DevOps gave us a shared vocabulary for shipping software reliably. MLOps did the same for models. Neither one maps cleanly onto agents — systems that don't execute predetermined workflows, that reason across multiple tools, that spawn sub-agents mid-task and accrue costs in non-linear ways. AWS just published a framework for what comes next, and Amazon Bedrock AgentCore is the infrastructure it runs on.
The Problem Nobody Had Good Words For
Standard software fails in predictable ways. You write tests, you catch regressions, you trace errors back to specific lines. Agents fail differently. The same input produces different outputs depending on memory state, tool availability, and whatever the LLM decided to weight this time. When a multi-agent chain produces a wrong answer, figuring out which agent, which tool call, and which decision point introduced the error is genuinely hard.
The cost problem is equally structural. A single user request can fan out across hierarchical agent chains or collaborative swarms, each one calling tools and spinning up compute that wasn't budgeted for. You can't rate-limit an agent the way you rate-limit an API endpoint — the agent decides how many calls to make.
