AI · Updated Apr 6, 2026 · ~8–12 min

Make AI agents (2026)

Make’s AI modules and agents can be a superpower — if you use them where they’re strong:

• understanding unstructured text,
• extracting fields,
• summarizing and routing,
• drafting content.

They are risky when you use them as the single source of truth for deterministic decisions.

1) When AI agents make sense

Good fits:

• classify inbound tickets (billing / bug / sales),
• extract entities (name, email, order ID),
• summarize long threads,
• route work to the right pipeline.

Bad fits (without guardrails):

• deleting data,
• approving payments,
• compliance decisions without validation.

2) Reliable patterns

Use these patterns to make AI workflows stable:

• Structured output: force JSON, then validate fields and types.
• Allowlists: only allow categories you define.
• Human-in-the-loop: for low confidence / high impact actions.
• Fallback path: rules-based default if AI fails or times out.

3) Cost & latency

In practice, AI adds:

• extra latency per run,
• extra failure modes (timeouts, rate limits),
• extra cost (per call).

To keep it under control:

• batch requests (process 10 items at once when possible),
• avoid re-processing duplicates,
• only call AI when simple rules can’t solve it.

4) Safety & compliance (EU / US)

Treat AI calls like sending data to a third party:

• minimize data (send only what’s needed),
• redact/avoid sensitive fields when possible,
• log safely (no secrets/PII in run logs),
• keep an audit trail for important decisions.

5) Checklist

• Define allowed outputs (schema + allowlists)
• Validate and handle “unknown” safely
• Add a fallback path
• Monitor accuracy and drift
• Budget operations + AI calls per month