The Real Reason Your Agents Underperform
Most revenue teams standing up AI agents this quarter are debugging the wrong thing. They switch models, adjust prompts, add tools — and the output still feels generic. The actual problem is almost never the model. It's that the agent has no idea who your customer is, what you sell, or why deals close.
Zapier's GTM team figured this out the hard way. Lindsay Rothlisberger built what Jared Robin calls a "shared brain" — a structured context layer that every GTM agent the company runs pulls from before it does anything. ICP definitions, messaging frameworks, competitive positioning, historical deal signals — all of it organized and accessible as a foundation layer, not scattered across Notion pages and Slack threads.
The result: agents that produce usable output instead of noise.
What a Context Layer Actually Is
This isn't a RAG implementation or an AI project. It's an organizational decision about what your agents need to know and where that information lives.
A working context layer for a B2B revenue operation typically includes:
- ICP definition — not a marketing persona, but a precise description of who buys, why they buy, and what signals predict fit
- Messaging framework — your actual value props by segment, the language that resonates, the objections that kill deals
- Historical deal data — won/lost reasons, sales cycle patterns, deal sizes by segment
- Process context — how your team works, what handoffs look like, what "good" looks like at each stage
Without this, every agent you deploy starts from zero. It produces output that's technically coherent but commercially useless — the AI equivalent of a new hire who's never spoken to a customer.
The Audit You Should Run This Quarter
Before you add another agent, workflow, or automation, run a context audit:
- List every agent or AI-assisted workflow your GTM team runs today — outbound sequences, lead scoring, pipeline summaries, call prep, whatever.
- For each one, identify what context it has access to — literally, what does it know about your ICP, your product, your deals?
- Find the gaps — where is the agent working from generic assumptions instead of your actual business reality?
The gaps you find are your quality ceiling. No model upgrade fixes them. Building the context layer does.
The teams generating real pipeline from AI agents this year aren't using better tools than everyone else. They built the infrastructure first. Start there.