SaaStr audit reveals only 1 of 16 AI agents comes from legacy vendors

At SaaStr, a leading B2B software community and conference company, we recently conducted an audit of our AI agent stack—the collection of artificial intelligence tools that handle various business functions from sales outreach to content creation. The results revealed something remarkable: only one of our 16 core AI agents comes from a traditional enterprise software vendor.

That single legacy vendor? Salesforce, whose Agentforce platform we’re implementing for AI-powered sales development. The other 15 tools come from startups that barely existed 18 months ago, or from agents we built ourselves using no-code platforms.

This shift represents more than just our company’s technology choices. After two decades in B2B software, I’ve never witnessed such widespread openness to new vendors across enterprise buyers. Companies that typically require 18-month evaluation cycles are signing with two-month-old AI startups. IT departments that normally demand extensive security certifications are starting trials with unproven vendors.

The reason? Traditional software vendors are still figuring out AI agents, while AI-native companies are shipping tools that actually work.

What makes AI agents different from traditional software

Unlike conventional business software that requires human input for each task, AI agents can autonomously handle complete workflows. Instead of a customer relationship management system that stores contact information, an AI agent can research prospects, craft personalized outreach messages, send follow-ups, and update records—all without human intervention.

This fundamental difference explains why legacy vendors struggle. Their existing architecture was designed for human-operated workflows, not autonomous decision-making systems. Adding AI capabilities often means bolting chatbots onto existing features rather than rebuilding from the ground up.

Meanwhile, AI-native startups design their entire platform around large language models (LLMs)—the technology that powers systems like ChatGPT—from day one. This architectural advantage allows them to create genuinely autonomous agents rather than enhanced automation tools.

Our complete AI agent breakdown

Here’s exactly what we’re running across different business functions:

AI agents we built ourselves

Using Replit, a cloud-based development platform that enables non-technical users to build software applications, we created six custom agents:

1. AI Mentor – A 24/7 assistant trained on over 20 million words of SaaStr content, providing instant answers to B2B software questions covering pricing strategies, sales team management, fundraising guidance, and product-market fit
2. AI VC Pitch Deck Analyzer – Comprehensive pitch deck analysis with scoring, fundability assessment, and actionable recommendations including traction analysis and investor matching
3. AI Valuation Calculator – Startup valuation estimates using real-time market data, growth scenarios, and benchmark comparisons against current market conditions
4. AI Startup Benchmarking – Board pack analysis that compares company metrics against industry leaders using data from Bessemer Venture Partners, ICONIQ Capital, and Carta, a cap table management platform
5. AI VC Dealflow – Intelligent matching system connecting founders with appropriate investors based on funding stage, check size, and company type across our network of venture capital firms
6. AI Content Review – Automated assessment of content submissions for B2B software relevance, expertise level, and community value with quality scoring and auto-approval capabilities

The remarkable aspect: we built these six mission-critical agents faster than completing a single vendor’s security questionnaire process.

Third-party AI agents for sales and marketing

Sales Development Representatives (SDR) and outbound:

• Agentforce (Salesforce) – Our sole legacy vendor, natively integrated into Salesforce to engage with our SaaStr community members and prospects
• Artisan AI SDR – Autonomous sales development handling over 15,000 messages across 100 days with 5-7% response rates, managing outbound campaigns for event tickets, sponsorships, and customer reactivation

Business Development Representatives (BDR) and inbound:

• Qualified AI BDR – Inbound chatbot with full Salesforce and Marketo integration that pre-books qualified meetings for sales representatives without human handoff

Sales operations and content:

• Delphi (Digital Jason) – A digital version of myself providing SaaStr-style advice on hiring, compensation, and scaling challenges, handling over 139,000 conversations
• Gamma – Automatically generates customized 20-page sponsorship presentations from our master template in minutes, fully branded for specific prospects
• Momentum & Attention – Revenue operations automation that transcribes sales calls, updates Salesforce, identifies next steps, and flags objections automatically
• Higgsfield.ai – Creates short promotional videos quickly for social media and marketing campaigns
• Reve – AI-powered visual content ideation for design and marketing materials
• Get Recall – Automatically summarizes video content, essential for managing SaaStr’s extensive video library
• Opus Pro – Repurposes long-form video content into clips and shorts for social media distribution

None of these companies existed in their current form three years ago. Most launched within the past 18 months.

The rise of “build your own” AI agents

Perhaps the most significant shift involves companies building their own AI agents rather than purchasing them. Six months ago, creating custom software required hiring engineers, enduring 3-6 month development cycles, managing complex infrastructure, and hoping you understood the requirements correctly.

Today, platforms like Replit enable business users to build functional AI agents by describing what they want in plain English, iterating in real-time, and deploying within hours. We built our AI Mentor using 20 million words of SaaStr content faster than completing a typical vendor security review.

This democratization of software development—sometimes called “vibe coding” for its intuitive, conversational approach—means companies can create perfectly customized solutions without technical teams or lengthy procurement processes.

Why legacy vendors struggle with AI agents

Traditional enterprise software companies face structural challenges that AI-native startups don’t:

Technical debt: Decades of code built for human-operated workflows can’t easily accommodate autonomous AI decision-making

Revenue pressure: Quarterly targets don’t allow for the fundamental architectural pivots required for genuine AI agents

Customer expectations: Existing customers expect backward compatibility, limiting innovation potential

Pricing models: Seat-based licensing doesn’t align with AI consumption patterns

Sales approach: Teams trained to sell features struggle to articulate AI agent outcomes and return on investment

Conversely, AI-native startups benefit from clean architecture designed for LLMs, founder-led selling focused on measurable results, usage-based pricing that scales with value, and nothing to protect while everything to prove.

The enterprise buying window opportunity

This represents the most open enterprise buying period in B2B software history. Companies normally requiring three industry references are becoming first customers in new verticals. IT teams typically demanding SOC 2 Type II certification are starting trials during the certification process.

The urgency stems from competitive pressure—waiting means falling behind, and everyone recognizes this reality.

However, this window won’t remain open indefinitely. Within 18-24 months, several forces will reshape the market:

Consolidation pressure: Companies will experience “AI agent fatigue” and consolidate to 2-3 core platforms rather than managing dozens of point solutions

Legacy vendor catch-up: Billions in AI investment will eventually produce competitive offerings from established vendors, with Salesforce potentially leading this charge

Sophisticated build vs. buy decisions: Organizations will develop internal AI capabilities and evaluate each new agent against their ability to build custom solutions

Tightened procurement: After the experimentation phase, security, compliance, and integration requirements will return to traditional enterprise standards

Distribution advantages: AI-native companies that survive will be those building robust go-to-market capabilities while the opportunity window remains open

Strategic implications for business leaders

For companies evaluating AI agents, several principles emerge from our experience:

Don’t wait for incumbent vendors: The best solutions are coming from companies you haven’t heard of, not from traditional software providers adding “AI features”

Consider building critical agents: For core business processes, custom-built agents using platforms like Replit might provide better results than vendor solutions

Embrace experimentation: The current market conditions favor rapid testing and iteration over lengthy evaluation processes

Focus on outcomes, not features: AI agents should be measured by work completed, not capabilities listed

Plan for consolidation: While experimenting broadly makes sense today, consider how you’ll manage and integrate multiple AI agents as your stack grows

The fundamental lesson from our AI agent audit extends beyond vendor selection. We’re witnessing a rare moment when everything is genuinely in play—vendor relationships built over decades matter less than actual AI capabilities, startup solutions compete head-to-head with enterprise incumbents, and companies can build mission-critical software themselves.

This transformation will define competitive advantage for the next decade. Organizations that recognize and act on this shift while the enterprise buying window remains open will establish significant advantages over those waiting for the market to settle into familiar patterns.