The Dual Nature of Forward Deployed Engineering

At Decagon, forward deployed engineering is not a secondary support function; it is a core product development engine. The role is split into two distinct but overlapping responsibilities:

• Agent Configuration: This involves training the AI agent on specific enterprise requirements—defining brand tonality, setting handoff rules for human intervention, and configuring backend integrations. This is often handled via UI-based tools.
• Product Engineering: Forward deployed engineers act as the front line for customer pain points. When a Fortune 20 client requests a feature, the engineer must recognize that this is a product requirement that will likely be needed by future customers. Consequently, the line between "forward deployed" and "product" is effectively non-existent; they share the same reporting structure and quality bar.

Scaling Through Restraint and Upstreaming

As the company grew from 50 to 500 employees, the approach to custom work evolved. The primary challenge is avoiding the temptation to use AI coding tools to build "brittle" one-off patches for specific clients.

• The Principle of Restraint: The most valuable skill for an engineer is knowing when not to build a custom hack. Instead of creating a unique solution for one client, engineers must architect solutions that can be generalized.
• Custom Becomes Self-Serve: The core ethos is that every custom integration must be upstreamed into the platform. If an engineer builds a custom CRM integration for the 25th time, the team shifts to building a self-serve version. This ensures that the agent compounds in capability with every new deployment, allowing future customers to inherit existing features for free.

Proving Value and Acting as an Advisor

Trust in multi-year enterprise partnerships is won in the first few weeks. To succeed, the team focuses on two key strategies:

• Defining Success in Writing: Before writing code, the team forces a clear, written agreement on what success looks like—specific metrics, channels, and pain points. This prevents scope creep and miscommunication.
• Advisory vs. Execution: While the team is responsible for executing customer requests, they also act as advisors. By analyzing historical support data across multiple companies, they can identify which automations will yield the highest ROI, even if those weren't the specific requests the customer initially made. This domain expertise allows them to guide enterprises toward more effective implementations.