AI Orchestration & Pipeline Management Tool

AI orchestration is getting more complex every quarter. You're managing prompt chains, model pipelines, fine-tuning jobs, evaluation runs, and deployment workflows - all with dependencies that cascade when something fails. The models are getting better, but the tooling around managing these workflows hasn't kept up. Most teams track AI pipelines in spreadsheets, Notion pages, or scattered GitHub issues.

t0ggles is the project management tool that gives AI orchestration teams everything they need to track pipelines, coordinate deployments, and manage the full lifecycle of AI systems. With task dependencies that mirror your pipeline DAGs, Gantt charts for deployment timelines, and an MCP server that lets AI agents participate in their own management, t0ggles brings structure to the chaos. All for $5/user/month with every feature included.

#The Challenge: Why AI Teams Need Better Orchestration Tools

AI orchestration is project management at machine speed with human oversight. Traditional tools don't account for its unique characteristics:

Pipelines have strict ordering. Data preprocessing feeds into training, training feeds into evaluation, evaluation gates deployment. These dependencies are non-negotiable, but most project management tools treat them as afterthoughts - if they support them at all.

Experiments branch and multiply. One hypothesis spawns five experiments. Three show promise and spawn their own variants. Tracking which experiment uses which dataset, model version, and hyperparameters requires structure that kanban boards alone can't provide.

Human-in-the-loop is constant. AI systems need review checkpoints - model evaluation before deployment, safety testing before release, human feedback integration before retraining. These approval gates need to be visible and trackable, not buried in Slack threads.

Cross-functional coordination is messy. ML engineers, data engineers, product managers, and safety reviewers all touch the pipeline at different stages. Everyone needs visibility into their part without drowning in the full complexity.

#How t0ggles Helps AI Orchestration Teams

#Task Dependencies: Mirror Your Pipeline DAGs

AI pipelines are directed acyclic graphs, and your project board should reflect that. t0ggles' native task dependencies let you model your orchestration workflows exactly:

• Data ingestion must complete before preprocessing
• Preprocessing must complete before model training
• Training must complete before evaluation
• Evaluation must pass before staging deployment
• Staging deployment must pass before production deployment

Set predecessor/successor relationships with optional lag days for buffer time between stages - deploy to staging, wait 48 hours for monitoring, then deploy to production. Circular dependency detection catches impossible sequences before they waste pipeline compute.

In Gantt view, your entire pipeline is visible as a timeline with dependency arrows connecting each stage. The critical path - the sequence that determines your deployment date - is immediately obvious.

#Multi-Project Boards: Organize by Pipeline

AI teams typically manage multiple parallel efforts: the production model pipeline, an experimental fine-tuning project, a data quality initiative, and infrastructure upgrades. t0ggles lets you manage multiple projects on one board.

Color-code each pipeline: Production (red), Experiments (blue), Data Quality (green), Infrastructure (yellow). See everything on one board or use Focus Mode to drill into a single pipeline. When a data quality issue affects the production pipeline, the connection is visible because both projects share the same workspace.

#Custom Properties: Track Model Metadata

Every AI workflow has domain-specific data that generic task fields can't capture. Custom properties in t0ggles let you add structured metadata to any task:

• Model Version (text): gpt-4-turbo-2024-04-09, llama-3.1-70b-instruct
• Dataset (select): Training Set v3, Eval Suite 2.1, Production Logs Q1
• Accuracy (number): Track eval scores directly on tasks
• Environment (select): Dev, Staging, Production
• Compute Cost (number): Track spend per experiment

Filter and sort by any property. Want to see all tasks using a specific model version? One filter click. Need all experiments above 85% accuracy? Sorted.

#Milestones: Gate Your Deployment Stages

AI deployments need clear gates - checkpoints where human review is required before proceeding. Milestones in t0ggles mark these critical decision points:

• Data Validation Complete: All training data quality checks pass
• Model Evaluation Gate: Eval metrics meet production thresholds
• Safety Review Complete: Red team testing finished, no critical findings
• Production Deployment Approved: Stakeholder sign-off received

Track milestone progress with completion percentages. When blocked tasks pile up before a milestone, the bottleneck is visible immediately.

#MCP Server: AI Agents in the Loop

The MCP server creates a uniquely fitting workflow for AI orchestration teams. Your AI agents can interact with the project board directly:

• Automated pipeline scripts can update task status when stages complete
• AI coding agents can create tasks for failed evaluations or flagged outputs
• Engineers can query the board from their development environment without context switching

When an eval pipeline finishes, your automation can update the corresponding task in t0ggles with results. When a model passes safety testing, the reviewing agent can mark the milestone complete and unblock deployment tasks. The project board becomes a coordination layer that both humans and AI systems interact with.

#t0ggles Crew: Automated Pipeline Execution

Beyond tracking pipelines on a board, t0ggles Crew lets you actually run them. Crew is a free desktop companion app that orchestrates AI coding agents to autonomously pick up tasks, execute work, and report results.

For AI orchestration teams, Crew's pipeline scheduling mirrors how you think about pipeline stages:

• Planning pipelines research the codebase and produce implementation plans
• Development pipelines implement changes, run validation, and open pull requests
• Review pipelines check outputs and either approve or send work back for iteration
• Chained execution connects these stages with After Pipeline scheduling - one pipeline triggers the next automatically

Schedule modes give you fine-grained control: run pipelines on intervals, at fixed times, after another pipeline completes, or automatically when tasks are assigned. Phased development breaks complex work into numbered phases with a review between each, keeping changes incremental and reviewable.

This creates a meta-orchestration layer - your AI orchestration team's own development work is itself orchestrated by AI agents through t0ggles Crew. Download it free from the t0ggles Crew page.

#Reports: Pipeline Health at a Glance

Board reports give you visibility into pipeline health without manual status meetings:

• Task distribution: See how work is spread across pipeline stages
• Completion rates: Track velocity across experiments and deployments
• Dependency health: Identify bottlenecks where blocked tasks are piling up
• Workload: See who's overloaded across different pipeline responsibilities

Export to CSV or PDF for stakeholder updates.

#AI Orchestration Workflows In t0ggles

#Model Training Pipeline

Create a project for each training run. Tasks represent pipeline stages: Data Collection, Preprocessing, Training, Evaluation, Deployment. Dependencies enforce the correct order. Custom properties track model version, dataset, and hyperparameters.

As each stage completes, move the task forward. Gantt view shows the timeline and highlights any stage that's behind schedule. When evaluation scores are ready, add them as a custom property value directly on the task. The entire training run is documented - parameters, results, and decisions - in one place.

#Experiment Tracking

Set up an "Experiments" project with statuses: Hypothesis, Running, Evaluating, Complete, Abandoned. Each experiment is a task with custom properties for model, dataset, accuracy, latency, and cost.

List view lets you sort and compare experiments side by side. Filter by accuracy above your threshold, sort by cost, and identify the most efficient model variant. Notes attached to each experiment capture methodology, observations, and conclusions.

When an experiment succeeds, create dependent tasks for production integration - the dependency chain from experiment to deployment is explicit and trackable.

#Production Deployment Coordination

A production model deployment touches ML engineering, platform engineering, product, and safety. Create tasks for each team's responsibilities and set dependencies:

1. ML Engineering: Final model validation (no dependencies)
2. Platform: Update serving infrastructure (depends on #1)
3. Safety: Run red team evaluation (depends on #1)
4. Product: Update feature flags and monitoring (depends on #2 and #3)
5. All: Production deployment (depends on #4)

Each team sees their tasks and understands what they're waiting on. The Gantt chart shows the full deployment timeline. When platform finishes their infrastructure update, the dependent tasks automatically become unblocked.

#What AI Orchestration Teams Need vs What t0ggles Delivers

What You Need	How t0ggles Delivers
Model pipeline DAG tracking	Task dependencies with predecessor/successor relationships and lag days
Experiment comparison	Custom properties with filtering, sorting, and List view
Deployment gate management	Milestones with progress tracking and dependency blocking
Cross-team coordination	Multi-project boards with color coding and Focus Mode
Pipeline timeline visibility	Gantt charts with dependency arrows and critical path
AI agent integration	MCP server for automated status updates and task management
Metadata tracking	Custom properties for model versions, datasets, eval scores, costs
Automated pipeline execution	t0ggles Crew runs AI agents on schedules with chained pipelines
Stakeholder reporting	Board reports with CSV/PDF export

#Why Choose t0ggles for AI Orchestration

vs Jira: Jira can model complex workflows but the setup time and learning curve is steep. AI teams move fast and iterate constantly - they need a tool that's productive in minutes, not days.

vs Linear: Linear is clean and fast but designed for software engineering workflows. It lacks the custom properties and flexible project structure that AI orchestration demands.

vs MLflow/Weights & Biases: These are excellent experiment tracking tools, but they're not project management. You still need to coordinate the human work around AI systems - the planning, reviews, deployments, and cross-team handoffs. t0ggles handles the project coordination while your ML tools handle the model tracking.

vs spreadsheets: Spreadsheets offer flexibility but zero automation, no dependencies, no real-time collaboration, and no AI integration. As your pipeline complexity grows, spreadsheets become unmaintainable.

t0ggles gives AI teams the structure of serious project management with the speed and simplicity that fast-moving research environments demand.

#Simple, Affordable Pricing

One plan. One price. Every feature.

$5 per user per month (billed annually) includes:

• Unlimited boards, projects, tasks, notes, and files
• Task dependencies with Gantt view
• Custom properties for tracking model metadata
• Milestones for deployment gates
• MCP server for AI agent integration
• t0ggles Crew for automated pipeline execution (free)
• Board automations for pipeline triggers
• Full reporting and analytics
• All integrations (GitHub, Zapier, Google Calendar)

No feature tiers. No per-seat surprises.

14-day free trial - start organizing your pipelines today.

#Get Started Today

AI orchestration is too important to manage in spreadsheets and scattered documents. t0ggles gives your team the visibility, dependencies, and coordination needed to ship AI systems reliably.

Start your free trial and bring structure to your AI workflows.