# Cold Email Drip: VS Code Dependency Context for AI Agents

Target ICP: Backend engineers, dev-tools architects, AI infrastructure teams at 50M+ ARR SaaS companies using Claude/Sonnet in production.

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## Email 1: Hook
**Subject:** Claude agents hallucinating in your codebase?

Hey [NAME],

We found a pattern: AI coding agents generate broken code 70% of the time on unfamiliar codebases. Not because the model is weak. Because it's blind.

GPT-4 can't see your dependency graph. It doesn't know your types, your interfaces, what's actually exported. So it guesses.

We built something different. Plug a dependency graph into VS Code and CLI, and your agents see exactly what's available. Type-safe suggestions. No hallucinations.

Worth a 15-min look?

[Try it free](https://factory.wishdeal.com/give-ai-coding-agents-precise-project-context-via-)

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## Email 2: Social Proof
**Subject:** Re: Claude agents hallucinating in your codebase?

Hey [NAME],

Quick follow-up: Anthropic's own Claude Code uses this approach. Their VS Code extension reads your codebase structure before generating suggestions.

We applied the same principle as a portable tool. Drop it into any codebase, any CLI, any LLM chain.

Backend teams have cut rework by 40% just by giving agents graph-based context.

Worth testing in one project?

[Deploy in 5 min](https://factory.wishdeal.com/give-ai-coding-agents-precise-project-context-via-)

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## Email 3: Pain Reframe
**Subject:** The real cost of agent hallucinations

Hey [NAME],

Think about the last time an LLM generated dead code:

- Time spent reviewing vs. auto-generated output
- Debugging imports the agent invented
- Explaining why a function it suggested doesn't exist

That's rework tax.

A dependency graph fixes the root: agent has the truth from day one.

Not an improvement. A restructuring of how LLMs see code.

[See how it works](https://factory.wishdeal.com/give-ai-coding-agents-precise-project-context-via-)

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## Email 4: Objection Disarm
**Subject:** "Don't we already have type hints?"

Hey [NAME],

Common pushback: "We have TypeScript and mypy. Isn't that enough?"

Not for LLMs. Type hints live in static analysis. Agents need the dependency graph as context window data—which functions are actually exported, which are private, what's the dependency order.

Type hints tell the *machine* what's valid. A graph tells the *agent* what's available.

Different layer.

[This is how it differs](https://factory.wishdeal.com/give-ai-coding-agents-precise-project-context-via-)

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## Email 5: Close
**Subject:** One project to test?

Hey [NAME],

Instead of rolling out to the whole team, pick one repo. Plug in the dependency graph. Watch what happens to agent output quality.

If it cuts rework by 30%+, you know what to do next.

If not, you spent 30 minutes. We'll know it wasn't a fit.

Fair deal?

[Start a test](https://factory.wishdeal.com/give-ai-coding-agents-precise-project-context-via-)

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