Design an Agent Execution Platform
An agent execution platform runs AI agents built by other teams. Teams provide the agent code, and the platform handles the infrastructure around it: scheduling runs, providing isolated sandboxes, preserving progress across failures, managing access to models and tools, requesting human approval, and recording what happened.
Some companies build this infrastructure around a durable workflow engine such as Temporal. Managed services offer another option.
Popular Examples: Amazon Bedrock AgentCore, Claude Managed Agents
Once a company has several agents in production, teams often start building the same infrastructure independently. One team writes retry logic for model calls. Another runs model-generated Python in a Docker container with unrestricted network access. A third keeps progress only in memory and loses unfinished work when a worker restarts. Meanwhile, nobody has a clear view of total spending or which agent called the payments API.
A shared platform handles these concerns for every team.
In this chapter, we'll design a platform that supports tens of thousands of concurrent agent runs. We'll start with requirements and a high-level architecture, then examine durable execution, sandbox lifecycle and warm pools, per-run credentials, and tracing across multi-agent runs.