Groq's mission is to deliver fast, low-cost inference that doesn't flake when things get real. The way Groq does this is through its purpose-built LPUs which are designed for inference, from chip to cloud. This same mission translates into a need to equip Groq's own engineers with state of the art coding agents that accelerate daily work.

In practice, most coding tools default to frontier-only models and are built for slow feedback loops, which makes it hard to mix fast, low-cost inference for everyday tasks with frontier intelligence when it matters. Groq feels this friction not just in the agents it helps power for builders, but within its own engineering organization.

Internally, Groq needed a developer experience that could:

• Switch models instantly as new proprietary and open-source models ship and work well with the leading open source models they host
• Keep iteration loops short enough to make AI feel collaborative
• Provide a coding agent that could be accelerated by their own fast inference

A state of the art model-agnostic agent harness in the CLI

Groq relies on Factory's Droid CLI for accelerating their engineering pace, dogfooding their own inference to build Groq with Groq, leveraging a plug-and-play, multi-model architecture that avoids lock-in to any one provider's endpoints or formats.

Klieger described Droid as unusually "complete" as a CLI experience: fast startup, auto-updates, thoughtful UX details (like completion sounds), and low perceived lag that reduces friction and encourages sustained use. And importantly, Droid delivers frontier-level quality which was non-negotiable for the team when choosing which coding agent CLI to use.

Pairing Droid with Groq inference to unlock new workflows

Paired with Groq's high-throughput inference, Groq's engineering team uses Droid to make previously marginal use cases part of everyday workflows:

• Codebase questions (where is X stored / which DB backs Y?)
• Rapid file discovery
• Small configuration changes
• Debugging instrumentation + telemetry updates
• CI failures and test coverage work

The adoption of Droid has also greatly accelerated the rate at which the Groq team can use its own inference with coding agents on daily tasks.

From 15-minute loops to 30-second answers

Klieger highlighted a common challenge in coding tools: long feedback cycles prevent developers from collaborating with agents as a collaborator for deep tasks.

Droid changes this dynamic for Groq's engineers by making it easy to get quick, low-friction answers. With Droid + Groq inference, tasks that take 10–15 minutes in slower loops can land in under a minute. Engineers can iterate interactively, guiding and refining results in real time.

Scaling engineering through parallel agents

"We care about speed, a lot. Minutes matter. Many of our engineers frequently use agents in parallel. Droid allows us to move faster and use parallelization during feature sprints, and dashing towards day 0 launches," Klieger states.

At Groq, engineers run multiple Droids in parallel, each pursuing a distinct line of inquiry or task. This parallelism lets teams advance feature sprints and other time-sensitive initiatives simultaneously, using fast, directional outputs as working material for fixes, pull request drafts, and review starting points. The result is sustained momentum which enables teams to move quickly when it matters most, without sacrificing clarity or control.

By combining Droid's model-agnostic harness with Groq's fast inference, Groq is unlocking both measurable speed gains and a deeper shift in how AI fits into engineering workflows.

The result is a developer experience that is faster than traditional frontier-only loops for many everyday tasks, more flexibility across models, and more scalable, as engineers can parallelize exploration and unblock themselves quickly.

Looking to accelerate your software development? Learn more about Factory.

*Comparing Droid powered by Kimi K2 on Groq to GPT-5 on Codex and Claude Code for on groq/openbench