According to AWS at this week’s re:Invent 2025, the chatbot hype cycle is effectively over, replaced by a new wave of frontier AI agents.
The message coming out of Las Vegas is blunt: the industry’s fixation on simple chat interfaces has given way to a more ambitious mandate “frontier agents” that don’t just respond, but operate autonomously for hours or even days, completing tasks end-to-end without human intervention.
We are shifting from the novelty stage of generative AI to a tougher era defined by infrastructure, economics, and operational complexity. The initial “wow” of a poem-writing bot has worn off; now the real cost is showing up the massive infrastructure required to keep these systems running at scale.
Addressing the plumbing crisis at AWS re:Invent 2025
Until recently, building frontier AI agents capable of handling complex, non-deterministic tasks was a bespoke engineering nightmare. Early adopters spent massive resources stitching together tools for context management, memory handling, and security.
AWS now wants to eliminate that complexity with Amazon Bedrock AgentCore a managed service that functions like an operating system for agents. It takes over the heavy lifting of state management and context retrieval, and the efficiency gains from standardising this layer are hard to overlook.
Take MongoDB, for example. By replacing their custom-built infrastructure with AgentCore, they streamlined their entire toolchain and launched an agent-based application in just eight weeks a process that previously took months of evaluation and upkeep.
The PGA TOUR reported even bigger gains, using the platform to create a content generation system that boosted writing speed by 1,000 percent while cutting costs by an astonishing 95 percent.
Software teams are getting their own AI workforce as well. At re:Invent 2025, AWS introduced three specialised frontier AI agents: Kiro (a virtual developer), a Security Agent, and a DevOps Agent. Kiro goes far beyond code completion it plugs directly into engineering workflows using “powers” (deep integrations with tools like Datadog, Figma, and Stripe), enabling it to act with real context instead of merely predicting syntax.
Agents that run continuously for days demand enormous compute power and if you’re paying standard on-demand rates, any meaningful ROI quickly disappears.
AWS is well aware of this, which explains the aggressive hardware push this year. The new Trainium3 UltraServers built on 3nm chips deliver a 4.4x boost in compute performance compared to the previous generation. For organisations training large foundation models, that kind of jump shrinks training cycles from months down to weeks.
But the more significant shift is where that compute now resides. Data sovereignty continues to be a major obstacle for global enterprises, often preventing them from moving sensitive AI workloads to the cloud. AWS is responding with its new “AI Factories” effectively delivering racks of Trainium hardware and NVIDIA GPUs directly into customers’ own data centres. It’s a hybrid strategy built on a simple reality: for certain data, the public cloud is still too far away.
Tackling the Burden of Legacy Architecture
Innovation around frontier AI agents is exciting, but most IT budgets are still choked by technical debt. Many teams spend nearly 30 percent of their time simply keeping systems running.
At re:Invent 2025, Amazon expanded AWS Transform to tackle this problem directly using agentic AI to automate the heavy lifting of modernising legacy code. The service can now manage full-stack Windows modernisation, including upgrades for .NET applications and SQL Server databases.
Air Canada used this approach to modernise thousands of Lambda functions and completed the process in just days. Doing it manually would have taken weeks and cost nearly five times more.
For developers who actually want to write code, the ecosystem is also expanding. The Strands Agents SDK, once limited to Python, now supports TypeScript. As the web’s lingua franca, TypeScript brings much-needed type safety to the unpredictable outputs of LLMs a necessary and overdue evolution.
In the age of frontier AI agents, sensible government
There is a real risk in all of this. An agent that can operate autonomously for “days without intervention” could also accidentally corrupt a database or leak sensitive PII before anyone notices.
AWS is addressing this with its ‘AgentCore Policy’ feature, which lets teams define natural language boundaries for what an agent can and cannot do. Combined with ‘Evaluations,’ which uses pre-built metrics to track agent performance, it creates a crucial safety net for autonomous operations.
Security teams also benefit from updates to Security Hub, which now consolidates signals from GuardDuty, Inspector, and Macie into single “events,” rather than overwhelming the dashboard with separate alerts. GuardDuty itself is being enhanced, using machine learning to identify complex threat patterns across EC2 and ECS clusters.
It’s clear we’ve moved beyond pilot programs. The tools unveiled at AWS re:Invent 2025 from specialised silicon to governed frameworks for frontier AI agents are built for production. For enterprise leaders, the question is no longer “what can AI do?” but rather, “can we afford the infrastructure to let it do its job?
