How to Chain Multi-Step AI Workflows in Google Opal

A one-block Opal app is a wrapper around a prompt. The interesting work starts when you chain several steps — transcribe, then summarize, then extract action items — and decide what happens when a step returns junk.

Direct answer: Opal supports multi-step workflows where the output of one block feeds the next; you build them by connecting blocks in sequence and passing named values forward, but there’s no custom code, so error handling is limited to what the visual builder offers.

How do I pass data between steps?

Give each block’s output a clear name and reference it in the next prompt, like “Using the summary from the previous step, extract every deadline mentioned.” Opal keeps the values in scope for downstream blocks. Vague naming (“output1”) makes a five-step workflow unmaintainable within a week.

What’s a good multi-step example?

1. Input: an uploaded meeting recording or transcript.
2. Step 1: summarize the discussion in five bullets.
3. Step 2: from the summary, list decisions made.
4. Step 3: from the decisions, draft action items with owners.
5. Output: a single formatted brief.

Each step is cheap to reason about. That beats one monster prompt that tries to do all four jobs and drifts.

Can I branch on a condition?

To a degree. You can structure steps so a classification block routes text down different paths — “Is this a complaint or a feature request?” and a following step handles each. Full boolean logic, loops, and custom functions aren’t available, so complex branching gets awkward fast. If you find yourself wanting an if statement, you’ve hit the ceiling of a no-code builder.

How do I make outputs reliable?

Constrain format at every step, not just the last. Ask for exactly three bullets, or a specific field structure, and validate by eye in preview. Small models degrade over long chains — by step four the output can wander. Keeping each step narrow and format-tight is what keeps a chain coherent.

What breaks in longer workflows?

Two failure modes stand out. Silent drift, where step two quietly misreads step one, and you only notice at the final output. And the no-code wall, where a task that needs a loop or an external API simply can’t be expressed. Both push you toward splitting a sprawling workflow into two smaller apps that each stay readable.

When should I move off Opal?

The moment you need database reads, third-party API calls, user accounts, or deterministic logic. At that point a low-code platform or a small scripted backend is the honest answer. Opal is great for prototyping the shape of an idea — not for the production version of it.

Key Takeaways

• Chain Opal blocks by naming outputs and referencing them in later prompts.
• Constrain format at every step to prevent drift over long chains.
• Simple classification branching works; loops and custom logic don’t exist.
• Split sprawling workflows into smaller apps to stay maintainable.
• Move to code once you need APIs, databases, or user accounts.