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Engineering the AI Agent Runtime
Series: Enterprise AI Systems Architecture
Phase: — AI Agents
1. An Agent Needs a Runtime, Not Just a Prompt
An AI agent can call a tool, inspect the result, and choose another action. That flexibility creates a runtime problem: what prevents it from repeating a step, consuming the request budget, or continuing after the caller has cancelled?
The answer is not a longer prompt. It is an execution boundary with explicit contracts, resource limits, validation, and terminal outcomes.
The central principle is: the model proposes the next step; the runtime decides whether execution may continue. This keeps control flow and safety policy in trusted application code rather than relying on the model to follow instructions perfectly.
2. Reference Architecture
The runtime coordinates six responsibilities: prepare context, invoke the model, validate its decision, check policy and budgets, execute permitted actions, and produce a predictable outcome. Each responsibility should be independently testable.
