Part 1: Why Build a Research Agent

Research is one of the highest-value tasks in any business function. Understanding a market, investigating a company, analyzing a competitive landscape, evaluating a technology, these tasks produce the insights that drive decisions. They also consume enormous amounts of analyst time.

A typical company research request, "give me everything we need to know about [Company X] before our sales call", takes an experienced analyst 2-4 hours to complete well. They search LinkedIn for executive backgrounds. They read recent press releases and blog posts. They check Crunchbase for funding history. They review G2 for product perception. They scan the company's job postings for strategic signals. They read recent news coverage. They synthesize it all into a brief.

A multi-step research agent completes the same research in 60-90 seconds. Not a shallow version of the research, a comprehensive synthesis of the same sources, organized in the same format, available instantly.

The compounding value:

The value of a research agent compounds in two ways. First, it makes research-backed decisions accessible for questions that would never have generated a formal research request. A sales rep preparing for a 15-minute discovery call would not ask an analyst to spend 2 hours on company research, but they would run a 60-second agent query. This expands the use of research to decisions that previously relied on guesswork.

Second, it accelerates the decision cycle. Decisions that previously required 24-48 hours of research turnaround can now be made in the same meeting where the question arises. The strategic value of faster decisions compounds over hundreds of decisions per year.

What a research agent covers:

Company intelligence: founding date, headquarters, employee count, recent funding, key products and positioning, recent news.

Leadership intelligence: CEO and key executive backgrounds, recent LinkedIn activity, public statements and interviews.

Competitive position: how the company positions against competitors, customer reviews and sentiment, product strengths and weaknesses.

Strategic signals: recent job postings by department (signals investment areas), recent partnerships and integrations (signals ecosystem strategy), recent content and thought leadership (signals positioning evolution).

Financial signals: for public companies, recent earnings highlights, guidance, and analyst sentiment. For private companies, funding history, investor composition, and revenue estimates.

This synthesis, assembled from 8-10 sources, organized into a consistent format, available in 60 seconds, is what a research agent produces.

Part 2: How to Build the Multi-Step Research Agent

This agent accepts a company name or research topic as input, autonomously searches 8-10 sources, synthesizes findings using Claude, and delivers a structured research brief within 90 seconds.

The architecture:

Unlike simple single-step automations that call one API and summarize the result, a research agent executes multiple steps sequentially, where each step's output informs the next. This is what makes it "agentic", it plans and executes a research process, not just a single lookup.

Step 1: Company identification

The agent takes the company name as input and identifies the canonical company: official name, website, LinkedIn URL, Crunchbase ID. This disambiguation step ensures subsequent searches target the correct entity. "Microsoft" and "Microsoft Azure" are different search contexts.

Step 2: Structured data collection

The agent queries structured data sources in parallel:

Crunchbase API: funding history, investor names, founding date, employee count, headquarters, and recent news.

LinkedIn Company API (or scraping): employee count, recent company posts, recent leadership announcements, and job posting volume by department.

NewsAPI: all news mentions in the last 90 days, sorted by relevance.

Step 3: Unstructured content collection

The agent retrieves unstructured content sources:

Company website: about page, product pages, recent blog posts (via RSS if available).

G2 or Capterra: recent reviews, rating trends, and category positioning.

Job postings: current open positions by department, seniority level, and required skills, a rich signal of strategic investment.

Step 4: Synthesis

All collected content is passed to Claude with a synthesis prompt that instructs it to produce a structured brief in a consistent format. The prompt specifies the use case (sales preparation, competitive analysis, partnership evaluation, investment diligence) so the synthesis emphasizes the right signals for the context.

Step 5: Delivery

The brief is delivered via the channel configured at request time: Slack DM, email, Notion page, or Google Doc. The delivery includes all source links so the requester can dive deeper on any section.

The request interface:

The research agent is triggered via a Slack slash command: `/research [company name] [use case]`

Examples:

The use case parameter selects the appropriate synthesis prompt variant. A sales-prep brief emphasizes trigger events, leadership changes, and recent strategic announcements. A competitive-analysis brief emphasizes product positioning, customer sentiment, and market share signals. A partnership-eval brief emphasizes technical capabilities, customer overlap, and integration ecosystem.

The brief format:

[Company Name] Research Brief

Generated [date] · [use case] · Sources: [list]

At a Glance

Founded, headquarters, employees, funding, key products (3-4 bullet points)

Recent Developments (last 90 days)

The 3-5 most significant recent events: funding, leadership changes, product launches, partnerships, news coverage. Each with a one-sentence significance note.

Strategic Signals

What the company's recent actions suggest about their direction: hiring patterns, content themes, partnership strategy, product roadmap signals.

Customer Perception

G2/Capterra rating trend, top themes in positive reviews, top themes in negative reviews, recent review highlights.

Competitive Position

How the company positions against the market, key differentiators they claim, customer-reported strengths and weaknesses.

Conversation Angles (for sales-prep use case)

3-5 specific topics relevant to the upcoming interaction, based on the research: "They announced a $50M Series C in March, ask about their expansion plans." "Recent G2 reviews mention implementation complexity, we can address this directly." "Their new CTO came from AWS, likely technical buyer orientation."

Quality calibration:

The brief is calibrated to be 80% accurate and 100% useful, not 100% accurate and 80% useful. The agent notes its confidence level for each section and flags anything that should be verified before use in a high-stakes context. The goal is a well-informed starting point, not a verified research report.

For high-stakes uses (board presentations, M&A diligence, major contract negotiations), the agent output is a first draft for human verification, not a final product.

Part 3: Get the Automation

The WorkplaceAI Multi-Step Research Agent accepts a company name and use case via Slack, searches 8-10 sources autonomously, and delivers a structured research brief within 90 seconds.

What's included:

Template workflow requires configuration. Technical familiarity with n8n and Slack app setup assumed. Setup guide included. Questions: support@workplaceai.ai

Read the full implementation guide: workplaceai.ai

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