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The AI agent builder where you just describe it.

Skip the drag-and-drop. Tell DeskFerry what you want an agent to do — in one sentence — and a running, tool-wired agent shows up in minutes. No code, no flowcharts, across 1,500+ apps you already use.

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Describe your agent

“Every Monday at 9am, pull last week’s Stripe revenue and post a short summary to #finance.”

DeskFerryBuild

Weekly Revenue Recap

Generated in 14s · 6 components wired

Ready

Apps

StripeSlack

Instructions

Summarize last week's revenue, flag anomalies vs plan, post a 3-bullet recap to #finance in your tone.

Trigger

Weekly · Monday 9:00 AM

Model

Claude Opus 4.7

Memory

Last 12 weeks of revenue snapshots

Knowledge

Finance reporting templates

Use cases

One sentence in. A wired, running agent out. Pick a use case to see another.

What gets built

Six decisions the builder makes for you.

A sentence is not a spec. These are the parts DeskFerry infers, wires, and hands back as an editable agent — the work you'd otherwise do node by node.

01

Trigger

Reads “every Monday at 9” or “when a contract is signed” out of your sentence and wires the schedule or event listener behind it.

02

Tools

Picks the connectors the job actually needs from 1,500+ apps, and asks you for credentials on those alone.

03

Instructions

Turns the sentence into the agent’s standing brief — what to produce, in what tone, and what to do at the edges.

04

Memory

Carries what happened on the last run into the next one, so the agent stops repeating itself by week three.

05

Knowledge

Grounds the output in your docs, pricing, and policies rather than whatever the model assumed about your business.

06

Approval gates

Marks the steps a human signs off on. Nothing is sent, charged, or written back until you say so.

The difference

Describe the agent. Don't draw it.

Every other builder starts with a blank canvas and a node palette. DeskFerry starts with the sentence you'd have used to explain the job to a colleague.

When you ask

Every Monday, recap last week’s revenue in #finance.

A workflow automation tool

“Add a Schedule trigger, then a Stripe module, then a Formatter, then a Slack action — and map every field by hand.”

DeskFerry

  1. Wiring the Stripe read against last week’s window
  2. Comparing the total to plan and flagging the anomalies
  3. Drafting a three-bullet recap in your tone
  4. Setting the schedule to Monday 9:00 AM, weekly

“Weekly Revenue Recap” exists, wired and scheduled, from one sentence — and every step of it is still editable.

When you ask

Triage the support inbox and draft the replies.

A chatbot bolted onto a helpdesk

“Here’s a template you could use for a refund request. Paste the customer’s email and I’ll adapt it.”

DeskFerry

  1. Classifying every new email by intent and urgency
  2. Grounding the answer in your help docs and refund policy
  3. Writing the reply into drafts, never into the outbox
  4. Posting it for a one-tap approval before it goes

The queue is sorted and the replies are written by the time you open it. You approve; the agent sends.

When you ask

When a contract is signed, start onboarding.

A drag-and-drop canvas

“That’s nine nodes, three branches, and a webhook. Budget an afternoon, and a re-test after every change.”

DeskFerry

  1. Listening for the signed envelope
  2. Creating the customer from the contract fields
  3. Cloning the onboarding template for the account
  4. Introducing CS in the channel with the details attached

Four tools, one sentence, no flowchart. The first signed contract after deploy runs it end to end.

When you ask

Actually — check for duplicates before it writes anything.

A visual builder

“Open the flow, insert a node before step three, re-map every downstream field, then re-test the whole run.”

DeskFerry

  1. Reading the change as a change, not a rebuild
  2. Adding the lookup ahead of the write step
  3. Leaving the rest of the agent exactly as it was

Changed in a sentence and live in under a minute — no redeploy, no re-mapping, no broken downstream steps.

How it works

From one sentence to a shipping agent.

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

  1. Stage 01

    Describe the agent you want.

    One or two sentences, plain English. Name the trigger, the tools, and the outcome — there are no prompt templates and no flowchart syntax to learn. If the sentence is ambiguous, DeskFerry asks one clarifying question instead of guessing.

  2. Stage 02

    Review the plan. Nothing is hidden.

    The generated agent arrives as a plain-English plan with every step listed. Rename steps, swap a tool, change the tone, adjust the schedule, or drop in an approval gate before a single run happens.

  3. Stage 03

    Deploy. Credentials auto-wired.

    One click and the agent goes live on its trigger. DeskFerry only asks for credentials on the tools it actually uses, and every run lands in a log you can open.

  4. Stage 04

    Iterate without rebuilding.

    Most teams treat the generated agent as a 90%-there first draft. Re-prompt to change a step, or edit the agent's instructions directly — changes ship without a redeploy.

Why it stays fast

No flowchart to learn first
You describe the job in words. There is no canvas or node palette to learn.
Nothing is hidden
Every step of the generated agent is visible and editable before it runs.
No rebuilds
Change your mind in a sentence. The rest of the agent stays where it was.

Agents you can describe today

One sentence in. A real agent out.

Eleven prompts real teams have given DeskFerry. Each one became a running, tool-wired agent.

Lead researcher

Inbound leads enriched from the web and your CRM, handed to sales as a ranked list each morning.

Support triage

Every ticket classified by intent and answered with a draft — never sent without your approval.

Revenue alerts

Failed charges and dunning events ping the owner and open a ticket before anyone notices the gap.

Contract runner

A signature starts billing, onboarding, and the internal hand-off without anyone chasing it.

Meeting prep

A prep card in Slack fifteen minutes before every call — recent threads, open tasks, last email.

Social monitor

Brand mentions summarised daily, with anything negative flagged the moment it appears.

Inbox assistant

Labelled, replied to, and scheduled in your tone — with a human gate before anything sends.

Ad spend recap

Friday spend against plan, on one page, in the CMO’s inbox without anyone building the deck.

QBR report

Usage, support volume, and expansion signals per account, compiled into a ready-to-send review.

Churn watcher

At-risk accounts spotted from usage and tickets, handed to CS as a weekly intervention list.

Onboarding agent

Every signup gets the welcome sequence, the in-app nudges, and an intro to their CS owner.

Or describe your own

Say what you want to happen once. DeskFerry builds it, wires it, and runs it from then on.

Whether you build an AI agent from scratch or start from a pre-built template, the level of control is yours. Most teams take a template and change the trigger, the tone, or a step — but every custom agent on the platform is built with the same tools, so there is no ceiling on what you can ship.

Setup & control

Running in under 30 minutes. In your hands the whole time.

Sign up, connect the first few apps, and ask for something real — that is the whole rollout, whether you are one person or five hundred. Every run is logged, and agents ask before they act.

You approve the actions

Put a gate on any step. The agent drafts, queues, and waits — nothing sends, charges, or writes back until you approve it.

Credentials stay scoped

DeskFerry requests access only for the tools an agent actually uses, and each connection is yours to revoke in one click.

Every run is logged

Open any run to see what the agent read, what it decided, and what it did — step by step, with the output it produced.

See pricing

Integrations

Connected to the 1,500+ apps you already use.

DeskFerry picks the connectors an agent needs from the catalog — CRM, inbox, calendar, billing, docs, support, analytics — and asks for credentials on those alone.

Notion
OpenAI (ChatGPT)
Anthropic (Claude)
Google Sheets
Telegram
Google Drive
HTTP / Webhook
Google Calendar
Schedule
Pipedream Utils
Shopify
Supabase
MySQL
PostgreSQL
AWS
Twilio SendGrid
Amazon SES
Klaviyo
Zendesk
ServiceNow
Slack
Microsoft Teams
Salesforce
HubSpot
Zoho CRM
Stripe
WooCommerce
Snowflake
MongoDB
Pinterest

And the rest of the 1,500+ catalog

Browse all integrations

Tool landscape

DeskFerry vs Zapier vs Make vs n8n.

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

FeatureDeskFerryZapierMaken8n
AI agents (autonomous, goal-based)Yes — nativeNo (rules only)No (rules only)Yes — AI Agent node, you wire it
How you buildDescribe it in a sentencePick trigger, then actionsDraw the scenarioWire nodes, often with code
No-code builderYesYesYesPartial — needs setup
Integrations1,500+6,000+1,800+400+
Pricing modelFlat monthly plan, unlimited agentsPer taskPer operationSelf-host / per seat
Best forAutonomous multi-step agentsSimple app-to-app triggersComplex branched workflowsDevelopers self-hosting

If your workflow is genuinely a linear trigger-to-action sequence — a form submission creating a CRM record, a Stripe payment posting to Slack — Zapier is often the simplest choice. If you need heavy branching with deterministic rules, Make and n8n are stronger. DeskFerry’s sweet spot is the work in between: tasks where the agent has to decide what to do next from context, read unstructured input like emails and documents, handle exceptions gracefully, and move across several apps in a single run. That is the agent-shaped work.

Describe it. We’ll build it.

Write one sentence about the agent you wish existed. DeskFerry assembles it, wires the tools, and hands it back ready to deploy. Start free.

Need a custom build? See our custom AI solutions.

FAQ

Frequently asked.

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. DeskFerry's AI agent builder is genuinely no-code. You describe your agent in plain English, connect apps by clicking through OAuth flows, and refine the agent by telling it what to change. 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. Teams that need more can call external APIs through webhooks and custom HTTP actions, but it's optional. The vast majority of DeskFerry 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.

Describe the job in plain English, for example "reply to new inbound leads within five minutes and log them in HubSpot". DeskFerry turns that into an agent, asks you to connect the apps it needs, and lets you set which actions need your approval. Run it once as a test, read the run history, and tell it what to change. You can also start from one of 200+ templates in the marketplace.

No. DeskFerry's builder is prompt-based: you describe what the agent should do and refine it by telling it what to change, instead of wiring boxes and arrows on a canvas. Every run is recorded in run history, so you can still see each step the agent took.

DeskFerry 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.

DeskFerry starts at $49/month for individuals and small teams, with Pro at $99/month and Scale at $299/month for bigger teams. There's a 7-day 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.