Visual AI agent builder · orchestrator, sub-agents, guardrails, one Deploy button
Drag an orchestrator onto the canvas, give it specialist sub-agents, wrap it in guardrails and a prompt-injection shield, test it with a full trace, then hit Deploy. Every change ships as a new version behind the same endpoint.
Use cases
Every agent is the same shape: an orchestrator that talks to the caller, sub-agents it delegates to, and the guardrails that keep it on the job. Here is what that looks like in practice.
The orchestrator answers customers; an Order Tracker sub-agent looks up shipping status. A guardrail keeps it on topic and the shield blocks jailbreak attempts.
Answers policy and process questions for staff. An output guardrail rewrites anything that leaks confidential detail, and every answer carries its trace.
Call the agent from your website form. It asks two follow-up questions, scores the lead and returns structured output your CRM can store.
Drafts posts and replies inside a natural-language brand policy. Off-brand output is rewritten to comply rather than blocked.
One orchestrator reads the request and hands it to the right specialist: billing, technical, or account. Each specialist has its own model and prompt.
The Agency plan runs up to 20 agents with 20 sub-agents each. Every client gets its own endpoint, API key and version history.
How it works
No orchestration code, no prompt plumbing, no separate moderation service. The builder compiles your design into a runtime and hosts it.
Add an Orchestrator and connect Sub-agents for the work it should delegate. Write each prompt in plain words, then press Polish to turn it into a production-grade prompt.
Drop in Guardrails with the policies your product needs and an Injection Shield that stops jailbreak attempts before the model sees them. Test it in the playground with a full trace.
One click compiles the design into a versioned deployment. Call it with a bearer key from any language. Change the design, deploy again, same URL.
Features
Each building block is a node on the canvas. Connect them, test them, deploy them.
Talks to the caller, decides what to do and delegates. Pick the model, set the step budget, write the prompt.
Specialists with their own model and prompt. Connect one to the orchestrator and it becomes a tool the orchestrator can call.
Natural-language policies checked on input or output. Block with your own message, or rewrite the text to comply.
Pattern rules, Meta's Llama Prompt Guard 2 and an LLM classifier. Optional hardening treats user input as data, never instructions.
Turn a one-line idea into a structured system prompt or policy. Preview, then accept or discard.
Versioned, immutable deployments, per-agent API keys with rotation, and a stage-by-stage trace on every run with latency and token usage.
GPT-OSS 120B and 20B, Llama 3.3 70B, Llama 4 Maverick and Kimi K2, all served by Groq for low latency. Choose a model per node.
Flip one switch in the Deploy dialog and the agent appears in Flows as an “Ask <agent>” step, re-synced on every deploy. No keys to copy.
Send a message or a whole conversation. Get back the answer, whether a guardrail or the shield blocked it, and the trace that explains why.
Comparison
Most agent builders assume you will bring hosting, a moderation layer or a specific vendor's models. Agent Studio ships the whole pipeline, shield to trace, behind one URL.
Swipe the table to compare →
| Criteria | Agent Studio | OpenAI Agent Builder | Microsoft Copilot Studio | Dify | Flowise / Langflow |
|---|---|---|---|---|---|
| How you build | Canvas of orchestrator, sub-agents, guardrails and shield; prompts in plain words with Polish | Visual canvas in the OpenAI platform | Low-code builder inside the Microsoft 365 ecosystem | Open-source visual LLM app builder with workflow canvas | Open-source node graphs of LangChain-style components |
| Models | GPT-OSS, Llama 3.3, Llama 4 Maverick and Kimi K2 on Groq, chosen per node | OpenAI models | Microsoft-hosted models | Many providers, bring your own keys | Many providers, bring your own keys |
| Guardrails | Natural-language input and output policies that block or rewrite, built in | Guardrail options available | Enterprise governance and policy controls | Moderation features available | Assemble your own from components |
| Prompt-injection defence | Three layers built in: pattern rules, Llama Prompt Guard 2 and an LLM classifier, with optional hardening; every decision in the trace | Safety checks available as guardrails | Platform-level protections | Add a moderation step yourself | Add it yourself |
| Hosting and deployment | Fully hosted; one click deploys a versioned API with a bearer key | Hosted by OpenAI; consumed through their APIs and SDKs | Hosted by Microsoft; published to Teams, web and other channels | Self-host, or their cloud | Self-host, or their cloud |
| Observability | Stage-by-stage trace, latency and token usage on every call, returned with the response | Tracing in the OpenAI platform | Analytics in the Microsoft admin experience | Logs and annotations in the app | Depends on your setup |
| Pricing model | Flat monthly plans from $10 after a 7-day free trial | Usage-based on OpenAI API pricing | Per-message or capacity packs | Free open source plus cloud plans | Free open source plus cloud plans |
| Best for | Shipping a guarded agent as an API in minutes with no orchestration layer to maintain | Teams standardised on OpenAI | Organisations living in Microsoft 365 | Teams that want open-source control and self-hosting | Developers experimenting with component graphs |
Choose OpenAI Agent Builder or Copilot Studio when you are committed to that vendor's ecosystem. Choose Dify or Flowise when you want to self-host and assemble every piece. Choose Agent Studio when you want the shield, guardrails, orchestration, versioning and trace already wired, and a live endpoint before lunch.
Competitor details are summarised from their public websites in September 2026 and simplified. Check each vendor for current features and pricing.
Pricing
Start with a 7-day free trial of one agent. Pick a plan when you are ready to keep it running. Change or cancel any time.
$0/month
7 days, no card.
$10/month
One production agent.
$25/month
Two agents with richer teams.
$49/month
Three agents for a growing team.
$200/month
Build for many clients.
When a trial ends, the builder and deployed agents pause and nothing is deleted. Pick a plan and everything resumes as you left it.
Agent Studio is a visual AI agent builder. You design an orchestrator and its sub-agents on a canvas, add guardrails and a prompt-injection shield, test in a playground with a full trace, and deploy the agent as a versioned API with one click.
No. You design on a canvas and write prompts in plain language. The only code is the one curl command you use to call your agent, and Agent Studio generates that for you.
A specialist with its own prompt and model. When you connect it to the orchestrator, the orchestrator can hand it tasks and use the result, the way a lead hands work to a teammate.
Three layers: fast pattern rules, Meta's Llama Prompt Guard 2 classifier, and a general LLM check. It can also harden your orchestrator so user text is treated as data. Every decision shows up in the trace.
GPT-OSS 120B and 20B, Llama 3.3 70B, Llama 4 Maverick and Kimi K2, all served by Groq for low latency. You can choose a model per node.
Every agent gets one endpoint that stays stable across versions. Send a message or a whole conversation with the agent's bearer API key and you get back the answer, whether a guardrail or the shield blocked it, and the trace that explains why. Keys can be rotated any time.
Those builders live inside one vendor's ecosystem and models. Agent Studio runs open models on Groq, ships guardrails and a three-layer prompt-injection shield as first-class nodes, and deploys every design as a versioned API with a trace on every call, on flat monthly pricing.
Those are open-source builders you host and assemble yourself, including moderation. Agent Studio is fully hosted: the shield, guardrails, orchestration, versioning and tracing are already wired, so you go from blank canvas to a live endpoint in about ten minutes.
The builder and your deployed agents pause, and you get an email with a link to choose a plan. Nothing is deleted; pick a plan and everything resumes as you left it.
Agent Studio was founded by Nishanthan Janarthanarajah, known as Nishy (nishyai.com), an AI engineer based in Colombo, Sri Lanka. Nishy is also the founder of Flows, and agents deployed in Agent Studio can be added to Flows automatically as workflow steps.
Agent Studio is designed, built and operated by Nishanthan Janarthanarajah, a full stack and AI engineer based in Colombo, Sri Lanka. It runs on Next.js and Vercel with Neon serverless Postgres, Groq for models, Google sign-in and Polar billing.
Free for 7 days. No card. Bring one job you keep answering by hand and let an agent take it.
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