AI Agent Runtime Architecture

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use When

• Standing up an agent runtime as a control-plane service, not as a function inside a web request.
• Deciding whether the work is an agent (LLM plans), a deterministic workflow (no LLM in the loop), or a cron (scheduled).
• Designing the state machine so a crashed worker can resume a 30-minute task without re-charging the customer.
• Adding step caps, wallclock caps, and idempotency so a runaway agent cannot spend USD 40k of provider budget overnight.
• Wiring agent task lifecycle events (agent.task.started, agent.step.completed, agent.task.paused, agent.task.killed, agent.task.completed) for observability and back-office.

Do Not Use When

• The task is the fundamentals of the ReAct loop / tool contract — ai-agents-tools.
• The task is the multi-agent coordination pattern — ai-agent-multi-agent-coordination.
• The task is long-running (hours-to-days) durability and progress UX — ai-agent-async-and-long-running-tasks builds on this skill.
• The task is the overall AI architecture — ai-on-saas-architecture.