AI Agent vs Zapier/Make in 2026: The Comparison That's Actually Still True

Posted on Oct 15 • Originally published at zahidhasantonmoy.vercel.app

"I already built this in Zapier, why would I need an agent?" A year ago the answer was easy. Not anymore — Zapier shipped AI Orchestration in July 2026, Make shipped AI Agents in its canvas earlier the same year. "Does a model decide something" stopped being the dividing line.

This post continues my series on building agents. The previous one covered writing a good agent prompt.

A trigger-action platform's workflow shape — which apps, roughly what order — is drawn by a human ahead of time in the builder, even when one step now delegates to a model. An agent you build yourself, the way this series has, decides that whole shape at runtime: which tools apply, in what order, for this specific message.

flowchart TD subgraph Fixed automation F0[Any trigger] --> F1[Step A, always] --> F2[Step B, always] end subgraph Agent A0[Any message] --> AD{Model reads it} AD -->|only A applies| A1[Step A] AD -->|only B applies| A2[Step B] AD -->|both apply| A3[Step A and Step B] AD -->|neither applies| A4[No tool, just reply] end Proving it in code // agent/fixed-vs-agent.ts import Anthropic from ' @anthropic-ai/sdk '; const client = new Anthropic (); const expenses: { item: string; amount: number }[] = []; const log: string [] = []; function addExpense ( item: string, amount: number ) { expenses. push ({ item, amount }); return { ok: true, recorded: expenses. length }; } function getTotal () { return { total: expenses. reduce (( sum, e ) => sum + e. amount, 0 ) }; } function notify ( message: string ) { log. push ( message ); return { sent: true }; } function runFixedAutomation ( item: string, amount: number ) { const step1 = addExpense ( item, amount ); const step2 = notify ( `Recorded: ${ item } ( ${ amount } )` ); return { step1, step2 }; } const tools: Anthropic. Tool [] = [ { name: ' add_expense ', description: ' Record one expense. Use it whenever the user says they spent money. ', input_schema: { type: ' object ', properties: { item: { type: ' string ' }, amount: { type: ' number ' } }, required: [ ' item ', ' amount ' ], }, }, { name: ' get_total ', description: " Return the exact sum of every expense recorded so far.

Use this whenever the user asks for a total, a sum, or how much they've spent overall. ", input_schema: { type: ' object ', properties: {} }, }, { name: ' notify ', description: ' Send the user a short notification. Use this only when the user explicitly asks to be notified or reminded. ', input_schema: { type: ' object ', properties: { message: { type: ' string ' } }, required: [ ' message ' ], }, }, ]; function execute ( name: string, input: Record < string, unknown > ): unknown { if ( name === ' add_expense ' ) return addExpense ( String ( input. item ), Number ( input. amount )); if ( name === ' get_total ' ) return getTotal (); if ( name === ' notify ' ) return notify ( String ( input. message )); return { error: ' unknown tool ' }; } async function runAgent ( userText: string ): Promise < string > { const messages: Anthropic.

MessageParam [] = [{ role: ' user ', content: userText }]; for ( let step = 0; step < 4; step ++ ) { const response = await client. messages. create ({ model: ' claude-sonnet-5 ', max_tokens: 300, tools, messages, }); messages. push ({ role: ' assistant ', content: response. content }); if ( response. stop_reason!== ' tool_use ' ) { const block = response. content. find (( b ): b is Anthropic. TextBlock => b. type === ' text ' ); return block?. text?? ''; } const results: Anthropic. ToolResultBlockParam [] = []; for ( const block of response. content ) { if ( block. type === ' tool_use ' ) { results. push ({ type: ' tool_result ', tool_use_id: block. id, content: JSON. stringify ( execute ( block. name, block. input as Record < string, unknown > )), }); } } messages. push ({ role: ' user ', content: results }); } return ' Stopped: step limit reached '; } runAgent ( ' I spent 500 on lunch. ' ). then ( console. log ); Enter fullscreen mode Exit fullscreen mode runFixedAutomation always runs both steps, regardless of content. runAgent, same three tools, picks a different path for "I spent 500 on lunch" (just add_expense ), "what's my total" (just get_total ), and "I spent 200 on tea, notify me" (both) — decided by the model each time, not written into the function.

The first draft argued no-code tools can't reason, full stop. Checking current reality before publishing caught that it was already wrong — both platforms shipped real agent features this year. The fix was rebuilding the comparison around what's still true, not whether a model is involved at all.

I write each post in English and Bangla. The full version is on my blog: AI Agent vs Zapier/Make

Where's your line between "use the platform" and "build it yourself"?