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EvaluationExperimental
cloudbox
by acoyfellow
Cloud-backed synthetic environments for long-horizon agent evaluation
TypeScript
Updated Jul 27, 2026
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Summary
Simulates synthetic cloud computers to train and evaluate long-horizon agents on Cloudflare. Maps agent personas to filesystems, artifacts, collaborators, and runtime simulation so you can run realistic multi-step scenarios and gather retrospective traces. Uses Cloudflare Workers and Durable Objects to model persistent environments and A2A evaluation for reproducible inter-agent benchmarks.
Why It Matters
As agents delegate tasks and interact over long horizons, synthetic yet realistic environments are needed to reveal failure modes and trust signals. See the Evaluation-Driven Development (EDDOps) pattern. Cloudbox provides a controlled cloud-backed sandbox where agent behavior, state, and artifacts are observable and repeatable—critical for measuring agent track record and Model Context Protocol (MCP) Pattern for agent-to-agent evaluation.
Target Use Cases
Researchers and engineers who need reproducible, persistent simulations to stress-test multi-agent interactions and measure agent track record.
Applications
- Model long-horizon collaboration where agents create and modify persistent artifacts
- Stress-test delegation and failure modes in a cloud-backed simulated filesystem
- Collect retrospective traces and metrics for agent-to-agent evaluation and reputation scoring
Works With
cloudflarecloudflare-workersdurable-objectstypescriptrepkit
Topics
agent-evaluationai-agentsalchemycloudflarecloudflare-workersdurable-objectsevalsynthetic-data
Similar Tools
agent-playgroundagent-arena
Keywords
multi-agent trustA2A evaluationagent-to-agent evaluationagent track record