Build a Research Agent Stack That Does the Work You Hate

Most solo operators spend hours each week doing research that a well-configured agent stack could handle in minutes. Competitive analysis, newsletter curation, customer feedback synthesis, market monitoring - these are all structured, repeatable tasks that follow predictable patterns.

This guide walks through how to build a research agent stack from scratch: what components you need, how to wire them together, and where people usually go wrong.

What a Research Agent Stack Actually Does

A research agent stack is a set of automated processes that handle the full arc of an information task:

1. Trigger - something kicks the process off (a schedule, a webhook, a user prompt)
2. Gather - agents pull raw information from sources
3. Filter - irrelevant or low-quality results get dropped
4. Synthesize - the remaining content gets summarized or structured
5. Deliver - the output lands somewhere useful (your inbox, a Notion page, a Slack channel, a database)

Each of these steps can be handled by a different tool or model. The key is treating the whole thing as a pipeline with clear inputs and outputs at each stage, not as one monolithic prompt.

The Minimal Viable Stack

You don't need a complex infrastructure setup to get started. Here's a stack that works well for most solo operators:

Trigger layer: A simple cron job (built into most hosting platforms) or a tool like n8n, which lets you schedule workflows without writing much code.

Gather layer: A web search API (Exa, Tavily, or Serper) paired with a headless browser tool like Playwright for pages that require JavaScript rendering. For structured sources, RSS feeds are underrated - they're reliable and free.

Filter layer: A fast, cheap model (GPT-4o mini or Claude Haiku) that scores each result for relevance against a rubric you define. This is where you prune the 80% of results that don't matter.

Synthesize layer: A more capable model handles the final summary or report. This is where you spend slightly more per token because quality matters.

Deliver layer: A webhook to your tool of choice. Notion, Linear, email, Discord - pick wherever you actually look.

Defining Your Relevance Rubric

The filter step is where most pipelines fall apart. People skip it or use a vague prompt like "is this relevant?" and then wonder why the output is noisy.

A good relevance rubric is specific and scored. For example, if you're monitoring AI tools for a weekly newsletter:

Score this article from 0-10 on the following criteria:
- Published in the last 7 days (0 or 1)
- Covers a tool, technique, or case study (not opinion/news recap) (0-3)
- Includes specific implementation details or data (0-3)
- Relevant to solo operators or small teams (0-3)

Return a JSON object with scores for each criterion and a total.

Any result under 6 gets dropped. This takes about 200 tokens per article and saves the downstream synthesis step from processing garbage.

Structuring the Synthesis Prompt

The synthesis step should produce output in a format you've pre-defined. Freeform summaries are hard to use consistently. Instead, define a schema:

For each article that passed the filter, produce:
- title: the article title
- source: domain name only
- one_line: a single sentence describing what the piece covers
- key_insight: the most useful specific fact, technique, or data point
- relevance: one sentence on why this matters to solo AI builders

Return as a JSON array.

This output is easy to pipe into a template, a database, or a frontend component without additional processing.

Handling Failures Gracefully

Agent pipelines fail. Sources go down, APIs rate-limit you, models return malformed JSON. Build failure handling in from the start:

• Wrap each step in a try/catch with logging
• Store raw gathered data before filtering, so you can replay the pipeline without re-fetching
• Set a timeout on each step - a stuck gather step shouldn't block delivery
• Send a simple alert (email or Discord webhook) when a run fails, with the step name and error

Five minutes of error handling setup saves hours of debugging later.

When to Add a Human in the Loop

Full automation is not always the goal. For high-stakes research - competitive intelligence, due diligence, anything that influences a major decision - add a review step before the output gets used.

This can be as simple as delivering to a staging document that you approve with one click before it publishes or sends. The agent does 90% of the work; you spend two minutes reviewing rather than two hours doing it from scratch.

Scaling the Stack

Once the basic pipeline is working, you can extend it in a few directions:

• Multiple input sources with source-specific gathering logic
• Categorization that routes different content types to different outputs
• Memory that tracks what you've already covered and filters out repetition
• Feedback loops where you rate outputs and the filter rubric adjusts over time

Start with the minimal version. Get one full pipeline running end-to-end, delivering useful output on a real schedule. Then extend it based on what's actually missing, not what sounds useful in theory.

The goal is a system that handles the collection and organization work automatically, so your time goes toward the analysis and decisions that actually require your judgment.