Question 1

What is the difference between an AI agent and agentic AI?

Accepted Answer

An AI agent is a single program that uses a model to decide, call a tool, and act toward a goal. Agentic AI is the broader system-level property where one or more agents operate autonomously over time: planning, using tools, coordinating with other agents, handling failures, and pursuing a goal with minimal human prompting. In short, an AI agent is a building block, and agentic AI is the autonomous behavior of the whole system.

Question 2

How do multi-agent systems work on AWS?

Accepted Answer

A multi-agent system on AWS splits a task across specialized agents (for example a planner, a researcher, and an executor) that share state and call tools. On AWS they are typically built on Amazon Bedrock models with an orchestration framework such as Strands, communicate over the Model Context Protocol, run on AWS Lambda or containers, and use a read-only IAM model with per-agent cost and trace tracking for safety and observability.

Question 3

Is agentic AI production-ready on AWS?

Accepted Answer

Yes, agentic AI can run in production on AWS when it is built with guardrails: least-privilege or read-only IAM, human approval for changes, durable workflows that retry and resume, tracing for latency, tokens, and cost, and governance that can block unsafe actions. These controls turn an experimental agent into a dependable production system.

Question 4

How do you control the cost of agentic AI systems?

Accepted Answer

Control agentic AI cost by tracking tokens and tool calls per agent, choosing right-sized models, caching repeated results, capping the number of reasoning steps, and alerting when a single agent's spend spikes. Per-agent cost attribution is key because in a multi-agent run one misbehaving agent can quietly dominate the bill.

Agentic AI: Autonomous Systems on AWS

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