That definition stuck because it describes what every production agent actually does. Kiro, Amazon Q Developer, Quick Agents, Codex, Claude Code: under the hood, they all run the same shape. The agent loop is the common denominator.

Pick a framework. Wire up tools. Provision sandboxed compute. Configure storage, secrets, networking. Decide where memory lives. Bolt on observability. Get the right dependencies into the right container. Also, local prototyping tends to be the easy part: a single developer can stand up an agent on their laptop in an afternoon. Getting it into production is where the work explodes, and the moment it has to serve more than one user, a whole new layer of work shows up: concurrency, isolation, identity, state, scaling.

Worse, that overhead multiplied with every new use case. Teams that wanted to experiment, try a different model, swap a tool, point the agent at a new domain, found themselves repeating the same plumbing. The bottleneck wasn’t intelligence. It was orchestration and infrastructure.

When we launched the AgentCore harness in preview in April, we made a bet: the AgentCore primitives (Runtime, Memory, Gateway, Browser, Identity, Observability) already give teams everything they need to run agents in production; what they shouldn’t have to do is wire them up by hand every time. The harness handles that wiring as a managed abstraction, so it becomes something you configure rather than something you build.

Today, Amazon Bedrock AgentCore harness is generally available. Two API calls (CreateHarness to define an agent, InvokeHarness to run it), a quick walkthrough in the AgentCore CLI (as shown in the below gif), or a few clicks in the console, and you have an agent running in minutes. It runs in its own isolated environment with a filesystem and shell, so it can read files, run commands, and write code safely. It remembers users and conversations across sessions, picks up skills you point it at (including the AWS-curated catalog), browses the web, calls your tools through gateway or MCP, and switches model providers mid-session without losing context. Every step streams back to you in real time and is automatically traced to CloudWatch. There’s no need to write orchestration code or build a container, except if you want to.