GTM context tools (2026): how to give AI agents and reps the same context

Our context system is a set of plain markdown files in a git repo, read by Claude Code, with live data pulled from the tools below. Every fact an agent uses, from the ICP to which client results we can quote, lives in one file with its source next to it.

These vendors package context as a product. They save you the build, and in return your ICP and messaging live in their system.

A GTM context system is the shared, structured record of how your company sells, kept somewhere both people and AI agents can read it. Every outbound email, account brief, and call prep an agent produces should start from it.

Most of this knowledge already exists. It is spread across a positioning deck, the founder's head, a few Notion pages, the CRM, and a year of call recordings. The work is pulling it into one place, writing each fact once, and recording where it came from so agents do not invent or repeat stale claims.

Buying a platform like Octave or Common Room gets you a working context layer quickly, with integrations, agents, and support. The trade-off is that your ICP and messaging live in a vendor's format, and you pay a platform fee whether or not every team uses it.

Building one from your CRM, call notes, and a set of documents costs less in software and keeps everything readable and portable. The trade-off is that someone has to design the structure and keep it current. Teams with a GTM engineer, or that already use Claude Code, tend to build. Larger teams with many agents across sales and marketing tend to buy.

Either way, start small. Write the ICP, disqualifiers, voice, and proof points first. Those four cover most of the mistakes agents make in outbound.

context-os is a markdown knowledge graph we build in each client's repo. We start from their website, CRM, docs, and call recordings, and turn them into short files, one per idea: the ICP, each persona, each signal, each play, the proof points, the voice rules, and the lessons from what has gone wrong. Files link to each other, and an index tells an agent where to start.

Each file records where its facts came from and how confident we are, marked as verified from a source or inferred. Signals carry a freshness window so an agent knows when a fact has gone stale. Facts flow one way, from the graph into copy, pages, and plays, so when something changes it changes in one file.

Claude Code reads the graph before it writes outbound, scores accounts, or builds a page. Attio, Granola, and Deepline keep the live data current. It is plain text in git, so the client owns it, can review every change, and can point any agent at it. Read more about how we use Claude Code for GTM.

Most teams start using AI agents for outbound before writing down what the agents should know. The output is generic, and nobody can say which claim came from where.

OneGTM builds the context system first, in your own repo and tools, then wires the agents and plays to it so every message and account brief starts from the same facts.