swarm-research

name	swarm-research
description	Two-phase research pattern: (1) quiz user to build structured selection criteria, (2) dispatch parallel agents across multiple models with intentionally varied context levels, then compile/rank/filter into a single report. Use when: (1) user asks to "find me a..." or "help me choose..." with multiple viable options, (2) group decisions with multiple constraints, (3) any scenario where breadth of search + structured evaluation both matter — travel planning, restaurant selection, apartment hunting, tech stack decisions, gear purchasing, event venue selection. Key insight: some agents get full criteria (targeted search) while others get minimal context (wider net, avoids confirmation bias). Different models surface different results.
author	Claude Code

Swarm Research

A two-phase pattern for decision research that combines structured criteria elicitation with multi-agent parallel search, then converges into a ranked, filtered report.

Problem

When users ask for help choosing something (a backpacking spot, a restaurant, an apartment, a tech stack), they typically under-specify what they actually need. And a single search agent — no matter how good — has blind spots shaped by its initial framing.

The result: generic recommendations that miss non-obvious constraints, or narrowly-targeted results that miss great options outside the search frame.

Context / Trigger Conditions

Use this pattern when ALL of these are true:

• Multiple viable options exist (not a single right answer)
• Multiple criteria matter (not just "the best one")
• The user hasn't fully articulated their constraints
• Breadth of search adds value (different sources/angles find different things)

Common triggers:

• "Find me a backpacking spot / restaurant / apartment / gift / venue"
• "Help me choose between..." (but they haven't defined "choose by what")
• Group decisions with diverse preferences
• Any "research and recommend" task

Solution

Phase 1: Criteria Elicitation

Don't start researching. Start quizzing.

The goal is to extract a structured criteria document BEFORE any search happens. This is the single biggest quality lever — it turns vague preferences into a scoring rubric.

How to quiz:

1. Ask rapid-fire questions in logical clusters (logistics, preferences, constraints, dealbreakers)
2. Use the ask_user tool with multiple-choice options where possible (faster for the user)
3. Probe for NON-OBVIOUS constraints — these are the ones that matter most:
   • Safety requirements that the user assumes are obvious
   • Group dynamics (not just the requester's preferences)
   • Hard dealbreakers vs. nice-to-haves (users often conflate these)
   • Practical filters (timing, budget, accessibility, logistics)
4. Reflect the criteria back as a structured document for user confirmation
5. Ask "anything to add, drop, or adjust?" before proceeding

Criteria document structure:

## Must-Haves (Non-Negotiable)

- [Hard requirements that eliminate options if not met]

## Strong Preferences

- [Things that significantly improve the experience but aren't dealbreakers]

## Practical Filters

- [Logistics, timing, budget, accessibility constraints]

## Special Considerations

- [Context-specific factors — group dynamics, safety, etc.]

Key principle: The criteria doc becomes the scoring rubric for Phase 2. Time invested here pays off 10x in research quality.

Phase 2: Divergent Multi-Agent Search

Dispatch 4-6 agents in parallel using the task tool with mode: "background". The critical technique is intentional context variation:

Full-context agents (2-3):

• Get the COMPLETE criteria document
• Use different models (e.g., opus-fast + codex + gemini)
• Produce targeted, criteria-matched results
• More likely to find the "right" answer

Minimal-context agents (2-3):

• Get only the basic ask (e.g., "find backpacking spots near Seattle, 4 people, 2 nights")
• NO criteria, NO constraints, NO special considerations
• Use different models from the full-context agents
• Cast a wider net — find options the targeted agents miss
• Surface things the user didn't know to ask for

Why this works:

• Full-context agents do confirmation search (find what matches)
• Minimal-context agents do exploration search (find what exists)
• Different models have genuinely different knowledge bases and search instincts
• The overlap between agents validates strong options (Shi Shi Beach was found by 4/5 agents)
• The non-overlap surfaces hidden gems or eliminates blind spots

Agent prompt template (full context):

You are researching [DOMAIN] for [USER CONTEXT].
Here are the COMPLETE selection criteria: [FULL CRITERIA DOC]

YOUR TASK:
1. Use web_search extensively to find 4-6 specific options in [SCOPE]
2. Use web_fetch to pull details from [RELEVANT SOURCES]
3. If you get 403 errors, DON'T STOP — try alternative URLs, different queries
4. For EACH option, provide: [STRUCTURED FIELDS]
5. [DOMAIN-SPECIFIC INSTRUCTIONS]

Return a structured report. Real links, real data.

Agent prompt template (minimal context):

Find [THING] for [BASIC CONTEXT]. [SIMPLE CONSTRAINTS].

Use web_search and web_fetch to research real options. If you hit 403 errors,
try alternative URLs. Find 4-6 options with: [BASIC FIELDS]

Return a structured report.

Model selection guidance:

• Use 3+ different models from different providers for maximum divergence
• Premium models (opus/codex/gemini pro) for full-context agents (better criteria matching)
• Faster models work fine for minimal-context agents (breadth over depth)
• If you have access to Playwright, mention it as a fallback for blocked sites

Phase 3: Convergent Compilation

Once all agents return:

1. Aggregate: Collect all unique options across all agents
2. Note consensus: Options found by multiple agents independently are strong signals
3. Score against criteria: Use the Phase 1 criteria doc as a rubric
   • Must-haves are pass/fail filters (eliminate if not met)
   • Strong preferences are weighted scores
   • Practical filters are additional pass/fail
   • Special considerations are risk flags
4. Rank: Order by criteria fit, noting where agents agreed/disagreed
5. Eliminate: Cut 2-4 options that don't meet must-haves or score poorly
6. Report: Produce a clean, readable report with:
   • Ranked options with details and links
   • Discarded options table (name, why cut, link) — still valuable for future reference
   • Clear recommendation with reasoning
   • Action items / next steps

Example Applications

Domain	Phase 1 Quiz Clusters	Full-Context Focus	Minimal-Context Focus
Backpacking	Group fitness, format, safety, scenery prefs, gear status	Match trail to specific constraints (fire, tides, terrain)	"Best backpacking near [city]"
Restaurant	Group size, dietary restrictions, vibe, budget, occasion	Match cuisine + ambiance + dietary needs	"Best restaurants in [area]"
Apartment	Commute, budget, lifestyle, dealbreakers, neighborhood vibe	Match specific neighborhood + amenity needs	"Apartments in [city] under $X"
Tech Stack	Team skills, performance needs, scale, timeline, constraints	Match framework to specific architecture needs	"Best framework for [type] app"
Gift	Recipient personality, interests, budget, occasion, relationship	Match gift to specific person profile	"Best gifts for [demographic]"

Notes

• Don't abandon slow agents. In practice, the slowest agent (16 min vs 2-3 min for others) found the most specific, bookable results — actual Hipcamp listings with direct links, fire restriction warnings other agents missed, and land-ownership gotchas. Speed ≠ quality in research. Wait for all agents before compiling, even if it feels slow.
• Agent count: 4-6 is the sweet spot. Fewer loses the diversity benefit. More creates diminishing returns and long wait times.
• 403 resilience: Always instruct agents not to stop on 403 errors. Web research agents will hit blocked sites frequently — they need to try alternative URLs, reformulate queries, or use browser tools.
• Playwright: If available, mention it in agent prompts as a fallback for sites that block simple fetches. Note that multiple agents can't share a single Playwright session simultaneously.
• Consensus scoring: If 4/5 agents independently find the same option, that's a strong signal regardless of how it scores on paper criteria. Note these in the report.
• The "minimal context surprise": The most valuable finds often come from minimal-context agents. They search without preconceptions and sometimes surface category-breaking options (e.g., a river valley option nobody asked for that turns out to be the safest choice).

Verification

The pattern is working when:

• Full-context agents return options that closely match criteria
• Minimal-context agents return at least 1-2 options the full-context agents missed
• The final report has a clear #1 recommendation with reasoning
• Discarded options are documented (not lost)
• The user can make a decision from the report without further research