How We Cut Agent Latency by 63%

A deep dive into the infrastructure improvements that made Queue agents faster, more reliable, and significantly more efficient at scale.

As adoption of Queue grew, so did the complexity of the workloads running on our platform.

Agents were processing larger codebases, executing more tools, interacting with more integrations, and handling increasingly complex workflows. While the platform remained reliable, we knew there was an opportunity to dramatically improve execution speed.

Over the past quarter, our engineering team focused on a single goal: reduce agent latency without sacrificing reliability or visibility.

The result was a 63% reduction in average execution latency across the platform.

Understanding where the time went

When we first analyzed agent runs, we discovered that model inference wasn't the primary bottleneck.

Instead, a significant amount of time was spent coordinating tool calls, loading context, retrieving workspace resources, and managing execution state between steps.

Individual delays were small, but across dozens of actions per run they compounded into meaningful overhead.

Rather than optimizing a single component, we needed to rethink the entire execution pipeline.

Rebuilding the execution engine

The largest improvement came from redesigning how agents coordinate work internally.

Previously, many operations were executed sequentially even when they could safely run in parallel. By introducing a more intelligent task scheduler, agents can now perform multiple non-dependent actions simultaneously.

Context retrieval, tool preparation, and environment setup now happen concurrently whenever possible.

This reduced idle time throughout the execution lifecycle and significantly improved responsiveness for complex workflows.

Smarter context management

Large context windows are powerful, but they're also expensive.

We introduced a new retrieval system that dynamically loads only the information an agent needs for a specific task. Instead of injecting large amounts of workspace context into every execution, agents selectively retrieve relevant files, documentation, integrations, and historical runs.

This reduced token usage while improving response quality and execution speed.

In many cases, agents now make better decisions with less information.

Scaling for the next generation of agents

Performance improvements aren't just about speed.

Lower latency creates a better developer experience, enables more complex workflows, and allows agents to operate more effectively in production environments.

As customers deploy larger fleets of agents, execution efficiency becomes increasingly important. Every second saved compounds across thousands of runs.

The infrastructure changes we shipped this quarter provide the foundation for the next generation of Queue agents.

What's next

We're continuing to invest heavily in performance, reliability, and observability.

Upcoming improvements include faster workspace synchronization, smarter caching strategies, improved agent memory systems, and additional optimizations throughout the execution stack.

While users will notice the speed improvements immediately, we're even more excited about what they enable.

Faster agents create room for more ambitious workflows, deeper integrations, and increasingly autonomous systems.

The future of software is autonomous—and it needs infrastructure that can keep up.