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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: 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:
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:
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