Visual AI agent builder

AI Agent Builder — describe your agent, we build it.

Skip the drag-and-drop. Tell Arahi what you want an agent to do — in one sentence — and a running, tool-wired agent shows up in minutes. No code. No flowcharts. Just the agent you had in your head.

  • 1,500+ apps auto-wired
  • Deploys in minutes
  • No code required

Build your agent — Free

How it works

From one sentence to a shipping agent.

Four stages. Most first-time users get a running first draft in under two minutes, then spend a few more refining it.

  • Stage 01

    Describe the agent you want.

    Triggers, tools, and outcomes parsed from your sentence. Ambiguous? Arahi asks one clarifying question instead of guessing.

    • One or two sentences, plain English
    • Name the trigger, the tools, the outcome
    • No prompt templates, no flowchart syntax

  • Stage 02

    Review the plan. Nothing is hidden.

    The generated agent is a real graph in Arahi's visual builder. Rename steps, swap tools, adjust the schedule, or add an approval gate with two clicks.

    • See the generated agent as an editable graph
    • Swap tools, change tone, add approval gates
    • Every step visible — nothing is hidden

  • Stage 03

    Deploy. Credentials auto-wired.

    Arahi only asks for credentials on the tools the agent actually uses. No 90-day rollout, no re-build when you tweak a step later.

    • One click — agent goes live on its trigger
    • Credentials auto-requested for tools in use
    • Runs on schedule, event, or chat command

  • Stage 04

    Iterate without rebuilding.

    Most teams use the generated agent as a 90%-there first draft. Tweak the schedule, drop a code node for a custom transform, or re-prompt — changes ship without a redeploy.

    • Open the visual builder any time
    • Re-prompt to regenerate — no rebuilding
    • Every change ships without a redeploy

Agents you can describe today

One sentence in. A real agent out.

Eleven prompts real teams have given Arahi. Each one became a running, tool-wired agent — most of them live inside five minutes.

Whether you want to build an AI agent from scratch or customize a pre-built template, Arahi adapts to the level of control you want. Most teams start from a marketplace template and tweak the trigger, tone, or steps — but every custom AI agent on the platform is built with the same tools, so there's no ceiling on what you can ship.

  • Lead researcher

    Pulls inbound leads, enriches them from the web + your CRM, and hands sales a ranked list each morning.

  • Support triage

    Reads Gmail or Zendesk, classifies by intent, drafts a reply — never sends without your approval.

  • Revenue alerts

    Watches Stripe for failed charges and dunning events — pings the owner on Slack and opens a Linear ticket.

  • Contract runner

    When DocuSign signs, it creates the Stripe customer, kicks off Notion onboarding, and Slacks the team.

  • Meeting prep

    15 minutes before every call, drops a prep card — recent threads, open tasks, last email — into Slack.

  • Social monitor

    Watches X and Reddit for brand mentions, summarizes sentiment daily, and flags anything negative in real time.

  • Inbox assistant

    Labels, replies, and schedules on your behalf — in your tone, with a human-in-the-loop gate before it sends.

  • Ad spend recap

    Every Friday, pulls Meta + Google Ads spend, compares to plan, and emails the CMO a one-page summary.

  • QBR report

    Quarterly, it compiles usage, support volume, and expansion signals per account into a ready-to-send deck.

  • Churn watcher

    Spots at-risk accounts from product usage + support tickets and hands CS a weekly intervention list.

  • Onboarding agent

    New signup triggers a personalized welcome sequence — email, in-app nudges, and a Slack intro to CS.

Describe it. We'll build it.

Free to start. No credit card. Write one sentence about the agent you wish existed — Arahi assembles it, wires the tools, and hands it to you ready to deploy.

Need a custom build? See our custom AI solutions.

Tool landscape

Arahi vs Zapier vs Make vs n8n

Zapier, Make, and n8n are workflow automation tools — they move data between apps when a trigger fires. Arahi is an AI agent builder: your agents reason, adapt, and make decisions across multi-step work autonomously.

If your workflow is genuinely a linear trigger-to-action sequence — a new form submission creating a CRM record, a Stripe payment posting to Slack — Zapier is often the simplest choice. If you need complex branching logic but deterministic rules, Make and n8n are stronger. Arahi's sweet spot is the work in between: tasks where the agent needs to decide what to do next based on context, read unstructured input like emails or documents, handle exceptions gracefully, and operate across many apps in a single run. That's the agent-shaped work.

  • AI agents (autonomous, goal-based)

    • Arahi — Yes — native
    • Zapier — No (rules only)
    • Make — No (rules only)
    • n8n — Partial (beta AI nodes)

  • No-code builder

    • Arahi — Yes
    • Zapier — Yes
    • Make — Yes
    • n8n — Partial — needs setup

  • Integrations

    • Arahi — 1,500+
    • Zapier — 6,000+
    • Make — 1,800+
    • n8n — 400+

  • Pricing model

    • Arahi — Per seat + usage
    • Zapier — Per task
    • Make — Per operation
    • n8n — Self-host / per seat

  • Best for

    • Arahi — Autonomous multi-step agents
    • Zapier — Simple app-to-app triggers
    • Make — Complex branched workflows
    • n8n — Developers self-hosting

Frequently asked questions

What is an AI agent?
An AI agent is an autonomous software system that can plan, make decisions, and take actions to accomplish a goal — not just answer questions like a chatbot. You give it an objective ("triage incoming tickets and draft replies"), and it figures out the steps on its own: reading the tickets, looking up customer history in your CRM, drafting a response grounded in your help docs, and handing off edge cases to a human. Modern agents chain together reasoning, memory, and tools (APIs, databases, SaaS apps) to complete multi-step work end-to-end. The key difference from traditional automation is adaptability — an agent handles unexpected inputs, recovers from errors, and adjusts its approach mid-run instead of breaking when reality doesn't match a pre-written flowchart.
Can I build an AI agent without coding?
Yes. Arahi's AI agent builder is genuinely no-code. You describe your agent in plain English, connect apps by clicking through OAuth flows, and design workflows in a visual canvas. The platform handles the LLM orchestration, prompt engineering, error handling, token management, and infrastructure — so you're never writing Python or managing a vector database. Developers who want more control can drop into custom logic, call external APIs, or inject their own code blocks, but it's optional. The vast majority of Arahi agents are built entirely without writing code, typically by operations managers, marketers, sales leaders, or founders who understand the problem space better than any engineer would.
How many integrations does Arahi support?
Arahi connects to 1,500+ apps out of the box — including Gmail, Slack, HubSpot, Salesforce, Notion, Google Workspace, Microsoft 365, Stripe, Shopify, Zendesk, Intercom, Postgres, Snowflake, and virtually every major SaaS tool. New connectors are added every week based on customer demand. For anything not in the library, your agents can connect via webhooks, custom HTTP actions, or direct database queries — so you're never blocked by an unsupported tool. Internal systems with APIs can also be wired in without middleware, which is the typical sticking point for enterprise teams trying to replace legacy automation.
What's the difference between an AI agent and a chatbot?
Chatbots generate text responses to questions. AI agents take action — they execute workflows, call APIs, update databases, send emails, process data, and orchestrate multi-app automations. A chatbot tells a customer their order status; an agent looks up the order, checks shipping with the carrier, drafts a personalized update, files a refund if the package is lost, and logs everything back to your CRM. Chatbots are a conversational interface; agents are autonomous workers. Many modern products use both together — an agent doing the underlying work and a chat UI giving users a way to instruct and observe it. Agents are built for doing work autonomously, not just answering questions.
How much does it cost to build an AI agent?
Arahi starts at $49/month for individuals and small teams, with enterprise plans that scale based on usage, seats, and compliance requirements. There's a free trial so you can build and test your first agent before paying. Compare this to building in-house: a single backend engineer to maintain agent infrastructure typically runs $150,000+/year, plus compute, monitoring, vector database costs, and ongoing prompt engineering — which quickly adds up to $250K-$400K in the first year alone. That's why most teams choose a managed AI agent builder instead of rolling their own. The ROI usually becomes obvious within the first month, since a single automated workflow can replace 10-20 hours per week of manual operator work.