deploy — Push to staging, then production
6. Monitor — Track execution success rate and performance
7. Iterate — Add features and optimize

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Common patterns

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Request-Response API

Input → Validate → Process → Format → Output
Use case: API endpoints, data transformations

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Multi-stage pipeline

Input → Extract → Transform → Load → Output
Use case: ETL, data processing, content generation

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Agent with tools

Input (query) → Agent (with tools) → Output (response)
  Tools: search_database, call_api, send_email
Use case: Conversational interfaces, decision support

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Parallel processing

Input → Split → [Process A, B, C] → Merge → Output
Use case: High-volume processing, independent tasks

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Best practices

Single responsibility — Each Project should have one clear purpose
Self-contained — Minimize dependencies on other Projects
Well-documented — Clear descriptions for all components
Tested — Save example payloads for regression testing
Monitored — Track success rates and performance

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Project scope guidelines

Too small ❌
Project: “Uppercase a string” → Should be an Action Just right ✅
Project: “User Onboarding” → Includes validation, account creation, welcome email, setup Too large ❌
Project: “Entire Customer Platform” → Split into multiple Projects: Support, Billing, Notifications Rule of thumb: If you can’t explain what the Project does in one sentence, it’s too large.

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Troubleshooting

Project won’t deploy
Check all components are configured, Project Variables are set, no circular dependencies Execution fails in Project but components work individually
Check connections, field mappings, and data flow Project is slow
Profile execution, identify bottlenecks, add parallel processing or caching Can’t find my Project
Check filters, search by name, verify you’re in the right Organization

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Next steps

See Agents, Flows, Actions, and Build a New Project.