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Part of Axiom Studio

Build Agents
Visually.

AI Studio is a visual workflow canvas for designing, orchestrating, and deploying intelligent agents. Wire up LLMs, vector stores, Kubernetes actions, and custom logic — then ship to production with built-in CI/CD.

Agent Library/Document Processor V21 trigger · 20 stepsTestSaveDeployBuilderExecution⚡ Triggers⚙ Control⬯ Actions▤ Data🔧 ToolsSCHEDULETriggerevery 5 minutesAIAI / LLMExtractorgpt-5.2AIAI / LLMValidatorgpt-5.2AIAI / LLMReconciliationgpt-5.2AIAI / LLMLLM-gatewaygpt-5.2?VECTOR S...AIAI / LLMgoogle-geminigemini-3-pro-preview</>CODEcode-output-jsonpythonJOINJoin-1SLACKSlack-1SPLITSplit-2HTTP REQUESThttp-upload-docPOST docu-ui-prod...+
Visual Agent Builder

Your Agent Logic,
Laid Out on a Canvas

A node-based workflow canvas purpose-built for AI agent pipelines. Drag in LLMs, connect vector stores, route decisions through conditional logic, wire up Kubernetes actions — all visually.

Infinite Workflow Canvas

Drag-and-drop nodes onto an infinite canvas. Connect them to define data flow, decision trees, and reasoning chains. Zoom, pan, and group — just like a whiteboard, but it runs.

First-Class LLM Nodes

Drop in any LLM — GPT-5, Claude, Gemini, Llama, or fine-tuned models. Configure prompts, temperature, and output parsing directly on the node.

Vector Store & RAG

Build retrieval-augmented generation pipelines visually. Connect vector databases, define embeddings, similarity thresholds, and reranking on the canvas.

Conditional Routing

Condition, Switch, Split, and Join nodes for multi-path workflows. Route based on LLM output, confidence scores, or custom logic.

Tool & API Integration

REST APIs, HTTP requests, Slack, email, Kubernetes operations, or MCP-compatible tool servers. Define schemas visually.

Real-Time Debug & Trace

Step through workflows node-by-node. Inspect intermediate outputs, token usage, latency, and decision paths in real time.

Node Palette

Five Categories.
Infinite Combinations.

Every building block you need — from event triggers and flow control to infrastructure actions, data transformations, and AI-native tools.

Triggers

Webhook
HTTP webhook
Schedule
Run on schedule
Manual
Manual trigger
+4 more

Control

Condition
If/else branching
Switch
Multi-way branch
Split / Join
Parallel branches
+3 more

Actions

K8s Get
Get resource
K8s Restart
Rolling restart
K8s Scale
Scale replicas
+5 more

Data

Transform
Transform data
Filter
Filter array items
Sort
Sort array items
+4 more

Tools

Vector Search
Search vector DB
MCP Client
External tools
Web Scraper
Extract web data
+6 more
CI/CD Pipeline

Ship Agent Code
Like Software

Built-in CI/CD with git-backed versioning, automated testing, staged deployments, and rollback.

Continuous Integration

Git-native builds with automated testing on every commit.

docu-ui
DOCUMENT-PROCESSING
CISucceededc402eb4b-15-551
⊸ c402eb4main
18 days agoadmin
21 days agosystem

Staged Deployments

Manual gates for production. Auto-deploy to devtest.

DEPLOY
PRODUCTION
Succeeded⏱ 48s
DEPLOY
DEVTEST
Succeeded⏱ 34s
Engineering Metrics

DORA Metrics, Built In

Track deployment frequency, change failure rate, lead time, and recovery — all automatically calculated.

0.16
Deploys / Day
Target: 1/day (Elite)
HIGH
34%
Change Failure Rate
Target: 30% (High)
MEDIUM
<1h
Lead Time
Commit → production
ELITE
<1h
Recovery Time
Time to restore service
ELITE
How It Works

From Idea to Production
in Four Steps

1

Design

Open the visual canvas. Drag in LLM nodes, vector tools, K8s actions, and control flow. Connect them into your agent workflow.

2

Configure

Set up blueprints for external services. Define schemas, auth, and environment profiles. Attach them as reusable nodes.

3

Test

Run workflows end-to-end on the canvas. Step through nodes, inspect outputs, tune prompts — all in real time with the visual debugger.

4

Deploy

Push to git. CI/CD builds, tests, and deploys to staging and production with approval gates and instant rollback.

Get Started

Stop Writing Agent Spaghetti.
Start Building Visually.

Design, test, and ship production AI agents — with the clarity of a visual canvas and the rigor of enterprise CI/CD.

Frequently Asked Questions

What is AI Studio?
AI Studio is a visual workflow canvas for designing, orchestrating, and deploying intelligent AI agents. It lets you wire up LLMs, vector stores, Kubernetes actions, and custom logic on a drag-and-drop canvas, then ship to production with built-in CI/CD and DORA metrics tracking.
What types of nodes are available in AI Studio?
AI Studio provides five categories of nodes: Triggers (Webhook, Schedule, Manual, and more), Control (Condition, Switch, Split/Join for parallel branches), Actions (Kubernetes operations like Get, Restart, Scale), Data (Transform, Filter, Sort), and Tools (Vector Search, MCP Client, Web Scraper). Each category includes additional nodes for a total of 25+ built-in node types.
Which LLM providers does AI Studio support?
AI Studio supports all major LLM providers through first-class LLM nodes, including OpenAI (GPT-4, GPT-5), Anthropic (Claude), Google (Gemini), Meta (Llama), Mistral, and any fine-tuned or self-hosted models. You configure prompts, temperature, and output parsing directly on the node.
How does the visual workflow canvas work?
The infinite workflow canvas lets you drag and drop nodes to define data flow, decision trees, and reasoning chains. Connect nodes visually to build multi-step agent pipelines, zoom and pan to navigate complex workflows, and group nodes for organization. The canvas runs your workflow exactly as designed.
Does AI Studio include CI/CD for agent workflows?
Yes. AI Studio includes built-in CI/CD with git-backed versioning, automated testing on every commit, staged deployments with manual approval gates for production, auto-deploy to devtest environments, and instant rollback capabilities. All agent workflows are treated as software with proper release engineering.
What are DORA metrics and how does AI Studio track them?
DORA (DevOps Research and Assessment) metrics measure engineering performance: deployment frequency, change failure rate, lead time from commit to production, and mean time to recovery. AI Studio calculates these automatically for your agent workflows, helping you benchmark against industry standards (Elite, High, Medium, Low).
Can I build RAG pipelines in AI Studio?
Yes. AI Studio supports building retrieval-augmented generation (RAG) pipelines visually. Connect vector databases, define embedding strategies, set similarity thresholds, and configure reranking — all on the canvas without writing code.
How do I debug agent workflows?
AI Studio includes a real-time debug and trace system. Step through workflows node-by-node, inspect intermediate outputs at each stage, monitor token usage and latency, and trace decision paths. The visual debugger shows execution flow directly on the canvas.
How is AI Studio deployed?
AI Studio runs on Kubernetes as part of the Axiom Studio platform. Workflows are git-backed and deploy through the built-in CI/CD pipeline. You can configure staged deployments with automatic devtest deployment and manual production gates.

See It In Action

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