What is an agent runtime?
The execution environment that manages an AI agent's model calls, tools, state, memory, permissions, retries, streaming, events, observability, and lifecycle.
Why is this important?
A runtime turns an agent definition into a dependable process with scheduling, isolation, state management, recovery, and controls.
How it works
The runtime accepts a task, loads policy and state, invokes a model, validates and executes tools, checkpoints progress, emits traces, and stops or resumes.
Technical example
A support runtime suspends while waiting for a customer, resumes after a webhook, rotates providers, and preserves one audit trace.
Implementation notes
Design for durable execution, idempotent events, cancellation, timeouts, concurrency, secret isolation, sandboxing, versioning, budgets, and approvals.
Sources
https://www.anthropic.com/engineering/building-effective-agentshttps://modelcontextprotocol.io/docs/learn/architecture
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