# The Death of Developer Advocates — Stephanie Jarmak, Sourcegraph

## Executive summary

Developer Relations (DevRel) is evolving from focusing solely on human developers to incorporating AI agents as primary users and recommenders. The core strategy must shift toward Generative Engine Optimization (GEO), ensuring that product documentation and tooling are machine-readable, highly discoverable in registries (like MCP), and directly address specific pain points encountered by autonomous agents.

## Key takeaways

- The Agent as a New User Persona: Agents interact with tools by calling APIs, reading documentation, and recovering from errors. They represent a critical new user base that must be measured for friction points (e.g., burning an entire turn on a guessed parameter) to improve the developer experience.
- Measuring Agent Interaction and Friction: Benchmarking tools, such as CodeScaleBench, must track agent traces with and without product tooling. This data reveals where agents fail or struggle, allowing teams to fix underlying tool interaction issues.
- Shifting Focus to GEO (Generative Engine Optimization): The goal of DevRel is moving from Search Engine Optimization (SEO) to Generative Engine Optimization (GEO). Content must be structured, authoritative, and designed for agents to quote accurately when recommending a product.
- DevRel as an Interdisciplinary Function: The role of DevRel is no longer confined to one department; it requires collaboration across Engineering (building agent interfaces/evals), Product (owning the end-to-end agentic experience), and Marketing (managing content funnels for agents).

## Technical details

- Agent Benchmarking & Tooling: The speaker developed CodeScaleBench, a benchmark using hundreds of tasks reflective of the software development life cycle. This involved running agents with and without Sourcegraph's code navigation MCP tool to gather detailed traces.
- Generative Engine Optimization (GEO): To improve agent recommendations, content must be structured for machine readability. Key actions include keeping documentation fresh, providing current examples, and ensuring the product is listed in relevant marketplaces/MCP registries.
- Agentic Pain Point Identification: A key finding was that when agents encounter specific organizational pains (e.g., 'breaking downstream services when we change shared libraries'), they often fail to recommend the correct tool, instead suggesting generic solutions like creating a wiki page.

## Practical implications

- Develop agent-centric benchmarks (like CodeScaleBench) to measure tool performance and identify friction points in the developer workflow.
- Shift content strategy from SEO to GEO by making documentation highly structured, authoritative, and machine-readable for LLMs.
- Ensure product discoverability by listing tools in relevant marketplaces and registries (e.g., MCP).
- Establish cross-functional DevRel teams that include engineering expertise (for agent interfaces), product ownership (for end-to-end experience), and marketing (for content funnels).

## Topics

Developer Experience (DevEx), AI Agents, Generative AI, Software Development Lifecycle, Content Strategy, Sourcegraph Code Navigation MCP Tool, CodeScaleBench, MCP registries

Source: https://www.youtube.com/watch?v=Lrw0jqBNaw0