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Marketing has a problem — there’s no unit test for human emotion, so brute-force iteration can burn your brand and your audience. The solution is an Agent Context Graph: continuously capture customer state, interventions, and results, then simulate counterfactuals so agents can iterate in the dark. Done right, you get verifiable-style optimization for marketing, with zero cold start and compounding revenue.
Isaac Newton wasn’t crazy, he was just early.
He and his peers were obsessed with alchemy: the transmutation of base metals into gold. They viewed it as magic. We view it as a physics problem.
If you look at AI in the limit, we have finally solved the transmutation problem. We are turning sunlight into thought and action.
We have a massive fusion reactor in the sky called the sun. We capture that energy, push it through silicon, and generate intelligence. We are moving from a world of scarce human processing to an abundance of synthetic thought.
Here is why that changes everything for the C-Suite.
Engineers love to complain about AI agents: "Look how many times it failed before it got the right answer! It burned 40,000 tokens! It’s inefficient!"
In an economy of scarcity, efficiency is king. In an economy of abundance, efficiency is a rounding error.
If you go to sleep and an Optimus robot takes five hours to do your dishes instead of five minutes, do you care? As long as the dishes are clean when you wake up, the "efficiency" is irrelevant. The cost of the energy is negligible compared to the value of the outcome.
The same logic applies to your enterprise. Spin up 20 agents against 10 coding tasks. Let them fail. Let them iterate. As long as they have a clear definition of done, the work is finished while you sleep.
2026 is the year we solve this for verifiable domains.
Coding, math, and gaming are deterministic. You can write tests. If the code compiles and passes the unit tests, the agent succeeded. The agent can hallucinate 50 times, but the compiler is the final judge. This is called a verifiable domain.
Marketing is not a verifiable domain.
You cannot write a unit test for human emotion. If you apply the "brute force" method to marketing — sending 1,000 bad emails to find the one that works — you aren’t just "inefficient." You are burning your total addressable market.
You cannot test sub-optimal messages in production without destroying your brand equity.
So, how do we turn marketing into math?
To make marketing verifiable, you need a simulation of reality. You need causality data.
We don't test on the customer; we test on the context graph.
Snapshot the state: continuously record the customer’s state, every intervention received, and the result. This causality data is the core of the agent context graph.
The counterfactual: Ask the agent, "What happens if we send X?"
The simulation: The agent uses the graph to predict the outcome based on causal history, not just language probability.
This allows you to tell an agent: "Write a message that converts this prospect. Iterate against the Context Graph until you maximize the probability of conversion. Do not email them until you hit a confidence threshold of 95%."
Now, the agent can burn 100,000 tokens simulating the conversation in the dark. It optimizes against the graph, not the customer’s patience.
When you plug this into AI decisioning, you achieve something previously impossible: Guaranteed optimization with zero cold start.
Most decisioning systems need months of data to "warm up" during their "explore phase." A causal agent context graph starts optimizing immediately because it understands the physics of the relationship, not just the history of the clicks.
Newton wanted to turn lead into gold.
We are turning sunlight into compounding revenue.
Let’s build.
