Learn how to build Opal agent workflows to automate work with fewer clicks. Use the new agent step to pick tools, ask for details, remember preferences, and route tasks. This guide shows setup, memory, routing rules, and chat prompts, plus examples you can copy for design, briefs, and stories. Opal moved from one-off model calls to goal-driven agents. You no longer choose a single model and hope for the best. You add an agent in the generate step, state the goal, and let the agent plan the path. It can search the web, call creative models like Veo for video, ask follow-up questions, and remember what matters for the next run. This change turns flat flows into two-way work. A Room Styler Opal can now talk with you, test ideas, and refine choices until the output fits your taste. A briefing Opal can route you down one path for new clients and a different path for existing ones. The result is faster delivery with less manual wiring.

What’s new: from static flows to thinking agents

Static flows felt like forms: you uploaded a file, entered a style, and received a result. If the answer missed the mark, you started over. With the agent step, Opal can now:

• Plan: It reads your goal and picks the next best action.
• Use tools: It calls Web Search for research or Veo for video without you switching steps.
• Ask: It opens a short chat to fill gaps or confirm choices.
• Remember: It stores useful facts across sessions, like your brand voice or color palette.
• Route: It follows rules you set to move to the right step for the situation.

This gives you speed and control. The agent handles the heavy lifting, while your workflow still has clear steps you can edit anytime.

How to build Opal agent workflows that deliver results

You can go from idea to working agent in minutes. Follow these steps to design a plan that is fast, safe, and reliable.

1. Define the user goal and the success output

Start with a single sentence: “Given X inputs, produce Y output that meets Z criteria.” Keep it simple and testable.

• Goal: “Turn a room photo and a style into three design variations with item lists.”
• Output: “Three images, each with a short caption, and a link to sources.”
• Guardrails: “Ask for missing info. Do not invent product links. If unsure, say so.”

Place this in the agent step instructions so the plan stays focused.

2. Map the plan into steps the agent can control

Even smart agents do better with clear stages. A simple map could be:

• Gather: Ask for inputs and clarify constraints.
• Research or create: Use tools and models to produce drafts.
• Review: Summarize choices and confirm with the user.
• Finalize: Deliver the result in the agreed format.

Add the agent in the generate step so it can choose tools and route to the next step when ready.

3. Add memory: what to store, when to store it, and how to use it

Good memory makes repeat work instant. Decide which facts are worth keeping and for how long.

• What: Brand voice, style preferences, target audience, budget ranges, common file formats.
• When: Save only after the user confirms. Avoid writing partial guesses.
• How: Use clear keys like “brand_voice,” “preferred_colors,” “meeting_context.”
• Privacy: Do not store sensitive data unless the user agrees. Set retention windows.

Example: In a Video Hooks Brainstormer Opal, store “brand_voice=playful, audience=first-time buyers.” On the next run, the agent instantly proposes hooks that match this voice.

4. Configure dynamic routing rules for smart paths

Routing rules keep your agent on the best track for each case. Describe plain-language conditions and the next step.

• If the user is meeting a new client, go to “Research” and run Web Search on the company and industry.
• If it is an existing client, pull internal notes and skip the public search.
• If no room photo is provided, stay in “Gather” and request a photo or floor plan.

In an Executive Briefing Opal, these rules save time and reduce noise by fetching only the context that matters.

5. Enable interactive chat to fill gaps fast

Let the agent ask short, specific questions instead of guessing.

• Ask one thing at a time.
• Offer examples or choices the user can tap.
• Stop asking when you have enough to proceed.

In the Room Styler Opal, the agent might say, “Do you prefer walnut, oak, or teak wood tones?” A clear choice speeds the next image round and keeps the style consistent.

6. Choose and authorize tools and models

Agents are powerful when they can reach the right tools.

• Web Search: Use it for facts, names, and trends. Summarize sources and link back.
• Veo (video): Use it to draft short video clips or storyboards from prompts.
• Image and text models: Use them to create and refine designs, hooks, or briefs.
• Internal data: Connect calendars, notes, or docs if your policy allows it.

Give the agent the least access it needs. Log tool calls so you can audit and improve them later.

7. Write agent instructions and guardrails

Good instructions prevent drift, cut costs, and improve trust.

• Tone: “Clear, friendly, and concise.”
• Budget: “Aim for under N tool calls. If extra calls are needed, ask first.”
• Safety: “Do not claim facts without sources. Mark uncertain items.”
• Decision rules: “If confidence is low, ask a question. If time runs out, return a partial result plus next steps.”

Keep instructions short. The agent should not fight a wall of text.

8. Prototype, test, and iterate

Run the agent on five to ten sample cases. Watch where it hesitates or over-queries.

• Trace the plan: Check which steps fire and why.
• Measure time and cost: Note tool calls and average run time.
• Review outputs: Compare against the success criteria you wrote in step 1.
• Tighten or relax: Add a rule if the agent wanders. Remove a rule if it gets stuck.

Real examples you can build today

Room Styler Opal

Goal: Turn a living room photo and a mid-century modern brief into three design options.

• Ask for a room photo, preferred wood tone, and budget level.
• Generate first images. Show a one-line note on what changed.
• Collect quick feedback: keep, tweak, or replace items.
• Store color and material likes in memory for the next session.

Why it works: The agent runs a short ask-act loop, learns user taste, and avoids repeating questions.

Visual Storyteller Opal

Goal: Build a short story draft with images and a plot line that fits a theme.

• Plan the beats: intro, tension, twist, ending.
• Ask for missing details like genre, setting, or narrator voice.
• Offer three plot point options per beat.
• Route to image creation after each approved beat.

Why it works: The agent proposes decisions, not walls of text, and routes to creation only when a beat is locked.

Video Hooks Brainstormer Opal

Goal: Produce ten hook lines for a 15-second product video.

• Save the brand voice and target audience in memory.
• Generate hooks in three angles: problem, benefit, and proof.
• Ask for a favorite angle, then expand that set.
• Export to a notes doc with categories and next steps.

Why it works: Memory reduces repeat questions. Structured angles keep ideas fresh.

Executive Briefing Opal

Goal: Prepare a one-page brief for a client meeting.

• If the client is new, search the web for company news and key people.
• If the client is existing, fetch internal notes and recent issues.
• Summarize risks, goals, and two talking points.
• Ask the user to confirm the meeting objective, then lock the draft.

Why it works: Dynamic routing brings only the context you need, which cuts noise and saves time.

Design patterns for reliable agents

You can boost quality with simple, repeatable patterns:

• Ask-Act-Verify: Ask one key question, act on the answer, verify the result fits the goal.
• Progressive detail: Start broad, then zoom in. Do not ask for ten specs at once.
• Deterministic checkpoints: Freeze decisions at milestones so the agent does not backtrack.
• Fallbacks and timeouts: If a tool fails, try a backup or return a partial with next steps.
• Cost-aware planning: Cap tool calls per run. Have the agent ask before expensive steps.
• Consent for memory: Confirm what to save, show how to clear it, and set retention limits.
• Source-first facts: Link to sources for claims. Mark items as “needs review” when unsure.

Metrics and observability

Track a few simple metrics to prove that your agent helps users:

• Success rate: The share of runs that meet the output criteria.
• Time to result: Average minutes from start to final output.
• Path coverage: Which routes get used and which stay idle.
• Memory hits: How often saved data removes a question.
• Tool errors and retries: Where the agent stumbles.
• User edits: How much users change the final output.

Use these signals to trim steps, tune prompts, and refine routing rules.

Security, privacy, and compliance basics

Responsible automation builds trust. Keep these rules:

• Collect only what you need. Avoid sensitive fields unless the user agrees.
• Set retention periods and show users how to clear memory.
• Limit tool scopes and log access. Rotate keys and review permissions.
• Respect copyright. Link to sources. Do not pass private data to public tools.
• Display disclaimers for uncertain content like market news or prices.

Troubleshooting and optimization tips

If the agent loops on questions

• Reduce open-ended prompts. Offer choices.
• Set a maximum of two follow-ups before moving on.
• Save answers in memory to avoid asking again.

If outputs feel generic

• Add concrete success criteria and style constraints in the instructions.
• Use examples: “Write like this sample, not like that sample.”
• Ask the user to rank options, then focus on the top one.

If routing picks the wrong path

• Make conditions explicit: “If field X equals Y, then go to Step Z.”
• Log which rule fired. Review real runs to spot edge cases.
• Add a short confirmation message before switching paths.

If tool costs spike

• Cache recent searches for the same client or topic.
• Batch similar actions in one step.
• Ask before long or high-cost calls.

Putting it all together

If you want a fast start, begin with a single use case you run every week. Write a one-sentence goal, add the agent in the generate step, and give it permission to ask two follow-up questions. Add one memory key and one routing rule. Ship it, watch real runs, and then add features. This simple approach will show you how to build Opal agent workflows that save time from day one. Agents in Opal make work feel like a guided conversation with a smart partner. You keep control with clear steps, while the agent plans, searches, asks, and remembers. With the patterns and examples above, you now know how to build Opal agent workflows that move faster, cut errors, and scale with your needs.

(Source: https://blog.google/innovation-and-ai/models-and-research/google-labs/opal-agent/)

For more news: Click Here

FAQ