October 1, 2026: ZoomInfo (NASDAQ: GTM) announced the acquisition of DoubleO.ai and the simultaneous launch of Agent Teams, a multi-agent orchestration layer for enterprise go-to-market teams. The move is one of the clearest signs yet that the GTM software category is reorganizing around coordinated AI agents rather than point tools or single-agent copilots.

What Happened

ZoomInfo closed the DoubleO.ai acquisition on September 30 and shipped Agent Teams to GTM Studio on October 1. The product is available to existing customers without a new SKU.

DoubleO.ai was founded by Derek Osgood (CEO) and Karthik Suresh (CTO), who previously co-founded Ignition, a product marketing platform acquired by Klue in 2024. The pair built DoubleO.ai specifically to solve orchestration reliability: the problem that individual AI agents succeed in demos but fail when chained together in production workflows.

ZoomInfo CEO Henry Schuck framed the problem bluntly in the launch announcement: “Every revenue leader has seen an AI agent demo. Almost none have seen it actually run a real GTM motion end-to-end.”

How Agent Teams Works

Agent Teams coordinates multiple specialized AI agents across a single GTM motion. Each agent handles a distinct task (research, signal detection, content drafting, outreach sequencing), but they share context continuously rather than passing static data files between steps.

The architecture rests on the GTM Context Graph, ZoomInfo’s proprietary data layer covering more than 100 million companies, 500 million contacts, and billions of real-time signals (intent, job changes, funding events, product usage). This is what differentiates Agent Teams from general-purpose multi-agent frameworks: the agents do not need to fetch context from external APIs at runtime, because the context graph is the runtime substrate.

Karthik Suresh described the dependency directly: “Agents can’t operate without deep, trusted context about every account, contact, and signal, and no one has more of that context than ZoomInfo.” Derek Osgood added: “We built DoubleO.ai to run complex workflows reliably, every time, which is where you see 10x gains. The one thing I desperately wanted while building it was ZoomInfo’s Context Graph.”

The evaluation layer runs in real time. Each agent’s output is assessed before the next step begins, which prevents compounding errors. This is the core reliability contribution DoubleO.ai brought to the deal: a pattern for multi-agent workflows that checks coherence at each handoff rather than only at the end.

Real Use Cases in Production

ZoomInfo published two reference patterns at launch.

Champion tracking. When a key contact at a closed account changes jobs, Agent Teams detects the signal, researches the new role, identifies open opportunities at the new company, drafts a personalized outreach sequence, and queues it for rep review. A workflow that would take a rep two hours of manual lookups runs in minutes, with richer context than most reps would assemble on their own.

Closed-won expansion. Agent Teams identifies lookalike accounts to a recently closed deal, pulls firmographic and intent signals on each, drafts a case study excerpt relevant to the target’s profile, and generates sequenced outreach. This collapses the gap between “we just closed a great deal” and “we have an active outbound motion against the accounts most likely to buy next.”

Caroline Burnett at Silverfort, one of the early design partners, described the result: “Agent Teams has given me the ability to scale RevOps and accelerate our GTM both in quality and outcomes.”

GTM.AI: The Headless Integration Layer

Alongside Agent Teams, ZoomInfo continues to develop GTM.AI, its headless GTM context layer operating at gtm.ai. This is the MCP-compatible API surface that lets third-party AI systems tap into ZoomInfo’s data.

Current integrations: Salesforce Agentforce, HubSpot Breeze, Microsoft Copilot Studio, Anthropic Claude, and OpenAI ChatGPT. The practical implication is that enterprises already running AI workflows in one of these platforms do not need to rebuild inside GTM Studio. They can pull ZoomInfo’s context graph directly into their existing agent infrastructure.

This positions ZoomInfo as a data substrate provider to the broader agentic enterprise stack, not only as a standalone product. For enterprise technology buyers, this matters: data gravity pulls toward whichever platform holds the richest, most reliable context, and ZoomInfo is betting that role belongs to them.

Why This Matters for Enterprise AI and GTM

The ZoomInfo-DoubleO.ai deal crystallizes a pattern that has been building across the GTM software market in 2026. Point tools (AI SDR, AI email writer, AI signal detector) are being folded into orchestrated systems. The question for enterprise buyers is shifting from “which AI tool produces the best individual output?” to “which platform can coordinate multiple agents reliably over a full GTM play?”

ZoomInfo’s answer: the coordination layer (DoubleO.ai’s orchestration and evaluation architecture) is only as good as its data substrate. Orchestration without trusted, real-time account and contact context produces agents that are fast but inaccurate. Context without orchestration produces useful data that still requires human assembly into a workflow.

The acquisition combines both. And the GTM.AI MCP integration means ZoomInfo is not restricting this combination to its own UI: enterprises that want to run these patterns inside Salesforce or their own Claude-powered workflows can do so.

For GTM and revenue operations leaders, the relevant pressure is now organizational, not just technical. If Agent Teams and similar platforms can compress a two-hour rep workflow to minutes with higher context fidelity, the question is whether revenue org structures, compensation models, and rep-to-account ratios are calibrated for a world where agents run the first two stages of most pipeline motions.

This is the challenge autonomous GTM systems create: not whether agents work, but how to redesign the human layer around them. ZoomInfo Agent Teams is the most substantive production-ready answer yet from the data intelligence side of the market. Salesforce Agentforce (previously covered here) approaches the same question from the CRM and workflow side. The two will increasingly compete at the orchestration layer, even as they interoperate through GTM.AI’s MCP compatibility.

Enterprise teams building their own agentic GTM stacks should assess both the orchestration architecture and the data layer independently. A multi-agent framework built on weak or stale data will fail at production scale regardless of how good the orchestration logic is. Clay’s $115M Series D earlier this year and ZoomInfo’s DoubleO.ai acquisition both signal that the market agrees: data context is the scarce input, and the platforms that own it will own the orchestration layer above it.

If you are designing an agentic GTM system for your enterprise, talk to Enera about how to structure the data substrate and orchestration architecture before you start building.

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