Agentic GTM OS Deepdive

This is the second in a three-part series on the Agentic OS for GTM (See Part 1). In Part 1, we explored the why - how GTM is in crisis, why it’s not entirely surprising that most AI implementations are not yet delivering transformational value. In this post we’ll explore in more detail how the Agentic GTM OS works, what makes it an “Operating System” and how it’s different from other approaches to AI in GTM.

Recap of why GTM OS?

GTM is in crisis and everyone’s desperate to accelerate growth efficiently. At the same time, McKinsey in its State of AI 2025 report says 8 out of 10 organizations are not even seeing bottom-line savings with Gen AI, let alone transformative outcomes. McKinsey research on agentic AI identifies the core issue:

At the heart of this paradox is an imbalance between “horizontal” (enterprise-wide) copilots and chatbots - which have scaled quickly but deliver diffuse, hard-to-measure gains - and more transformative “vertical” (function-specific) use cases - about 90 percent of which remain stuck in pilot mode.

The Agentic GTM OS is designed to break through this barrier. It accelerates growth by orchestrating demand at scale across your entire GTM engine. It powers use cases from ABX and partner co-sell to event marketing and demand generation, taking actions across web, mobile, social, email, phone, and in-person channels. Unlike traditional automation, agents sense, reason, act, and learn - not just automating tasks but amplifying your organizational capacity. Before looking at how the Agentic GTM OS works, we need to examine what makes agents fundamentally different from traditional software approaches.

How are agents different?

Agents are indeed revolutionary in terms of possibilities they open up - but not without downsides like hallucinations, explainability, reliability, and so forth.

In GTM, to utilize agents at scale for anything beyond low-level tasks that do not directly impact revenue or customer experience, requires significant engineering skill and months of effort. Even high-growth software companies building products using AI face these formidable engineering challenges as this ICONIQ study shows.

The reality is stark: while “vibe coding” simple AI apps and agents may seem deceptively easy, building enterprise-grade GTM AI systems that handle customer-facing and revenue-impacting workflows is quite complex. With $4.6 trillion worth of work potentially shifting to Services as Software powered by agents, the stakes couldn’t be higher. There has to be a better way - and that’s exactly what the Agentic GTM OS delivers for Sales and Marketing.

Unpacking the Agentic GTM OS

As discussed in Part 1, we are taking a deliberate approach to designing this as a unified GTM system of agents. Agentic AI enables us to make this giant leap away from the constraints imposed by traditional software engineering such as structured data models and leverage the power of agents such as multimodality and reasoning/planning.

So how does this unified approach actually work in practice? The best way to understand the how of the agentic OS is to consider it as an outer flywheel loop that drives business outcomes - and hundreds of inner flywheels with agents driving individual tasks across multiple GTM domains.

In the outer loop, you can see the flywheel gather momentum by progressing from Attract > Engage > Convert > Expand. Not only does it break free from the rigid linear funnel that does not correspond to how technology is bought any more, it also emulates how good organizations should learn by iterating and collaborating with expert oversight.

Now let’s examine how these inner flywheels actually operate. Each consists of specialized agent teams following the core Sense-Reason-Act-Learn cycle:

• Sense: Agents continuously listen to zero-party data and 1st party signals like form fills or badge scans at events to online deep research and 3rd party intent data.
• Reason: Agents can reason based on the signals, its own knowledge base, operational policies and deploy one or more strategies like Field Sales or Partner-led, Nurture, applying SEO/GEO, etc.
• Act: The ability to take actions across channels like Web, Email, Social and internal systems and tools.
• Learn: Agents have to learn from their own performance, other agents and most importantly human expertise leveraging techniques ranging from few-shot examples and fine-tuning to SFT and Reinforcement Learning.

Now that you understand the high-level architectural choices, let’s meet the specialized agents that make it all work.

Agents & Core Services

At the core of the GTM OS are more than a dozen agents specializing in multiple Sales, Marketing, and Product domains - all trained on your data, operational policies and connected to your core systems of record.

• Research Analyst to research target accounts
• Planning Analyst to create account sales plans
• Product Analyst to analyze product or technology capabilities
• Solution Analyst to map product offerings to customer’s needs
• Competitive Analyst to analyze competitor signals and recommend actions
• Copy Writer to create copy in the voice of specific writer personas
• Copy Editor to review and provide feedback to writer agents
• Your own custom agent using providers like OpenAI, Gemini, Anthropic, Llama or your own custom models. Hooked up to tools for everything from churn prediction with your proprietary ML models to 3rd party MCP servers.

What makes this an “Operating System” are the shared platform services that enable it to operate at scale

• Workflow Orchestration: Ensure reliable execution and performance across multiple agents and inter-agent co-ordination
• Scheduling: Scale agentic workflows by routing hundreds thousands if not millions of requests and API calls across several external and internal API and model providers
• Context Engineering: Ensure agents use models with the right prompts with relevant knowledge and context along precise guidelines on how to use it
• Security: OS security model with multiple rings and a least privilege-based access controls and end to end auditability
• Agent User Experience: Both command-and-control for ops users and documents as zero-friction collaborative surfaces for end users like sellers
• FinOps: Track and manage what is spent by the agents per task, per account or partner - to control Customer Acquisition Cost (CAC) and maximize RoI.

This all sounds complex in theory, but it’s remarkably simple in practice and only takes minutes to start and produce results. There’s no software to install, servers to set up or complex UIs and workflows to learn.

Here’s how it works for one of our most popular use cases - Agentic Account-based Experiences (ABX) in three simple steps. A team of 6 or more agents working on hundreds of target accounts across 10+ channels, listening to 60+ signals on auto-pilot.

Behind the scenes, Poexis spins up agents to do target account research, creates solution analysis for each target account, design engagement plans, craft 1:1 customized seller tools, create customer-facing content like emails, slide decks and enable you to publish customer-facing landing pages in a single click. This is what typically would have taken 3-4 weeks for a cross-functional team of 5-6 people, all in minutes.

How’s it different?

Given we’re still in the early stages of the AI paradigm shift, there’s naturally a lot of buyer confusion and vendor noise. We’ve created a GTM AI adoption model to help GTM leaders navigate this journey - from experimenting with chatbots to implementing enterprise-grade agentic systems. More on this framework in next week’s post, however the key take away should be that not all GTM AI solutions and approaches are alike - you really have to choose your trail and level up to drive significant business value.

But with so many AI vendors making similar claims, how do you evaluate what’s truly different and what will get you where you want to go? Needless to say this is a fast evolving space - and a comparison table can only capture so much nuance - but here’s how the Agentic GTM OS compares across a set of key parameters:

The Agentic GTM OS delivers what’s needed in the AI era for GTM teams to fully leverage the power of Agentic AI - not just better tools, but a complete operating system designed for agents working alongside humans at Enterprise scale.

In Part 3, we’ll explore real use cases and customer implementations: how companies are using the Agentic GTM OS to transform their ABX programs, accelerate partner co-sell, and achieve results like 1750% ROI.