This workflow powers a deep research assistant that takes a user-defined topic and performs iterative cycles of web research and summarization. At each cycle, it generates a search query, gathers online sources, summarizes them, and reflects to identify gaps. The cycle repeats for a configurable number of times before producing a final markdown summary with citations. It is useful for writing comprehensive briefs, conducting market research, or understanding complex topics in depth.
You care about improving the entire lifecycle of buyer and customer journeys. And you want inspiration for areas where Ai may help.
You want inspiration for real-world ways to help people as you're building your Ai product.
You're focused on the stage in your daily job and want to find ways for Ai to help you do more with less.
Does this AI agent workflow rely too much on AI and not enough on human know-how? Or the reverse? Is it missing steps or tools?
Note that this Ai workflow is presented as inspiration for what's possible. Adjust the amount, type and quality of the data inputs. Adjust how much or how little your human team mates (or you), AI and fully autonomous agents contribute.
And test it! Learn what works and what doesn't.
Don't forget! In the end, it's not just about efficiency. It's about delivering great experiences for your customers and customers-to-be.
Generate a concise, relevant search query to explore this research topic: {researchTopic}
Summarize the key points from the following sources related to {researchTopic}. Include source references.
Sources:
{webSources}
Based on this summary of {researchTopic}, determine if there are any significant knowledge gaps. If gaps exist, generate a revised search query to fill them. If the summary is sufficient, indicate that no further search is needed.
Summary:
{currentSummary}
Combine all findings from multiple research cycles into a single, structured markdown summary. Include clear citations from all sources used.
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