AI Features Guide
Unlock the full power of AI collaboration with Workunit's multi-LLM features, context-aware assistance, and intelligent workflow automation
Last updated: October 2025
Overview
Workunit is designed from the ground up for AI collaboration. Unlike traditional project management tools adapted for AI, Workunit treats AI models as first-class team members, enabling seamless context sharing, real-time synchronization, and intelligent workflow automation across multiple LLMs.
This guide explores Workunit's AI features in depth, showing you how to leverage multi-LLM collaboration, preserve context across sessions, and accelerate your development workflow with AI-powered assistance.
Multi-LLM Collaboration
This is Workunit's core value proposition: enabling multiple AI models to collaborate on the same project by sharing context in real-time. Stop re-explaining your project every time you switch models. Start leveraging the unique strengths of Claude, GPT, Gemini, and future models—all working from the same shared context.
Why Use Multiple AI Models?
Different AI models excel at different tasks. By using the right model for each phase of your work, you can dramatically improve both speed and quality:
- • Breaking down complex problems into clear task hierarchies
- • Designing system architecture with security and scalability in mind
- • Writing comprehensive problem statements and success criteria
- • Code review and architectural analysis
- • Fast code generation and feature implementation
- • Writing tests and boilerplate code efficiently
- • Debugging and fixing implementation issues
- • Executing well-defined tasks from the plan
- • Analyzing performance metrics and identifying bottlenecks
- • Processing large datasets and generating insights
- • Code quality analysis and optimization suggestions
- • Visual reasoning and diagram interpretation
Real-World Multi-Model Workflows
Here's how teams use multiple models together on the same workunit:
Example: Building a Payment Integration
- • Detailed problem statement with security considerations
- • 8 well-defined tasks covering API integration, webhooks, testing
- • Success criteria including PCI compliance and error recovery
- • AI context documenting security requirements and architecture decisions
- • Reads Claude's plan and understands security requirements
- • Implements API client, payment processing, and webhook handling
- • Marks tasks complete as work progresses
- • Adds implementation notes to AI context (libraries used, gotchas)
- • Reads GPT's implementation notes from AI context
- • Reviews code for webhook signature verification
- • Suggests improvements for idempotency handling
- • Updates AI context with security findings
- • Reads Claude's security recommendations from AI context
- • Applies improvements to webhook handling and idempotency
- • Writes unit and integration tests covering edge cases
- • Marks remaining tasks complete
- • Reads full context from Claude's plan and GPT's implementation
- • Analyzes test performance metrics
- • Identifies database query bottleneck in webhook processing
- • Updates AI context with optimization recommendations
Result: Five different AI interactions on the same workunit, each model building on previous work. Total context re-explanation time: zero. Each model used its unique strengths while maintaining perfect continuity.
Real-Time Status Synchronization
When any AI model updates a workunit or task, the change is immediately visible to all other models. This prevents duplicate work and enables true parallel collaboration:
How Synchronization Works
- Claude marks Task 1 as "In Progress" via MCP call
- Workunit immediately updates the task status in its database
- GPT queries the workunit 2 seconds later, sees Task 1 is in progress
- GPT skips Task 1 and picks up Task 2 instead
- Both models work in parallel on independent tasks
- When Claude finishes, it marks Task 1 "Done"
- GPT and Gemini instantly see the update when they next query
- No duplicate work, no conflicts, no manual coordination needed
Choosing the Right Model for Each Task
Use this decision guide to maximize efficiency:
- • Initial project planning and task breakdown
- • Architecture design and system design decisions
- • Security reviews and compliance analysis
- • Complex code reviews requiring deep reasoning
- • Writing comprehensive documentation
- • Executing well-defined implementation tasks
- • Writing tests and boilerplate code quickly
- • Bug fixes and debugging sessions
- • Refactoring and code cleanup
- • API integration and endpoint implementation
- • Performance analysis and optimization
- • Processing and analyzing log files or metrics
- • Code quality analysis and pattern detection
- • Visual diagram interpretation and creation
- • Data transformation and migration tasks
AI Context Writing
One of Workunit's most powerful features is that AI models can write context directly to workunits. This creates a trail-of-thought documentation system where insights, decisions, and learnings are preserved for future sessions and other AI models.
What is AI Context?
AI context is a markdown field on each workunit where AI models can write summaries, document decisions, record patterns discovered, and preserve reasoning for future reference. Think of it as the AI's lab notebook—capturing not just what was done, but why and how.
- • Session Summaries: What was accomplished in each AI session
- • Technical Decisions: Why specific approaches were chosen over alternatives
- • Patterns Discovered: Insights about code structure, optimization opportunities, gotchas
- • Implementation Notes: Libraries used, configuration details, setup steps
- • Blockers & Solutions: Problems encountered and how they were resolved
- • Next Steps: What should be done next and what to consider
When AI Models Should Write Context
Encourage your AI assistants to write context at these key moments:
- • After completing significant work: "Update the workunit with what we accomplished today"
- • When making important decisions: "Document why we chose JWT over sessions"
- • After discovering patterns: "Add notes about the middleware chain pattern we're using"
- • When resolving complex issues: "Document how we fixed the race condition"
- • Before pausing work: "Summarize current state and next steps before we switch to another task"
Best Practices for AI Context
- Technical decisions made (JWT vs sessions, bcrypt cost factor)
- Libraries used and why
- Patterns discovered (middleware chain structure)
- Gotchas encountered (token expiry handling)
- Next steps (refresh token implementation)"
Context-Aware Help
AI models connected to Workunit via MCP understand your project context automatically. Instead of lengthy explanations, you can ask direct questions and get informed assistance based on your workunit's problem statement, tasks, assets, and AI context.
How AI Models Understand Your Context
When you ask an AI assistant about a workunit, it automatically receives:
- • Problem Statement: The core problem you're solving and why it matters
- • Success Criteria: How you'll know when the work is complete
- • Task History: What's been completed, what's in progress, what's blocked
- • Linked Assets: Related products, systems, people, and knowledge
- • AI Context: Summaries and insights from previous AI sessions
- • Current Status: Active, paused, completed, or archived
Getting AI Assistance on Specific Workunits
Here are practical examples of how to get context-aware help:
AI-Generated Summaries and Insights
Ask AI models to generate summaries of your workunits to quickly understand status or prepare updates:
Next Steps
Ready to unlock AI-powered collaboration? Here's where to go next:
MCP Integration
Connect your AI tools to Workunit and start using multi-LLM workflows
Quick Start Guide
Create your first AI-enabled workunit in 10 minutes
Core Concepts
Deep dive into workunits, tasks, and context preservation
Team Setup
Enable AI collaboration for your entire team
Questions About AI Features?
We're here to help you get the most out of AI-powered workflows.