Coordination

why runloop

Ephemeral devboxes aren't built for multi-step workflows

When a run ends, its state evaporates. For a single-agent run, that is sufficient. When the task completes, the environment is torn down because nothing needs to persist. Multi-step systems need state that outlives the run. The moment agents have to share state, pick up where they left off, or react to external events, ephemeral devboxes hit a wall. Coordination closes that gap.

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Power Background and Event-Driven Agents

A complete agent sandbox does three things: define and provision the environment, control its state across runs, and run anything the agent needs inside it. Runloop ships each as a composable primitive, so you can use one on its own or wire them together.

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Devboxes: Hardware-isolated microVMs with full system access and sub-second startup. Your agent gets a real machine to work in, with nothing it breaks reaching anything else.

Blueprints: Your devbox environment as code: a Dockerfile, system setup commands, code mounts, build args, secrets, named contexts, and network policie.

Together, they define what your agent agent's environment looks like and provision it on demand

Configurable

Query the stream from any control surface: UI, API, CLI, SDK

Scale

Production-scale durability and availability

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Suspend-Resume for Autonomous Agent Workflows

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Durable Event Streams

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Streamlined Surface Area:
Create a session, publish an event, subscribe to the stream. Hold the whole surface in your head.

Same Calls no matter the workload:
The surface stays the same no matter how complex the worflow is

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Sentry Alert Triage

A Sentry exception wakes an agent that investigates the failure and hands back a diagnosis, then goes right back to sleep when it is done. It burns no compute while it waits and wakes with full context intact, so triage runs on demand and costs nothing between alerts.

Sentry fires an exception alert

The agent wakes on the event, no one paged.

Agent investigates the failure

Pulls the stack trace, recent error history, and the failing code path. Checks last three deploys to see if the same issue occured before.

Hands back a diagnosis

It posts to Linear or Slack with a proposed root cause and candidate fixes.

Suspends until the next alert

Full context persists with zero compute burn, ready to wake and pick up where it left off.

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MCP Hub

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Validate Agent Behavior Across Multi-Step Workflows

MCP Hub

When an agent is tasked with resolving a GitHub issue, it makes dozens of sequential decisions: which files to read, what edits to make, which tests to run, whether to retry a failed command. A single misstep at step 14 can invalidate the preceding 13 steps. Runloop captures the complete decision trajectory so you can pinpoint exactly where agent reasoning diverges from expected behavior.

Sentry fires an exception alert

Full execution path recording with decision-point annotations

Replay and modify

Change parameters at any step and re-run from that point

Hands back a diagnosis

Run each scenario N times to distinguish real failures from noise

Regression detection

Compare current agent against baseline across identical scenarios

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Everything You Need to Know

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The Execution platform for AI Agents

Ephemeral devboxes aren't built for multi-step workflows