The Agents Economy

We're at an inflection point. AI agents—autonomous systems that can research, decide, and act—are fundamentally changing how B2B sales and marketing operate.

These aren't chatbots. They're intelligent systems that can prospect for leads, enrich contact data, craft personalized outreach, and engage with prospects at scale. They work 24/7. They don't forget. They don't get tired.

But here's the thing most people miss: AI agents are only as good as the data they can access. Feed an agent stale data from a static database, and you get stale results. Feed it real-time intelligence, and you unlock a new paradigm of intelligent automation.

This is the Agents Economy. And it requires a fundamentally different approach to B2B data infrastructure.

Traditional B2B databases were built for humans. They give you a list of contacts with company names, titles, and email addresses. You download a CSV, import it to your CRM, and start blasting outreach.

The problem? That data is already stale by the time you get it.

Consider this: almost 39% of people change jobs every two years. A database of 100 million contacts would need to track and update nearly 50 million job changes each year just to stay accurate. The scale is impossible.

When your AI agent reaches out to leads using outdated information—wrong titles, old companies, dead email addresses—the result is wasted compute, damaged sender reputation, and missed opportunities.

This is why companies like Clay have thrived. Rather than relying on a single, static data source, they aggregate real-time information from multiple providers, ensuring data is always current and actionable.

Captain Data was actually the first provider to introduce waterfall enrichment back in 2020, setting a new standard for the industry. Waterfall enrichment combines multiple third-party data sources, creating a layered approach to data collection. If one source is incomplete or outdated, additional sources are brought in to fill the gaps.

But for AI agents, even waterfall enrichment isn't enough. Agents need data that's not just accurate—it needs to be real-time and programmatically accessible.

In the Agents Economy, timing is everything. An AI SDR needs to know:

• Who just got promoted to a decision-making role
• Which companies just raised funding and are likely scaling their team
• Who's engaging with competitor content on LinkedIn
• Which prospects just visited your pricing page

These are intent signals—real-time indicators of buying readiness. Traditional databases can't capture them because they're static snapshots, not live feeds.

Pigment, a leading FP&A platform, uses Captain Data to continuously monitor real-time signals and push the right data into their CRM. Their sales reps focus on prospects showing strong buying intent at that moment. The result:

• 20% less manual work — Intent signals are automatically enriched and funneled directly into their CRM
• Higher focus — Clear indicators of which prospects are in-market
• Better conversions — Reaching out when prospects show clear intent

This is the power of real-time data: it transforms outbound from "spray and pray" into precision targeting, whether you're a human sales rep or an AI agent.

The evolution of sales technology has followed a predictable arc:

Phase 1: Manual (2010s) — Sales reps did everything by hand. Research, prospecting, outreach, follow-ups. CRMs were glorified spreadsheets.

Phase 2: Automation (2015-2022) — Tools like Outreach, SalesLoft, and HubSpot sequences automated the execution layer. You still had to tell them exactly what to do, but they'd do it at scale.

Phase 3: Orchestration (2022-2024) — Platforms like Clay, n8n, and Zapier let you chain tools together. Complex workflows that would take developers weeks could be built by ops teams in hours.

Phase 4: Agents (2024+) — AI agents don't just execute workflows—they make decisions. They research, prioritize, adapt, and act. They don't need a predetermined playbook; they figure out what to do based on context and goals.

We initially believed that Sales Operations and Revenue Operations would become increasingly technical, with complex workflows automating every CRM process. We documented this expectation in our Rise of Operations white paper.

But since 2020, we've observed the opposite. Sales stacks became too technical for most teams. Sales reps wanted to focus on selling, not managing tools. Executives sought simplification.

AI agents are the answer. They absorb the complexity so humans can focus on what humans do best: building relationships and closing deals.

There's a parallel trend reshaping the market: the shift from "best of breed" point solutions to all-in-one platforms.

Products like Lemlist, Apollo, and Zeliqare leading this charge. They integrate multiple functionalities—data, sequencing, engagement—into a single platform. This consolidation makes it easier to leverage data, especially intent data, without juggling a dozen tools.

For AI agents, all-in-one platforms are ideal hosts. The agent has a unified environment with consistent data formats, integrated actions, and coherent state management. No need to orchestrate between fragmented tools.

This explains why Captain Data is evolving from a workflow-based approach to becoming an API-first toolkit. Our goal is to empower technical teams—whether they're building full-fledged SaaS products, internal tools, or AI agent systems—with the data infrastructure they need.

One thing that hasn't changed, and in fact has intensified, is the dependency on LinkedIn. The rise of social selling on LinkedIn is undeniable. Whether you're leveraging content, connecting with leads, or driving outreach, LinkedIn remains the central hub for modern B2B sales strategies.

AI agents thrive on intent signals—real-time indicators that reveal when a prospect is ready to buy. Here are the signal categories that matter most:

Job and Role-Related Signals

• When a company recruits for key positions (e.g., VP of Sales)
• When your target changes jobs or companies
• When a company's headcount is growing or shrinking
• When a satisfied customer moves to a new company

Funding and Financial Signals

• When a company raises funding
• When a company raises from your existing investor (warm intro opportunity)

Competitor and Market Signals

• When a bad review is published about a competitor
• When a company enters a new market
• When a company launches a new product

Engagement and Community Signals

• When your target asks a question in a professional community
• When your target follows your company's or a competitor's LinkedIn page
• When your target comments on relevant posts
• When your target downloads one of your resources
• When your target registers for an event

Technology Signals

• When a company uses a complementary or competing tool
• When a company is listed on a marketplace (Salesforce AppExchange, HubSpot, etc.)
• When a company is running ads (analyze them for insights)

By feeding these signals to AI agents in real-time, you enable them to prioritize outreach, personalize messaging, and engage prospects at the exact right moment.

In conversations with Guillaume Cabane, a well-known figure in sales tech and growth, he emphasized:

"Tools are important, but mindset is what determines success. Those who embrace intent data thrive, while others fall behind."

The key advantage of intent data lies in precision. By narrowing your total addressable market (TAM) and focusing on high-intent leads, you prioritize efforts where they matter most.

Simply relying on tools like ZoomInfo or Clearbit provides generic insights. But with the "lasagna approach"—stacking multiple data sources—you get a more complete picture of prospect behavior:

• Layer 1: Firmographics (company size, industry, location)
• Layer 2: Technographics (tools they use, tech stack)
• Layer 3: Intent signals (web visits, content engagement)
• Layer 4: Social signals (LinkedIn activity, job changes)
• Layer 5: Relationship data (mutual connections, shared experiences)

Each layer adds context. An AI agent with access to all five layers can craft outreach that feels genuinely personalized—because it is.

Attention.tech, whose CEO Anis explains:

Captain Data powers our in-product referral program and feeds real-time intent signals into our enrichment engine, making our GTM strategy scalable.

With Captain Data's automation, their sales reps save 30%+ of manual effort and focus on prospects showing live intent.

As Product-Led Growth (PLG) models gain traction, leveraging real-time data becomes even more critical. PLG companies depend on product usage data to identify when users are ready to convert.

As Nicolas Druelle explains in his conversation with Guillaume, Captain Data's co-founder & CEO, about GTM operations:

PLG growth models require seamless onboarding and personalized engagement to thrive. Intent data helps fine-tune this approach by aligning sales and product teams to focus on high-conversion opportunities.

AI agents are the perfect complement to PLG. They can:

• Monitor product usage patterns and identify expansion opportunities
• Reach out to users who hit key milestones or show buying signals
• Provide instant responses to pricing questions via chat
• Automate the entire upgrade flow for self-serve conversions

The combination of PLG + AI agents + real-time data creates a flywheel: better data leads to smarter agents, which drive more conversions, which generate more data to train the agents.

One company that exemplifies the power of agent-driven strategies is Cargo. As an AI Revenue Orchestration Platform, Cargo helps GTM teams build AI agents that qualify leads, engage prospects, and convert opportunities—scaling revenue operations without adding headcount.

Cargo leverages Captain Data's APIs to power their real-time data infrastructure. They use our People Search and Company Search APIs to help their customers identify best-fit accounts and generate high-quality lookalike lists. Combined with our enrichment capabilities, Cargo keeps their customers' CRMs up-to-date with accurate, live data from multiple providers.

The impact:

• Always-fresh data — Cargo's AI agents operate on real-time data, not stale databases, ensuring every outreach is based on current information
• Scalable infrastructure — By leveraging Captain Data's APIs, Cargo can scale to thousands of customers without building their own data pipelines
• Focus on core product — Instead of maintaining scrapers and enrichment tools, Cargo focuses on building the best AI agent experience for GTM teams

This combination of AI-driven orchestration and real-time data infrastructure gives Cargo the competitive edge they need to lead the AI GTM revolution.

At Captain Data, we provide the infrastructure layer for the Agents Economy. Our platform includes 7 powerful API endpoints designed for real-time B2B data:

People APIs

• Enrich People — Get comprehensive profile information from a LinkedIn URL
• Search People — Search and discover people based on criteria
• Find People — Find people by full name and optional company name

Company APIs

• Enrich Company — Get comprehensive company information from a LinkedIn URL
• Search Companies — Search and discover companies based on criteria
• Find Company — Find a company by name or domain
• Find Employees — Retrieve employees for a specific company

Together, these endpoints form the foundation of our platform, helping developers and GTM teams integrate real-time B2B data into their operations—or build AI agents that do it autonomously.

Developers can harness Captain Data to power their AI agents and SaaS products:

• Simple REST APIs — Our 7 endpoints cover people search, company search, and enrichment—everything your agent needs to understand prospects and accounts
• MCP Integration — We offer a Model Context Protocol (MCP) server that lets AI agents like Claude directly access our B2B data APIs with natural language
• Built for scale — Handle millions of API calls with predictable pricing and reliable infrastructure, perfect for powering data features in your product

This flexibility allows developers to focus on building products while offloading complex data infrastructure to Captain Data. We handle the heavy technical lifting, allowing faster time-to-market and reduced complexity.

For AI agent builders specifically, our APIs are designed with predictable rate limits, consistent response formats, and real-time data—exactly what autonomous systems need to operate reliably at scale.

GTM teams can leverage Captain Data across their entire operational ecosystem:

• Power your AI assistants — Use our MCP server to give ChatGPT, Claude, or any AI assistant access to real-time B2B data for research and prospecting
• Integrate with automation platforms — Connect Captain Data to n8n, Make, Zapier, or any workflow tool to enrich leads and accounts automatically as they flow through your GTM stack
• Unify your data layer — Instead of juggling multiple data providers, use a single API to search and enrich people and companies across your entire tech ecosystem

Whether you're building no-code automations or working alongside AI agents, Captain Data provides the real-time B2B data foundation your GTM operations need to scale.

As a fellow SaaS company, we know that buy vs. build decisions must make both technical and financial sense.

Captain Data's business model reflects this reality—our pricing is based on the number of synced users and automated actions, ensuring scalability without unpredictable costs.

We've helped companies like Zeliq, AI SDR,Lemlist, and Clay reduce development time and costs, scaling millions of API calls while maintaining predictable pricing.

In the Agents Economy, speed matters. The companies that can deploy AI agents fastest will capture the market. We're here to help you move fast.