Foundation Capital recently articulated why one of the next trillion-dollar opportunities in AI will come from context graphs: systems that capture decision traces, not just data. Systems of record tell you what happened. Context graphs capture how it happened: what decisions were made, what changed, and why an account moved the way it did.

As models become general-purpose, everyone gets access to the same baseline intelligence. Advantage won’t come from better models. It will come from better context: the proprietary knowledge a company accumulates about how it sells, what works, and why.

Nowhere do you pay more for missing context than GTM.

Why GTM Is a Rich Domain for Agents and Context Graphs

Enterprises spend roughly 30-50 (!!) percent of operating budgets on sales and marketing. That means even small improvements in decision quality and productivity have outsized impact. A few points of cycle time, a few points of win rate, earlier risk detection, a better renewal posture. Those changes compound.

GTM is also information-dense. Over the lifecycle of a single account, teams generate an enormous amount of context: calls, emails, objections, stakeholder dynamics, product usage signals, pricing moves, competitive positioning, internal debate, and the assets created along the way. Every single piece of information is critical in driving the optimal next best action. But in practice, that context is scattered across tools, trapped in people’s heads, and lost at the moment it matters most.

The opportunity is huge and the goal is clear. Acquire and expand customers. The hard part is that GTM context is uniquely difficult to capture and use.

Why GTM Context Is So Hard

GTM has three layers of context complexity.

First, people fragmentation. GTM is a relay race across SDRs, AEs, CSMs, AMs, leaders, RevOps, and marketing. Each role has different incentives and a different slice of the account story. Critical context is created during handoffs, and a meaningful amount of it gets lost.

Second, systems fragmentation. There is no single source of truth. Context lives across Slack, email, call recordings, documents, CRM, product usage, and internal systems. Even when you integrate systems, you still have pieces of the story scattered across different surfaces and timestamps.

Third, decision traces. What actually drove the account forward? What stalled it? What was tried, what was considered and rejected, and what changed the team’s belief about the deal? This is where best practices come from, and it’s the least captured because decisions happen in motion and feedback loops are long.

If you solve these three layers, GTM stops bleeding judgment. Today learning stays trapped in local pockets, and it resets when territories shift or people change roles.

The Reality of GTM Today

In practice, account truth is scattered across a dozen places: Slack deal rooms, call transcripts, emails, CRM, product usage, and the assets created during the cycle (decks, RFPs, one-pagers, mutual plans).

Two examples of obvious places this disjointed reality shows up are forecasting and deal execution.

Forecasting

Accurate forecasting is a Board and C-Level concern. As a technologist, you’d be shocked if you sat in weekly sales forecasting calls and see how they are run. Instead of processing what’s happening across calls, emails, deal rooms, documents, and activity, most organizations compress reality into a handful of CRM fields and a weekly rep summary.

Management layers become the integration layer. Frontline managers interpret reps. Directors interpret managers. VPs interpret directors. By the time the truth reaches the CRO, it’s too late to change the outcome. Forecasting becomes a slow-motion post-mortem.

When the number misses, the ritual repeats. A retro, a few takes about qualification, a tweak to stage hygiene, then everyone moves on. The underlying issue doesn’t change because the system still isn’t capturing the decision trace. The learning doesn’t get encoded, so it doesn’t compound.

Deal execution

Reps learn the official ‘way to sell’ in bootcamp when they just join a company. But in practice,   they go to the best reps and ask how they actually win in specific situations. How they closed a similar deal, what they do when procurement pushes back, who really mattered in the account.

The real playbooks live in people’s heads. The system can’t answer “what works in situations like this,” so the organization falls back to oral tradition.

It’s also why performance is so uneven. You can give two reps similar territories, the same tools, the same support, and one will finish at 50% to quota while another finishes at 300%. The difference is decision quality in context-rich moments, and today that decision-making doesn’t get captured or compounded nor does it spread democratically.

The Solution: What a Context Graph Looks Like in GTM

A context graph is an account’s lived history, represented as a sequence of decisions over time. Not just who the account is and what happened, but the commitments that moved it forward (or didn’t), the context that informed those commitments, and the outcomes that followed.

In practice it links together what’s currently scattered: the people involved, the artifacts created during the cycle (calls, emails, decks, RFPs), the hypotheses driving strategy, and the exceptions that actually determine outcomes (pricing, security, legal, procurement). The “graph” isn’t that you stored more notes. It’s that these elements are connected in a way that explains causality: this change led to that escalation; this stakeholder shift led to that slip; this tradeoff led to that close.

You can bootstrap an initial version by reconstructing trajectories from what already exists: transcripts, emails, deal docs, Slack threads, and even CRM timestamps. You won’t recover every nuance, but you will recover the inflection points.

The real unlock comes when the trace is captured during execution. That’s when it stops being reconstruction and starts compounding.

That leads to the obvious question: who is positioned to build it?

Salesforce Has Lost the Deal

Ask any sales leader how they get up to speed on a live account or deal. They’d cite a ton of different tools, but Salesforce is probably the last place they’d go to.

Salesforce is optimized to store outcomes: stage, amount, close date. It’s built around tidy fields and periodic updates. But it doesn’t capture what determined the outcome. It doesn’t reliably capture why decisions were made, what alternatives were considered, which signals mattered, how momentum evolved, or what exceptions were used and why. 

It also doesn’t fully capture the current state. Reps have no incentive to keep it current — why would I update Salesforce if it doesn’t do anything for me? 

Even when Salesforce does get updated, it only occasionally captures the state change. A stage moved. A close date slipped. An amount changed. The decision that produced that change is largely invisible: what the rep believed, what they considered doing, what constraints they faced, and why they chose that path.

The result is that Salesforce may be a system of record, but it has stopped being the source of truth. In an agent era, that gap becomes a hard limit, because agents don’t just need final fields. They need comprehensive context and decision traces.

If Salesforce can’t be the truth layer, the next instinct is the 10+ systems of action/workflows around it.

Why Point Solutions Don’t Compound

In GTM, there are over 10+ point solutions that serve as systems of action built around specific workflows and personas – whether Sales Engagement Platforms, Conversational Intelligence, Enrichment, or Intent Data tools. They help execute slices of the lifecycle, but they only ever capture a limited aspect of the account story.

That means they end up with less context than the humans operating the deal. They don’t see the full trajectory. They don’t capture the decision trace. Even if you integrate them all, you still don’t get the thing that matters: a continuous account-level record of context and decisions that improves over time.

To get the full trajectory, you need continuity.

Context Graphs Meet Per-Account Agents

GTM is becoming more agentic. Teams are deploying agents for research, outbound drafting, meeting prep, follow-ups, and CRM updates.

The problem is that most of these are workflow agents. They run inside a narrow task, produce an output, and disappear. There’s no continuity or any concept of “evolving state”. That sets them up poorly for building a context graph, because a context graph is the sequence of decisions over time.

To build the graph, you need to capture and improve that sequence. That requires continuity.

That’s why the right unit of intelligence isn’t the opportunity. Nor is it the workflow. Nor is it the specific persona (SDR, AE, CSM). It’s the account. The account is where context accumulates and where the objective stays stable across the lifecycle.

A per-account agent is context-dense, long-running, and goal-directed. Its job is to progress an account through the lifecycle: prospect to pipeline, pipeline to closed won, onboarding to renewal, renewal to expansion. Because it persists, it can maintain state throughout the account’s trajectory.

Once an account agent sits in the execution path, you can capture the decision trace continuously: what the agent recommended and why, what the rep accepted, edited, or rejected, and what happened next. That delta becomes training signal and precedent. Over time, the organization stops relying on oral tradition and starts encoding what it learns. (Side note – see here for ways to interact with long running account agents)

It also does the boring, but critical work in the background: monitoring account changes, identifying new stakeholders, building slide decks, preparing for meetings, identifying timing or risk signals before a human notices. Reps shift from doing everything manually to managing agents: approving actions, correcting reasoning, escalating edge cases, and excelling at what humans do best.

This earns the per-account agents the right to capture the decision trace, because, unlike the system of record, every unit of information provides leveraged value to the end user.

Ultimately, these decision traces can then be aggregated across the account agents: what worked for similar accounts with a similar pain when the deal stalled in this way? How accurate is this forecast from similar historical deals? What are the most effective actions to mitigate risk?

At large enterprises, thousands of reps go into work every day and do independent, disjointed “local” learning; persistent context capture across every account enables “global” learning in a way that rapidly compounds. We will literally start to see companies grow at an accelerating rate for the first time as a result.

Conclusion

GTM is rich, but it’s hard for a reason. Context is fragmented across people and systems, and the decision trace is rarely captured. That’s why forecasting is reactive and why execution depends on local judgment instead of compounding organizational learning.

A context graph tied to the right atomic units helps change the substrate. It connects the account’s lived history across time and makes the decision trace legible. But it only compounds when it’s captured during execution, which is why workflow agents alone won’t get you there. You need continuity, and in GTM continuity lives at the account level.

The platform opportunity isn’t “AI on top of the CRM.” It’s a system of agents that captures the sequence of decisions, learns from outcomes, and improves how the whole organization sells over time.

If you are interested in seeing what this looks like or building the future of revenue & growth, come check us out at Actively AI!