InfoQBen Linders4 min readintermediate
Building an Internal Developer Platform with Artificial Intelligence
Summary
This article discusses building internal developer platforms with AI agents that use semantic search across internal data sources like Git, Slack, and Jira. It highlights the need for guardrails to control agent actions and comprehensive observability via logs, metrics, and traces to understand agent behavior and improve developer experience.
- AI agents are emerging as the next generation of internal developer platforms, providing tooling by combining developer input with system context.
- Effective semantic search for agents requires proper data chunking, continuous ingestion for freshness, and distinguishing knowledge from transient state.
- Guardrails are crucial for AI agents to block or allow actions, potentially requiring human approval for sensitive operations.
- OpenTelemetry traces are essential for debugging agent behavior, understanding execution paths, and auditing actions, especially given non-deterministic LLM outputs.
Platform teams and engineers building internal tools should care about this approach to leverage AI for developer experience and gain deep insights into platform usage and agent effectiveness.
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