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

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.

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.

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.

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.

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.