Use case Last updated: October 15, 2026

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#1

n8n

Winner for technical builders — a visual workflow engine with a native AI Agent node, 400+ integrations, code steps, and a self-host option.

Best for

Agents that live inside real business workflows: triggers, webhooks, CRMs, databases, and any LLM provider, with JavaScript/Python when you need it.

Pricing (approx.): Self-hosted Community edition free; Cloud from ~€20/mo (execution-based) · check vendor for current pricing

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Worked use case Inbox triage agent that drafts replies and logs leads

  1. Start from a Gmail trigger node that fires on new messages.
  2. Add an AI Agent node with your model of choice and a system prompt describing triage rules.
  3. Give the agent tools: a Google Sheets "append lead" tool and an HTTP tool for your CRM.
  4. Add a memory node so it keeps per-thread context.
  5. Route "needs reply" outputs to a Gmail "create draft" node — never auto-send.
  6. Test with pinned sample emails and inspect each tool call in the execution log.
  7. Activate the workflow and review the drafts daily for the first week.

Best for teams already on the OpenAI platform — a visual canvas for multi-agent workflows with guardrails, evals, and embeddable ChatKit UI.

Best for

Customer-facing chat agents and internal copilots built on OpenAI models, where you want versioned workflows, built-in evals, and an export to the Agents SDK.

Pricing (approx.): No separate seat fee; pay standard OpenAI API usage · check vendor for current pricing

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Worked use case Support agent with a knowledge base and escalation guardrail

  1. Create a new workflow in Agent Builder and start from the customer-support template.
  2. Attach your help docs with file search so answers stay grounded.
  3. Add a guardrail node to catch PII and off-topic requests.
  4. Add a classifier branch: billing questions go to a human-handoff node.
  5. Preview the agent and run evals on a set of real past tickets.
  6. Publish and embed it with ChatKit on your help page.
  7. Export to the Agents SDK when you need custom code.

Best no-code "AI workforce" for go-to-market teams — prebuilt sales and research agents you can configure without writing code.

Best for

Sales, marketing, and ops teams that want an AI SDR or research agent running on a schedule, with human approval steps.

Pricing (approx.): Free tier; paid plans are credit-based · check vendor for current pricing

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Worked use case Lead research agent that enriches and qualifies inbound signups

  1. Create an agent from the lead-research template.
  2. Connect the input: new rows in your CRM or a webhook from your signup form.
  3. Give it tools: web search, LinkedIn/company lookup, and a CRM update tool.
  4. Write qualification rules (company size, role, use case) in the agent instructions.
  5. Turn on approval mode so a human reviews each CRM write at first.
  6. Run on 20 past leads and compare its scores to your own.
  7. Switch to autonomous mode once accuracy is acceptable.
#4

Make

Best visual automation platform for no-code teams — drag-and-drop scenarios across thousands of app integrations, with AI modules and agents layered on top.

Best for

Non-developers automating workflows across SaaS apps (CRM, email, sheets, Slack) who want to add AI steps without touching code, and who like seeing every branch on one visual canvas.

Pricing (approx.): Free plan with a monthly operations allowance; paid plans from ~$9–11/mo, billed by operations/credits · check vendor for current pricing

Try Make → (affiliate link)

Worked use case Form-to-CRM lead router with an AI summary

  1. Create a new scenario and add your form tool (Typeform, Google Forms, or a webhook) as the trigger.
  2. Add an AI module that summarizes the submission and tags intent (sales, support, partnership).
  3. Add a router with one path per tag.
  4. On the sales path, create or update the contact in your CRM and post the summary to Slack.
  5. On the support path, open a ticket in your helpdesk instead.
  6. Run once with sample data and inspect each module's input and output bubbles.
  7. Schedule it and check operation usage after the first week.

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