Serverless Multi-Agent System

Autonomous AI agent platforms such as OpenClaw are increasingly deployed to serve long-running, interactive workloads that span multiple tool calls, model invocations, and external API interactions. However, existing deployments typically run agents on statically provisioned containers or fixed-size instances, leading to chronic resource inefficiency: agents sit idle between turns, blocking scarce GPU and memory when not actively reasoning, yet cannot acquire additional capacity on the spot when a complex multi-step task demands sudden bursts of compute. The lack of an elastic, cloud-native substrate for agent execution therefore caps concurrency, inflates serving cost, and prevents agents from scaling with workload demand.

This project proposes a serverless, cloud-native system architecture that allows autonomous agents such as OpenClaw to elastically acquire and release resources at fine granularity while preserving interactive serving semantics. The system decouples agent identity and conversation state from the underlying execution runtime, so that an agent can be suspended, checkpointed, migrated, and re-hydrated across serverless function invocations without losing context or incurring cold-start penalties on every turn. A reactive scheduler continuously monitors agent activity signals—pending tool calls, queue depth, model batch size, and context footprint—and triggers rapid scale-out and scale-in across a pool of serverless executors, charging for resources only during the active portion of each reasoning cycle.

A second focus is intelligent context sharing across agents. The architecture exposes a shared context layer through which cooperating agents can securely exchange intermediate reasoning artifacts, retrieved knowledge, tool outputs, and memory snapshots, so that multi-agent workflows avoid recomputation and redundant API spending. Context objects are versioned, access-controlled, and cache-aware, allowing downstream agents to reuse upstream work and enabling systems such as OpenClaw to coordinate a team of specialized agents as a single elastic entity. Experimental evaluation targets a reference deployment of OpenClaw-scale agents over a serverless container runtime, measuring serving cost, cold-start overhead, end-to-end task latency, and context-reuse hit rate under realistic multi-agent workloads.

Codebase

TBD

Publications

TBD