How Datadog built a “universal machine tool” for Claude Code

Summary

Datadog engineers now rely on AI coding tools for all production work, with Claude Code accounting for at least two-thirds of that activity. The tooling generates personalized workflows across the software development lifecycle, yet these workflows grow more complex to construct and harder to verify as agent involvement increases. What was once a direct mapping from engineer intent to code has become an indirect process in which engineers define the context, constraints, and evaluation criteria that agents then act upon.

This change effectively elevates engineers into roles that resemble management: they decide what information an agent receives, which tools it can invoke, how success is measured, and how failures are detected. Sessions powered by Claude Managed Agents can run for days, during which the agents autonomously create their own tools, glue code, and conventions. While this autonomy increases capability, it also widens the gap between agent-generated artifacts and the human-oriented interfaces and observability systems already in place at Datadog.

To address the resulting operational friction, the company built Temper. The system functions as a minimal structural layer—analogous to the jigs, fixtures, and gauges used in precision manufacturing—that lets agents produce consistent, inspectable components for mission-critical infrastructure such as databases. Rather than supplying agents with ad-hoc scaffolding, Temper supplies the smallest kernel of capabilities required for safe, repeatable construction and operation of large-scale systems.

Why it matters

This article provides crucial strategic insights for product teams and builders on managing autonomous AI agents in enterprise environments. It highlights the necessary infrastructure required to ensure safe, reliable agentic workflows, aligning with the Dutch market's emphasis on robust and transparent AI deployment.