We've reached the point where the playbooks stop. What happens when you've connected all the tools, wired all the data, and still don't know what's next? This is a framework for pushing past that wall.
The Event Horizon
Every GTM team eventually hits the same wall.
You connect Clay to Outreach. You wire up your intent data to your sequences. You build the perfect orchestration workflow. You hit play.
And then nothing changes.
You're standing at what I call the event horizon - the point where you've done everything the playbooks tell you to do, and you still can't see what's next. The tools are connected. The data is flowing. But the fundamental problem remains: you're still manually deciding who to reach out to, what to say, and when to say it.
The workflows automated the keystrokes. They didn't automate the judgment.
Why GTM Is Harder Than Code
Here's something most people don't understand: building agents for GTM is fundamentally harder than building agents for coding.
Coding agents work because code is deterministic. You can verify correctness. A test passes or it doesn't.
Customer support agents work because knowledge bases are static. The answer to "how do I reset my password" doesn't change week to week.
GTM is different. It's a dynamic environment where:
- What worked yesterday stops working tomorrow
- Each account's context is completely unique
- The "right" decision requires synthesizing signals that change hourly
- There's no ground truth - only outcomes you won't see for months
This is why the go-to-market space is 6-12 months behind the coding agent frontier. The problem is genuinely harder.
But that's also why the opportunity is so massive.
The Five-Layer Agent Architecture
After studying teams that have actually deployed agent systems at scale - thousands of agents running in production - a pattern emerges. Here's the architecture:
Layer 1: The Blueprint
The hard-coded identity layer. What this agent is, what it's entitled to do, what it's forbidden from doing. Think of it as the agent's constitution - it doesn't change based on context.
Layer 2: Responsibilities
Mini-behaviors encoded in plain English. Each responsibility is a discrete piece of work: "When a Tier 1 account visits the pricing page twice in one week, draft a personalized outreach sequence." A single agent might have dozens of responsibilities.
Layer 3: Event Listeners
What ambient signals should this agent care about? Job changes. Website visits. Intent spikes. Competitor mentions. You encode the trigger: "When this happens, wake up and evaluate."
Layer 4: Tool Access
The capabilities available to the agent. CRM queries. Email sending. Ad targeting. Meeting scheduling. You outfit each agent with exactly the tools it needs for its responsibilities - nothing more.
Layer 5: Constituent Scope
Each agent instance is scoped to a specific entity. One account. One deal. One person. This keeps context manageable while allowing thousands of agents to run simultaneously.
| BLUEPRINT (Identity + Entitlements) |
|---|
| RESPONSIBILITIES (Behavioral specifications) |
| EVENT LISTENERS (Triggers from world) |
| TOOL ACCESS (Capabilities to act) |
| CONSTITUENT SCOPE (Account/Deal/Person) |
The key insight: Humans don't operate the agents. They configure the behavioral specifications, observe the outputs, and tune the responsibilities. The agents operate themselves.
The Inter-Agent Context Problem
Here's where it gets hard.
Once you have an agent system running at scale, you immediately hit the second-order problem: your agents don't know what other agents are doing.
Agent A decides to send an email. Agent B decides to retarget on LinkedIn. Agent C schedules a call. None of them knows what the others just did. You end up with a prospect receiving three touches in one hour, or worse, contradictory messages from different channels.
At scale, you need something above the individual agents: an orchestration layer that maintains coherence across the entire system. Not just routing requests, but understanding the holistic state of each account and coordinating actions across all the agents working on it.
This is genuinely unsolved. The teams building at the frontier are experimenting with:
- Parent event streams that all agents subscribe to
- Router responsibilities that allocate work across agents
- Skill-set abstractions that group responsibilities into coherent units
Nobody has cracked it yet. But this is where the real differentiation will emerge.
The Tracing Imperative
When something goes wrong (or right), you need to understand why.
With traditional workflows, debugging is linear: Step 1 led to Step 2 led to Step 3. Easy.
With agents making decisions based on context, the trace becomes a graph. The agent read these 15 signals, weighted them somehow, and decided to take this action. Why? What would it have done if one signal were different?
This is why decision traces are becoming the new primitive.
Every decision an agent makes should be logged with:
- What context it had access to
- How it interpreted that context
- What alternatives it considered
- Why it chose what it chose
Without tracing, you can't debug. Without debugging, you can't improve. Without improving, you're just shipping black boxes and praying.
The Economic Model Nobody's Talking About
Here's something that will reshape the entire industry.
Traditional SaaS sells workflows. "Here's sequence automation - $50K/year." You package a capability, put a price on it, and sell it to a department.
The problem? You're leaving massive value on the table.
If a workflow solves a $200K problem but costs $30K to run, you don't capture that premium. And you've pigeonholed yourself into one department, one use case, one budget holder.
The new model is usage-based and department-agnostic.
Instead of selling a workflow, you say: "Here's the amount of dollar spend you want to allocate. For every problem we solve across your entire organization, we'll itemize that on your receipt."
The bet: Jevons Paradox applies to agent systems. When you make it cheap and easy to solve problems, customers don't spend less - they find exponentially more problems to solve.
Each successful agent deployment uncovers the next use case. More spend, more usage, deeper integration. The flywheel spins.
Counter-intuitively, buyers prefer this model:
- One contract instead of 10 vendors
- Freedom to experiment without stakeholder wrangling
- Transparent cost-to-value alignment
- No commitment to workflows that might become obsolete
The Context Graph
Here's the solution for GTM specifically.
We've been obsessed with data, intent signals, firmographics, technographics, website visits, call recordings. We have more data than ever.
But data isn't knowledge. The context graph is
A context graph is the connected understanding of everything happening with an account, structured in a way that agents can reason over.
It's not just that someone visited your pricing page, it's:
They visited pricing → after reading a competitor comparison → after their VP of Sales liked a LinkedIn post about your category → while their company is hiring 3 SDRs → and they're 6 months into a contract with your competitor
That's context. And it requires connecting:
- CRM data (deals, contacts, history)
- Website behavior (pages, time, patterns)
- Social signals (engagement, follows, shares)
- Intent data (research topics, competitor interest)
- Hiring signals (roles, departments, growth)
- News (funding, leadership changes, M&A)
All connected, all accessible to agents in a single tool call.
Most companies have the data, but almost nobody has the graph.
The Practical Path Forward
Here's what I'm doing right now. It's not theoretical - this is running today.
1. Connect everything to a single reasoning interface
For me, that’s a single reasoning interface connected to every data source: CRM, website analytics, Slack, call recordings, and intent signals. One place where all context is accessible.
2. Start with human-in-the-loop, capture the traces
Don't fully automate on day one. Run the process manually, but through the agent interface. When the output looks good, save the reasoning pattern. Build a library of "this is how we handle this situation."
3. Encode policies, not templates
Stop crafting email templates. Start encoding decision policies:
- "For Tier 1 accounts with pricing page visits, prioritize meeting-first outreach"
- "For closed-lost accounts re-engaging, acknowledge the history and lead with what's changed"
- "For technical personas, skip the business value pitch and go straight to integration"
The agent generates the specific content. You define the strategy.
4. Parallel execution
Once the policies are sound, scale horizontally. Five terminals. Ten agents. Fifty accounts per day, each getting genuinely personalized treatment based on their full context.
5. Measure ruthlessly, kill what doesn't work
Emails not working? Stop sending emails. Calls getting ignored? Shift to social. SDRs not adding value beyond what agents produce? Make the cut.
The goal isn't to automate for automation's sake. It's to get past the event horizon and see what actually moves numbers.
The Six-Month Window
Here's the uncomfortable truth.
Right now, almost nobody in GTM has their context graph built. Almost nobody has agents running in production. Almost nobody has the traces being captured.
In six months, the playbook will be obvious. Everyone will have agents connected to their own data. The baseline will be, “of course you have agents running your outreach.”
The window to build differentiation is now.
If you're reading this and thinking "we should start exploring this," you're already behind. The teams that will succeed are the ones treating this as their primary initiative, not a side experiment.
The Manifesto Question
I've noticed something about teams that successfully make this transition.
They have someone who writes manifestos.
Not product specs. Manifestos - documents that lay out an architectural thesis for where the future is going and why the organization needs to replatform to get there.
Most organizations don't have this. They have:
- A head of sales saying "just help me hit next week's number"
- A co-founder saying "I need baseline metrics for an exit"
- Engineers who need well-defined problem boxes
Without the manifesto writer - the person who intuits the abstract space and can translate it into organizational change - you can't replatform. You can only optimize what exists.
And optimizing what exists means staying on this side of the event horizon.
So here's my question for every GTM team: who's writing your manifestos?
What's Next
I don't have all the answers. Nobody does - we're building the plane while flying it.
But the framework is becoming clear:
1. Context graph: All data connected and queryable
2. Agent architecture: Blueprint → Responsibilities → Events → Tools → Scope
3. Decision traces: Every choice logged and debuggable
4. Orchestration layer: Coherence across all agents
5. Policy-based learning: Encode strategy, generate tactics
6. Usage economics: Itemized receipts, not workflow subscriptions
This is the architecture for what comes after workflows.
The question is whether you'll build it, or whether you'll watch someone else build it and wonder what happened.
If you're building in this space, I want to hear what you're discovering. If you think this is wrong, even better - the only way we figure this out is by pressure-testing these ideas against reality.
The event horizon is right there. Let's see what's on the other side.