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Your agent responds to natural language — research questions, commands, follow-ups, and instructions all work. Here are the patterns that get the best results.

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Research queries

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Be specific about the topic and angle

“What are indie hackers saying about building in public? What strategies are working and what are the common mistakes?”

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Ask for comparisons

“How do developers compare Supabase vs Firebase for new projects? What are the trade-offs people mention most?”

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Request specific formats

“Summarize the top 5 complaints about Slack from Reddit, with links to the original posts”

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Target specific communities

“What are the most discussed topics in r/SaaS and r/startups this week?”

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Command patterns

Beyond research, your agent handles many types of instructions:
PatternExample
Save a link”Save this for me: [URL]“
Add to tasks”Add to my task list: review competitor pricing”
Draft content”Draft a LinkedIn post about AI agent tools”
File to workspace”Save all of this to the workspace — create a research file and add bookmarks”
Update files”Add this to our competitive research”
Scrape and analyze”Scrape their website and see how they position themselves”
Create a skill”Create a skill so you know how to convert markdown to Google Docs”
You don’t need specific syntax. Natural language like “remind me to…” or “I need to track…” works just as well as explicit commands.

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Multi-session patterns

Your agent remembers everything in its workspace. Use that continuity: 
Add this to our OpenClaw research

Can you find the competitors we saved last week?

What links do we have related to AI agent frameworks?

Can you track down Dialog related research files?
Referencing past work naturally (“our research”, “the competitors we saved”) tells your agent to search its workspace and build on previous findings.

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Iteration patterns

Dialog is built for back-and-forth. Iterate on results: 
I like this one but it needs to skew more towards the original angle

Give me 5 more options, each with a different approach

OK let's move forward with option F

No em dashes please
Your agent maintains full context within a session, so each message builds on the last. Don’t re-explain — just give direction.

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Follow-up strategies

  1. Start broad, then narrow — Begin with an overview question, then drill into specifics
  2. Shift angles — Ask about sentiment, then pricing, then features
  3. Compare and contrast — After researching one topic, ask how it compares to alternatives
  4. Request different formats — Ask for a summary table, bullet points, or a different structure
  5. Save what matters — When you find something useful, tell your agent to save it to the workspace

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Common research tasks

TaskExample query
Competitive intel”What are users saying about [competitor]‘s latest update?”
Idea validation”Are people looking for a tool that does [your idea]?”
Pricing research”How do [category] tools price their products? What do users think is fair?”
Feature prioritization”What features do people wish [product category] tools had?”
Market sizing”How active are the communities around [topic]? What’s the engagement like?”
Customer pain points”What are the biggest frustrations with [existing solution]?”
Comprehensive report”Build a comprehensive competitive report on [company]. Use all available tools.”
Tool discovery”Can you track down the best tools for [task]? Use Reddit and web search.”