Our mission is to bring GTM into the agentic era.
Everyone is racing to ship revenue agents. Almost nobody asks the two questions that matter: can we trust them, and how would we know?
Go-to-market work is limited by human attention.
Every revenue team has work that nobody has time for. Someone must read six systems before a discovery call. Someone must examine every open deal and every renewal each week, and find the one that went quiet. Someone must keep the plan current.
People do this work badly, and they are not careless. The work never ends, so they do the most urgent part first. The part they never reach is the part that becomes a problem.
It is important to know which work this is, because people are anxious about it. Agents do not take the work a team already does well. Agents do the work that was too hard, too slow, or too large to do at all. The account nobody reached. The renewal nobody checked. The analysis that takes a week and never ran.
An agent does not get bored, and it has no limit on hours. It can monitor everything continuously. It can assemble the context before a person asks for it. It can act at the moment something changes.
The larger change is the limit itself. A team with agents can try ten plays where it tried one before, and then see which plays moved the number. The limit is no longer how much a team can do. The limit becomes how well a team can decide. That is why everyone is racing.
A better model does not correct this.
Ask raw CRM and billing data how the quarter is pacing. The answer is confident, specific and wrong. Nothing in the answer shows that the model guessed. Confident is not the same as correct. An agent that makes a thousand decisions each day on a bad number is wrong a thousand times each day.
Models will continue to improve, and that does not correct this. An agent in production needs eight parts: a goal, instructions, a model, tools, context and memory, limits, a measure of success, and an owner. The model is one part of eight. If a part is absent, you have a demonstration and not a product.
Much of what vendors sell as an agent is a chatbot or an automation with a new label. Gartner calls this practice agent washing. The cause of the failures is not weak technology. The causes are unclear value, weak data, and absent controls.
MIT NANDA, across 300+ publicly disclosed deployments.
The easy conclusion is that the models are not good enough yet. They are. Hold the model still and change only what it stands on.
Same Claude. Same monthly business review. Two foundations.
The model was not the variable.Read the field report
The gap is below the agent, not inside it.
A capable model can find much of this by itself. Give it five systems and ask which records are the same company. It will do a fair job. The question is not whether the model can do it. The question is whether you want it to guess again on each request.
A guess gives a different answer each time you ask. It is wrong some of the time, and nothing in the answer tells you which time. You pay for the guess in tokens and in delay on each call.
A guess also leaves nothing to inspect. When an agent acts on a number it calculated in the moment, no record shows how it got there. You cannot find the mistake later. A better model guesses better. It still guesses each time.
Resolve the same facts one time. They then become cheap, identical for every agent, and open to audit. One company is no longer a separate record in each tool. The plan has a place to live, so "how are we pacing" has a denominator. A picklist value called SQL becomes a real definition of SQL.
This is not a data quality problem to correct one time. It is a layer that does not exist yet.
Vasco is the GTM brain for AI agents.
Vasco is the revenue context layer that lets go-to-market agents run in production. The brain is what agents know. It holds the plan, the context and the numbers. Vasco resolves and defines them one time, for every agent above it.
The harness is how agents are allowed to act. Every agent runs inside the harness. Every agent owns an outcome. Every agent answers for each number it acts on.
The brain answers the first question: can you trust them. The harness answers the second: how do you know. One brain serves many agents, Vasco’s own or any model over MCP, so one question gets one answer. You rent the model. You own the memory.
Vasco also adapts. Every revenue engine is different, which is why templates plateau: a template is somebody else’s engine. We hold the structure, and the customer holds the shape. A machine-readable plan and shared definitions have to exist. What they say is the customer’s to decide. A funnel that looks like no other is their engine, not a data error to correct.
Growth should be engineered, not hoped for.
We held this belief before agents existed. It was the right idea at the wrong time. The work was too large: resolve the context, keep the plan current, measure what moved. No team can do that by hand, each week, without end.
Agents change the arithmetic. They are the first tool that can do this work continuously, at the scale a real engine needs. We hold the belief more strongly now.
The result is not a better dashboard. It is a team that can see what its own engine does. The team can change one thing and then know if the change worked. The engine is theirs, and not a copy of somebody else’s.
This needs agents that a team can trust. It also needs a way to know when the team must not trust them. That is the work. That is why this page opened with those two questions.