HR Technology & People Analytics Research Agent

HR technology is a sprawling, fast-consolidating category — HRIS/HCM suites, payroll, ATS, performance management, learning, compensation intelligence, people analytics, and DEI tooling — where build-vs-buy and platform-vs-point-solution decisions ride on analyst quadrants, implementation economics, and a moving regulatory floor (pay transparency laws, AI hiring rules, employee-data privacy). This guide builds a Python agent for CHROs and VP People, HR tech vendors and their buyers, people ops teams, HR technology investors, workforce management consultants, compensation analysts, and HR software implementation consultants. It chains all four Superhighway endpoints — /research for the market landscape, /search against authoritative HR trade publications and the vendor ecosystem, /scrape for a specific Magic Quadrant excerpt or vendor comparison, and /news for funding, workforce, and compliance updates — then uses an LLM to emit a structured HR technology brief as JSON.

Overview

The agent takes an HR technology, platform, practice, or workforce trend — "Workday vs SAP SuccessFactors enterprise HCM comparison", "people analytics platform AI workforce planning" — and produces a structured HR technology and people analytics brief:

• Synthesizes the market landscape: HR tech category structure, HRIS/HCM platform capabilities, vendor positioning, category trends, and implementation patterns
• Searches authoritative HR trade publications — HR Executive, HR Technologist, SHRM, Gartner — for platform reviews, market positioning, implementation ROI, and category definitions
• Searches the vendor ecosystem — HRIS/HCM (Workday/SAP SuccessFactors/Oracle HCM vs. Rippling/Gusto/BambooHR), compensation intelligence (Radford/Mercer/Carta), people analytics (Visier/Worklytics), LMS (Cornerstone/Degreed), performance management (Lattice/Betterworks/15Five), and DEI analytics (Syndio/Paradigm)
• Scrapes one relevant page: a Gartner Magic Quadrant excerpt, an HR tech vendor comparison, a SHRM research report, or a G2/Capterra category page
• Pulls recent news: HR tech M&A and funding rounds, workforce trends (layoffs, RTO mandates, AI workforce impact), labor law changes, HRIS platform updates, and compensation data releases
• Uses an LLM to generate a structured brief — vendor landscape, market dynamics, implementation, analytics maturity, compensation benchmarks, regulation, workforce trends, and funding as JSON

Who it's for: CHROs and VP People, HR tech vendors and their buyers, people ops teams, HR technology investors, workforce management consultants, compensation analysts, and HR software implementation consultants.

How it works

Five endpoint calls feed one LLM synthesis:

1. /research — deep synthesis of the market: HR tech landscape, HRIS platform capabilities, vendor landscape, category trends, and implementation patterns.
2. /search (trade publications) — platform reviews, market positioning, implementation ROI, and category definitions scoped to HR Executive, HR Technologist, SHRM, and Gartner.
3. /search (vendor ecosystem, time=year) — HRIS/HCM platforms, compensation intelligence, people analytics, LMS, performance management, and DEI analytics vendors.
4. /scrape — one relevant URL, e.g. a Gartner Magic Quadrant excerpt, an HR tech vendor comparison, a SHRM research report, or a G2/Capterra category page.
5. /news (time=month) — recent HR tech funding and M&A, workforce trends (layoffs, RTO, AI impact), labor law changes, platform updates, and compensation data releases.

Full example

pip install openai requests python-dotenv

Create a .env file with your two keys:

SUPERHIGHWAY_API_KEY=your_key_here
OPENAI_API_KEY=your_key_here

import requests, os, json
from openai import OpenAI

SUPERHIGHWAY_KEY = os.getenv("SUPERHIGHWAY_API_KEY")
BASE = "https://superhighway.walls.sh"
HEADERS = {"Authorization": f"Bearer {SUPERHIGHWAY_KEY}"}

llm = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# 1. Deep synthesis of the HR technology market landscape
def research_market(query: str) -> str:
    """HR tech landscape, HRIS capabilities, vendor landscape, category trends."""
    r = requests.get(
        f"{BASE}/research",
        params={"q": f"{query} HR technology people analytics workforce management"},
        headers=HEADERS,
    )
    data = r.json()
    return data.get("summary", "")[:3000]

# 2. Authoritative HR trade publications and analyst reports
def search_trade(query: str) -> list[dict]:
    """Platform reviews, market positioning, implementation ROI, category defs."""
    r = requests.get(
        f"{BASE}/search",
        params={
            "q": f"{query} site:hrexecutive.com OR site:hrtechnologist.com "
                 f"OR site:shrm.org OR site:gartner.com "
                 f"HR technology workforce analytics platform",
        },
        headers=HEADERS,
    )
    return r.json().get("results", [])

# 3. HR tech vendor ecosystem (last year)
def search_vendors(query: str) -> list[dict]:
    """HRIS/HCM, compensation, people analytics, LMS, performance, DEI vendors."""
    r = requests.get(
        f"{BASE}/search",
        params={
            "q": f"{query} HRIS HCM workforce management compensation analytics "
                 f"DEI platform vendor comparison",
            "time": "year",
        },
        headers=HEADERS,
    )
    return r.json().get("results", [])

# 4. Scrape one relevant Magic Quadrant / vendor comparison / SHRM / G2 page
def scrape_page(url: str) -> dict:
    """Pull a Magic Quadrant excerpt, vendor comparison, research report, or G2 page."""
    r = requests.post(
        f"{BASE}/scrape",
        json={"url": url, "mode": "markdown"},
        headers=HEADERS,
    )
    data = r.json()
    return {
        "url": url,
        "title": data.get("title", ""),
        "content": data.get("markdown", data.get("text", ""))[:2500],
    }

# 5. Recent HR tech news: funding, workforce trends, compliance (last month)
def get_news(query: str) -> list[dict]:
    """HR tech M&A, funding, layoffs, RTO, AI HR impact, labor law, comp data."""
    r = requests.get(
        f"{BASE}/news",
        params={
            "q": f"{query} HR technology workforce layoffs hiring freeze "
                 f"remote work AI HR",
            "time": "month",
        },
        headers=HEADERS,
    )
    return r.json().get("results", [])

def generate_brief(
    query: str,
    market: str,
    trade: list[dict],
    vendors: list[dict],
    scraped: dict | None,
    news: list[dict],
) -> dict | None:
    """Generate a structured HR technology & people analytics brief as JSON."""

    trade_text = "\n".join(
        f"- {r.get('title', '')}: {r.get('snippet', '')} ({r.get('url', '')})"
        for r in trade[:6]
    )
    vendors_text = "\n".join(
        f"- {r.get('title', '')}: {r.get('snippet', '')}"
        for r in vendors[:6]
    )
    news_text = "\n".join(
        f"- {n.get('title', '')}: {n.get('snippet', '')}"
        for n in news[:6]
    )
    scraped_text = ""
    if scraped and scraped.get("content"):
        scraped_text = f"{scraped['title']}\n{scraped['content']}"

    response = llm.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": (
                    "You are an HR technology and people analytics market analyst. "
                    "Use ONLY the provided sources. Do not invent market sizes, vendor "
                    "rankings, compensation figures, or implementation costs — if a "
                    "detail is not in the sources, say 'not found in sources.' Be "
                    "precise about vendor positioning, category boundaries, and "
                    "regulatory status, and flag when figures may be estimates or "
                    "sector averages."
                ),
            },
            {
                "role": "user",
                "content": f"""Write an HR technology & people analytics brief for: {query}

Market Landscape (synthesis):
{market}

HR Trade Publications (HR Executive / HR Technologist / SHRM / Gartner):
{trade_text}

HR Tech Vendor Ecosystem:
{vendors_text}

Scraped Magic Quadrant / Vendor Comparison / Research Report / G2 Page:
{scraped_text}

Recent HR Tech News:
{news_text}

Return JSON with ALL of these fields:
- subject: HR technology, platform, practice, or workforce trend being researched
- hr_domain: "recruiting" | "performance-management" | "compensation" | "learning-development" | "workforce-planning" | "DEI-analytics" | "benefits" | "HRIS-HCM" | "employee-experience" | "people-analytics" | "mixed"
- company_size_focus: "SMB" | "mid-market" | "enterprise" | "all-sizes"
- vendor_landscape: key HR tech vendors and their positioning — e.g., Workday/SAP/Oracle for enterprise HCM; Rippling/Gusto/BambooHR for SMB; specific point-solution vendors by sub-category
- market_dynamics: market size estimates, consolidation trends, build-vs-buy decisions, platform vs. point-solution tension, embedded-AI proliferation in HR tech
- implementation_and_adoption: typical implementation timelines and costs, change management requirements, integration complexity (HRIS to payroll to ATS to LMS), common failure modes
- data_and_analytics_maturity: what analytics capabilities the category offers — reporting vs. predictive vs. prescriptive; data sources (time-tracking, surveys, performance data); privacy constraints (GDPR/CCPA on employee data)
- compensation_benchmarks: if compensation-relevant: salary band philosophy, pay equity analysis approaches, comp survey sources (Radford/Mercer/Carta/Option Impact), equity compensation structures
- regulatory_and_compliance: EEOC/OFCCP requirements, pay transparency laws (CO/CA/NY), GDPR Article 88 (employee data), FLSA classification, global payroll compliance, AI hiring law (NYC Local Law 144)
- workforce_trends: macro trends affecting HR strategy — AI/automation displacement anxiety, RTO vs. remote policy, skills-based hiring, gig/contingent workforce growth, generational workforce shifts
- investment_and_funding: VC/PE activity in HR tech, recent funding rounds, M&A (Workday acquiring smaller tools, PE roll-ups), public company multiples for listed HR tech
- data_quality: "high" | "medium" | "low" — based on coverage from HR trade publications and public analyst data""",
            },
        ],
        response_format={"type": "json_object"},
    )

    try:
        return json.loads(response.choices[0].message.content)
    except (json.JSONDecodeError, KeyError):
        return None

def research_hr_tech(query: str) -> dict | None:
    """Run the full HR technology & people analytics research pipeline."""
    print(f"Researching HR technology: {query}")

    print("Synthesizing market landscape...")
    market = research_market(query)

    print("Searching HR trade publications...")
    trade = search_trade(query)

    print("Searching HR tech vendor ecosystem...")
    vendors = search_vendors(query)

    print("Scraping a relevant Magic Quadrant / vendor comparison / report...")
    scraped = None
    for result in trade + vendors:
        url = result.get("url")
        if url:
            scraped = scrape_page(url)
            if scraped.get("content"):
                break

    print("Pulling recent HR tech news...")
    news = get_news(query)

    print("Generating HR technology brief...")
    return generate_brief(query, market, trade, vendors, scraped, news)

def print_brief(brief: dict):
    if not brief:
        print("Could not generate brief.")
        return
    print(f"\n{'='*60}")
    print(f"HR Technology & People Analytics Brief")
    print(f"{'='*60}")
    print(f"\nSubject: {brief.get('subject', '')}")
    print(f"HR Domain: {brief.get('hr_domain', '')}")
    print(f"Company Size Focus: {brief.get('company_size_focus', '')}")
    print(f"\nVendor Landscape:\n{brief.get('vendor_landscape', '')}")
    print(f"\nMarket Dynamics:\n{brief.get('market_dynamics', '')}")
    print(f"\nImplementation & Adoption:\n{brief.get('implementation_and_adoption', '')}")
    print(f"\nData & Analytics Maturity:\n{brief.get('data_and_analytics_maturity', '')}")
    print(f"\nCompensation Benchmarks:\n{brief.get('compensation_benchmarks', '')}")
    print(f"\nRegulatory & Compliance:\n{brief.get('regulatory_and_compliance', '')}")
    print(f"\nWorkforce Trends:\n{brief.get('workforce_trends', '')}")
    print(f"\nInvestment & Funding:\n{brief.get('investment_and_funding', '')}")
    print(f"\nData Quality: {brief.get('data_quality', '?')}")

if __name__ == "__main__":
    import sys
    query = sys.argv[1] if len(sys.argv) > 1 else "Workday vs SAP SuccessFactors enterprise HCM comparison"
    brief = research_hr_tech(query)
    print_brief(brief)

Usage examples

• "Workday vs SAP SuccessFactors enterprise HCM comparison" — maps enterprise HCM market positioning, compares implementation timeline/cost and module breadth (payroll/benefits/learning/recruiting), traces AI/ML roadmaps and analyst quadrant positions, surfaces customer satisfaction data (G2/Gartner Peer Insights), and frames typical deal size and contract structure.
• "pay transparency law compliance HR technology" — covers CO/CA/NY/IL pay transparency requirements, profiles pay equity analysis software (Syndio/Trusaic/PayScale), weighs OFCCP audit risk and SEC pay ratio disclosure, flags EU Pay Transparency Directive 2026 preparation, and maps vendor features for job-posting compliance.
• "people analytics platform AI workforce planning" — profiles Visier vs. Worklytics vs. Crunchbase People positioning, traces workforce planning use cases (headcount forecasting, attrition prediction), maps data source requirements (HRIS + ATS + performance), covers privacy-preserving analytics approaches, and weighs build vs. buy at enterprise scale.

HR tech market data and compensation benchmarks evolve rapidly. Verify platform rankings and compensation figures against primary analyst sources (Gartner Magic Quadrant for HCM, Mercer/Radford/Willis Towers Watson surveys) and vendor-published documentation.

Getting your API key

Grab a free Superhighway key at /pricing (1,000 calls/month, no credit card). For an agent that provisions its own access, skip the key entirely with x402: it pays $0.002 per call in USDC on Base — no signup, no key management. See the x402 pay-per-call guide for the wallet setup.

See also

The talent research agent applies the same four-endpoint pattern to hiring trends and talent pools rather than the HR tech stack, and the regulatory research agent covers the compliance and pay-transparency landscape that shapes HR technology requirements.