Leadership
Peter Micciche
July 7, 2026
By Peter Micciche, CEO, Certain
TL;DR: No. Every agent platform on the market reads the same foundational inputs, namely CRM records, enrichment APIs, and third-party intent data, so when two competitors feed identical data into different platforms, the targeting and prioritization converge. The differentiation comes from a data layer your competitors can't replicate, and the first-party engagement data from the events you already run is exactly that.There's a question consuming every GTM leader's calendar right now: which AI agent platform should we buy? I've watched it unfold across boardrooms, vendor pitches, and conference panels for two quarters, and it's the wrong question. Every platform reads from the same foundational inputs, so when two competitors feed identical enrichment data into two different agent platforms, the outreach looks much the same. The phrasing differs and the timing shifts by a few hours, but the targeting, prioritization, and contact selection converge. The edge comes from a data layer your competitors can't replicate and you alone own.
What data layer is missing from most AI agent stacks?
The layer missing from most agent stacks is first-party event engagement. Who attended which session, who visited your booth twice, who asked a specific question during a breakout, and which three stakeholders from the same target account showed up together and booked a follow-up meeting.
Event management platforms capture this every day. In most organizations it never reaches the systems where agents operate.
According to RevSure's 2026 study of 306 B2B GTM leaders, 47% cite data quality and lead quality as the primary barrier to scaling agentic AI. The report frames this as a readiness gap. The issue goes deeper.
The data most agents read is missing an entire signal category. That category sits in an event ops silo. The category is exported to a spreadsheet. The category is maybe uploaded to the CRM weeks later as a static list. The category is stripped of all the behavioral context that made it useful in the first place.
This is the data that shows you how events reveal buying committees before your CRM ever names them.
Think of it this way. Most GTM teams have built a high-performance engine. Most GTM teams connected the engine to a fuel line that delivers the same grade of gasoline as everyone else on the track. The engine is not the problem. The fuel is. The highest-octane fuel available, the one your competitors cannot buy at the same pump, is the first-party engagement data from the events you already run.
Why is event data structurally different from intent data?
Event data is structurally different because it operates at the individual stakeholder level. Intent data stops at the account.
Third-party intent providers can tell you a company is surging on a topic. Third-party intent providers can't tell you that the CFO, CTO, and VP of Procurement all attended the same breakout and sat together for 45 minutes discussing it.
Intent data identifies the account. Event data identifies the people forming the buying committee.
Third-party intent data has become genuinely useful. Providers like Bombora have built sophisticated models that tell you which accounts are actively researching a topic. That signal is an essential starting point for any agent-driven outreach.
The progress in this space over the past few years is quite real.
Where intent data reaches its limit is at the individual level. Bombora can tell you "Company X is surging on ERP." Bombora can't tell you which people inside Company X are mobilizing.
Gartner's research puts the average B2B buying group at 6 to 10 stakeholders. Event data fills that gap.
This is also why event data is the best first-party data for sales that agents can act on.
These are complementary signals. Agents need both to work well.
Intent data narrows the field to the accounts worth pursuing. Event engagement data shows you who within those accounts is mobilizing.
Research on multi-threaded selling shows that single-threaded deals close at half the rate of multi-threaded ones. Research also shows that deals with three or more engaged contacts close at significantly higher rates across industries and deal sizes.
Consider the gap that compounds over quarters when both signal types feed the same agent stack.
How much event data do you need for it to matter?
You need far less than you'd think. The signal density per event is high.
A single 200-person field event generates more buying committee formation data than six months of website visits. The reason is that you're seeing real people in real rooms making real decisions about whether to engage further.
Four field dinners with the right accounts, captured and routed into the agent stack in real time, can change a quarter's results.
There's a reasonable objection here. Most companies don't run Dreamforce-sized programs. If you're running four field dinners a quarter, is there enough event data to matter?
The answer is yes. The reason is signal density compared to other channels.
The real question isn't volume. The real question is whether you're capturing what happens at the events you already run. The real question is whether you're routing those signals so your agents can act.
That routing is where orchestrating event signals at scale earns its keep.
What does feeding event data into an agent stack look like in practice?
In practice it looks like an agent catching a buying committee no intent source can see.
Take a mid-market SaaS company with an 800-attendee annual user conference. The RevOps team wires the event data directly into their GTM agent stack the week before.
When three stakeholders from a target account attend the same breakout and book a follow-up at the partner pavilion, the agent flags the cluster within hours.
The program is the typical one you'd expect. The program includes a keynote, breakout sessions, a partner pavilion, and networking dinners.
Their event management platform captures session-level attendance, polls, and meeting requests. Those items are standard event operations.
When three stakeholders from a target account, including a VP of Finance who had never appeared in their CRM, attend the same breakout on procurement automation and then book a follow-up meeting, the agent routes the signal to the account owner. The agent drafts context-specific outreach referencing the session topic. The agent updates the opportunity record with the new buying committee member.
The deal closes 11 weeks later. The VP of Finance, invisible to every intent data source in their stack, turns out to be the economic buyer.
What makes the difference is their agent's access to a signal no one else in the deal can see.
Why is a proprietary event data layer a wiring problem, not a build problem?
It's a wiring problem because event management platforms already capture this data.
You don't need to build a proprietary data layer from scratch. The gap is the orchestration layer that routes event engagement to the agent stack in real time.
Companies that solve this quarter get the compound advantage of proprietary signal accumulating. Competitors keep drawing from the same third-party well.
Some may be thinking that building a proprietary data layer sounds expensive and slow. Most GTM teams are under pressure to show agent ROI this quarter. Most GTM teams are not building infrastructure for next year.
That objection assumes you need to build from scratch. You don't.
Craig Rosenberg at Scale Venture Partners has observed that traditional GTM playbooks have been made obsolete by AI in just nine months. I'd add that the next playbook isn't about which AI to deploy. It's about which data to feed it.
According to the same RevSure study, 76% of organizations are already deploying or implementing agentic AI across their GTM stacks.
The agent platforms will keep improving. The agent platforms will increasingly resemble one another. The first-party data layer you own is where the divergence happens.
The Bottom Line: Your Competitors Can Buy the Same Agent by Friday
The agent platforms will continue to converge. Your competitors can buy the same agent platform by Friday. They can't buy what happens in your events.
That's the whole argument in one line. The engine everyone races is becoming the same engine. The fuel is what you own. The fuel is sitting in the events you're already running.
Wire it into the agent stack. You start building an edge that compounds every quarter your competitors keep drawing from the same well.
Frequently Asked Questions
Does the AI agent you pick matter more than the data you feed it?
No. Every agent platform reads the same foundational inputs: CRM records, enrichment APIs, and third-party intent data.
When competitors feed identical data into different platforms, the targeting and prioritization converge.
The edge comes from a data layer your competitors can't replicate. Your first-party event engagement data is exactly that.
What data layer is missing from most AI agent stacks?
First-party event engagement data is missing from most AI agent stacks. First-party event engagement data includes who attended which session, who visited your booth twice, who asked a specific question, and which stakeholders from the same account showed up together.
Event platforms capture it. It usually sits in an ops silo and never reaches the systems where agents operate.
How is event data different from third-party intent data?
Intent data identifies the account that's researching a topic.
Event data identifies the specific people within that account who are forming a buying committee.
Gartner puts the average B2B buying group at 6 to 10 stakeholders. Event engagement shows you who's mobilizing inside the account intent data flagged.
Do you need a huge event program for event data to matter?
No. A single 200-person field event generates more buying committee formation data than six months of website visits.
Four field dinners with the right accounts, captured and routed into the agent stack in real time, can change a quarter's results.
Peter Micciche is CEO of Certain, the leading AI-powered Event Signal Platform for enterprise B2B companies. Connect with Peter on LinkedIn or visit certain.com to about transforming events into revenue engines.