research-agent

name	research-agent
description	Investigate topics deeply with cross-referenced sources and produce evidence-backed findings. Use when evaluating technologies before adoption, analyzing competitors, or investigating bug root causes across docs and issues.
domain	agents
tags	["agent","ai-agent","automation","orchestration","research"]

Research Agent

When to Use

Trigger phrases:

• "research agent"

• "Evaluating a technology, library, or framework before adoption"

• "Investigating a bug root cause across documentation, issues, and forums"

• "Competitive analysis of tools, products, or approaches"

• Evaluating a technology, library, or framework before adoption

• Investigating a bug root cause across documentation, issues, and forums

• Competitive analysis of tools, products, or approaches

• Building a technical recommendation backed by evidence

• Understanding an unfamiliar codebase or system architecture

• Researching API capabilities, rate limits, and edge cases

• Investigating security vulnerabilities or incidents

When NOT to Use

• When the task is simple enough for a single command
• When real-time human judgment is required
• When the agent lacks access to required tools or data

Overview

Research Agent is an AI agent skill for agent orchestration. It enables autonomous execution of complex tasks with minimal human intervention.

Capabilities

• Autonomous operation — Execute multi-step research agent workflows independently
• Context awareness — Adapt behavior based on current state and history
• Error recovery — Handle failures gracefully with retry and fallback logic
• Integration — Connect with external tools and services as needed

Workflow

# Example: Agent orchestration
from dataclasses import dataclass

@dataclass
class Task:
    name: str
    priority: int
    assigned_agent: str

def orchestrate(tasks: list[Task]) -> dict:
    results = {}
    for task in sorted(tasks, key=lambda t: t.priority):
        results[task.name] = execute(task)
    return results

1. Initialize — Set up the agent context and load required resources
2. Plan — Break down the task into executable steps
3. Execute — Run each step, monitoring for errors and adapting as needed
4. Verify — Validate results against acceptance criteria
5. Report — Summarize outcomes and suggest next steps

Configuration

• Define task objectives and constraints clearly
• Set appropriate timeout and retry limits
• Configure tool access and permissions
• Enable logging for debugging and audit

Anti-Rationalization

Rationalization	Reality
"I will just do it manually"	Agents automate repetitive tasks — manual work does not scale
"The agent will figure it out"	Without clear instructions, agents hallucinate. Give explicit context.
"One agent is enough"	Complex tasks benefit from specialized agents working in parallel

Process

1. Scope — Define research questions, identify data sources, set time boundaries
2. Gather — Collect data from primary sources, APIs, and public records
3. Synthesize — Analyze findings, identify patterns, produce actionable report

Verification

• All steps executed successfully
• Results validated against acceptance criteria
• Error handling tested with edge cases
• Documentation updated with findings