GTM had real AI usage. People were getting value in chat windows and then pasting the output somewhere by hand. Every win stayed local, and the effort of moving information between systems ate most of the gain.
The request was
“Roll out more AI tools to the GTM team.”
Discovery showed
More tools would have added more copy and paste. The friction was between the systems, not inside them. Context lived in Notion and Salesforce, conversation lived in Slack, and the model had access to neither at the moment it was needed.
The goal I set was radical frictionlessness: the work reaches the right person or agent with the context already attached, and the result writes back where the next step happens. That is an architecture problem, not a licensing one.
- Mapped where GTM context actually lives and which systems can read and write it.
- Traced handoffs to find the points where information was retyped rather than passed.
- Tested MCP connections and skills against real tasks before committing to a pattern.
- Set autonomy levels per task class so write-back never outran trust.
Exhibit
The Operating Architecture
01Surfaces
Slack and Cowork, where the work is already being discussed
02Agents & skills
Reusable skills per task class, with defined autonomy
03Context (MCP)
Notion, CRM, and data sources connected as first-class context
04Write-back
Results land in the system of record, not in a thread
05Notifications
The next human is told, with the reasoning attached
Write-back is what turns a helpful answer into a completed step.
Exhibit
Autonomy by Task Class
| Context needed | Write access | Human role | |
|---|---|---|---|
| 01Draft and summarize | Thread + docs | None | Reviews output |
| 02Assemble and enrich | CRM + Notion | Scoped write | Spot checks |
| 03Route and notify | Ownership map | Write + notify | Handles exceptions |
| 04Commit a change | Full record | Audited write | Approves first |
- 01An MCP-connected context layer so agents read from the real systems of record.
- 02Reusable skills for recurring GTM task classes rather than one-off prompts.
- 03Write-back and notification paths that close the loop into Notion, CRM, and Slack.
- 04Autonomy levels tied to task class, with evaluation before any level increase.
- Architecture
- Designed the connected system and chose the integration patterns.
- Build
- Configured MCP servers, skills, and write-back paths hands on.
- Adoption
- Worked inside GTM workflows rather than shipping a tool and leaving.
- Guardrails
- Defined autonomy levels and the evidence required to raise them.
- AI moved from isolated chat sessions into the GTM workflow itself.
- Handoffs that used to require a human copying context now carry it automatically.
- New use cases plug into existing context and write-back instead of starting from zero.
- Autonomy became a decision with criteria rather than a preference.
The interesting engineering was not the agents. It was the plumbing that let them see the truth and change it safely. Nobody demos plumbing, and it is the entire difference between a pilot and an operating system.