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AWS CEO Matt Garman predicts AI agents could eventually have an impact on businesses comparable to the internet or cloud—but that is a forecast, not evidence that companies are already seeing returns on that scale. Speaking at AWS re:Invent in Las Vegas, he argued that agents could move AI toward material business results even as many customers had yet to see returns matching AI’s promise. (ITPro, 2 December 2025)
What did Matt Garman say about AI agents?
Garman said: “This change is going to have as much impact on your business as the internet or the cloud.” He was referring to the expected effect of AI agents on business, not claiming that their impact has already matched those technologies. He also described agents as a possible turning point in AI’s trajectory: “I believe that the advent of AI agents has brought us to an inflection point in AI’s trajectory.” (ITPro, 2 December 2025)
The distinction matters: Garman acknowledged that many customers had not yet seen returns commensurate with AI’s promise. His argument was that agents could change that, not that a broad business payoff had already been demonstrated.
How might agents differ from chatbots and copilots?
A chatbot or copilot generally responds to a person’s prompt, often by generating information or helping with a task. An agent is intended to pursue a task through a sequence of actions, potentially using tools and business systems along the way. That can make an agent useful for longer workflows, but it also makes its autonomy, access and oversight important.
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To assess whether an agent is more than a conversational interface, a business should ask:
- Autonomy: What actions can it take without approval, and which require a person to confirm?
- Task scope and duration: Can it complete a multi-step process, or does it handle only a narrow request?
- Integration: Which business data and systems can it access, and under what permissions?
- Controls and security: Can the organization constrain, inspect and audit its actions?
- Measured return: Does it improve a defined business outcome enough to justify implementation and oversight?
These are practical comparison criteria, not a standardized scorecard: the cited event coverage and analyst commentary do not provide a common evaluation of agents across vendors.
What did AWS connect to its agent strategy?
ITPro’s live coverage of re:Invent, updated 3 December 2025, described several AWS announcements and offerings connected to the company’s agent strategy. The coverage presented them as product context, not proof that agents have delivered internet-scale business impact.
| Offering | Role described in the 3 December 2025 event coverage |
|---|---|
| AgentCore | A service for building and deploying agents, with policy controls and evaluations intended to constrain and inspect behavior. |
| Amazon Quick | Analysis and workflow automation. |
| Kiro | Software development. |
| Security Agent | Development security and penetration testing. |
| DevOps Agent | Investigating incidents. |
| AWS Transform | Legacy and mainframe modernization. |
These descriptions reflect the event coverage at that date. Product names, features and availability can change; consult ITPro’s live coverage and current AWS documentation before treating the announcements as a guide to what is available now.
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Do businesses already see returns on AI agents?
The sources behind Garman’s forecast do not establish that AI agents have produced business returns comparable to the internet or cloud. ITPro’s event coverage relayed Garman’s claim that more than 100,000 companies used Amazon Bedrock and that more than 50 customers had processed over one trillion tokens through it as of the 2 December 2025 keynote. Those are AWS executive claims reported at the event; they are usage figures, not independently audited measures of agent-driven business impact. (ITPro, updated 3 December 2025)
Adoption maturity is also disputed. In a 5 December 2025 analysis, TechCrunch quoted Forrester principal analyst Naveen Chhabra saying: “Most enterprises are still piloting AI projects and are rarely at the levels of maturity AWS expects them to be to take advantage of the offerings that come out of these announcements.” That is an analyst’s assessment, not a measured sector-wide adoption rate. (TechCrunch, 5 December 2025)
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TechCrunch also cited a widely discussed MIT study from August that reportedly found 95% of enterprises were not seeing an AI return on investment. That figure is secondhand in the cited article and is not independently established by the reporting cited here, so it should not be treated as a verified measure of current agent outcomes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should businesses judge the forecast?
The internet-or-cloud comparison sets an ambitious bar, but the cited reporting provides no direct statistic comparing agents’ business impact with the historical impact of either technology. The useful test for a business is therefore not whether an agent sounds transformative, but whether a particular deployment reliably improves a specific workflow.
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- Choose a bounded process. Identify a task with a clear owner, inputs, desired result and acceptable error rate.
- Define human control. Decide which steps an agent may take independently and where approval or review is required.
- Check access and safeguards. Limit system and data permissions to what the task requires; determine how actions can be inspected and corrected.
- Measure the outcome. Compare the agent-assisted process with the existing one using relevant measures such as completion time, accuracy, cost or service quality.
- Expand only on evidence. Broaden use when results are repeatable and the organization can support the agent’s security, integration and oversight needs.
This makes the forecast testable at the level that matters to an individual organization. A successful pilot may justify further deployment, but it does not by itself demonstrate economy-wide impact on the scale Garman predicts.
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