AWS unveiled "frontier agents" at re:Invent 2025 (Dec 3 - 5) - a new class of AI agents that work autonomously for hours or days without human oversight. This isn't a chatbot. These agents actively solve enterprise problems: the Kiro autonomous developer can write and test code; the AWS Security Agent conducts automated penetration testing; the AWS DevOps Agent investigates production incidents and recommends fixes - all while your team sleeps.

The key shift: Amazon Bedrock AgentCore, a managed service that acts as an operating system for agents, abstracts away the infrastructure complexity that previously forced teams to spend months building custom agent scaffolding. Session isolation, context management, memory persistence, and tool governance are now built-in. The result: MongoDB went from six-month infrastructure projects to shipping production agents in eight weeks. The PGA TOUR generates 140 - 180 tournament articles per week with an agent pipeline, cutting content production cost by 95% while delivering articles within 5 - 10 minutes of tournament end.

Frontier agents represent a fundamental shift from "models that chat" to "agents that work." For enterprise technology leaders, this matters enormously.

1. Autonomous work shifts infrastructure economics entirely

Your on-call engineers, security teams, and content writers have a new class of always-on competition. AWS DevOps Agent investigates production incidents 24/7, correlating CloudWatch alarms, GitHub deployments, and system logs to identify root cause and recommend fixes - often in 15 minutes, work that takes human experts hours. AWS Security Agent proactively penetration-tests your applications as part of your CI/CD pipeline. If you're not stress-testing your incident response and security workflows against autonomous agents right now, you're underestimating your vulnerability window.

2. The infrastructure bet just changed targets

Previously, success with AI required massive engineering effort: building context managers, memory layers, tool orchestration, and observability from scratch. AgentCore eliminates that. Teams now compete on workflow design and tool integration, not infrastructure scaffolding. This means smaller teams can ship production agents, but it also means your team's competitive advantage shifts from "we have better infrastructure engineers" to "we understand our business processes well enough to decompose them into agent-solvable tasks."

3. Cost structure of knowledge work is about to collapse

The PGA TOUR example is the proof: 25 cents per article, 95% cost reduction, produced in minutes instead of hours. If your organization generates content, analyzes data, or investigates issues at scale, frontier agents will reshape your budget. The question isn't "can we afford to try this?" It's "can we afford not to?"

Pick one critical workflow where your team spends the most time on repetitive, decision-intensive work. Examples: security alert triage, production incident investigation, content generation, customer support ticket classification, or DevOps runbook execution.

Step 1 (5 minutes): Document your workflow. Map the steps your team actually takes: What data do they gather? What systems do they query? What decisions do they make? What tools do they integrate with?

Step 2 (10 minutes): Identify the tool integrations you'd need. AWS DevOps Agent, for example, integrates with CloudWatch, GitHub, ServiceNow, and Slack out of the box. If your workflow spans different systems, list them.

Step 3 (15 minutes): Run a proof-of-concept using AWS Bedrock AgentCore with one of AWS's pre-built agents (DevOps Agent for infrastructure, Security Agent for threat detection) or build a simple agent using Kiro's IDE. Give it a real incident ticket or alert.

Step 4 (document, 10 minutes): Run this 3 times. Log:

Expected outcome: Quantified evidence of where frontier agents create value in your organization, and where your team's domain expertise is still required. If the agent handles 70%+ of your workflow without human correction, you've found your first production use case.

Pro Tip: If your PoC agent achieves >80% accuracy on a high-volume, repetitive task, allocate a small infrastructure budget and assign one engineer to productionize it by Q1. The agents that launch first will set the baseline for your organization's expectations. The agents that launch second will inherit the operational lessons. Don't be third.

Getting Started with AWS Bedrock AgentCore

AWS DevOps Agent — Autonomous Incident Response

Amazon Kiro IDE — Spec-Driven Agent Development

Python / Boto3 Integration

All claims in this brief are sourced from official AWS documentation, verified case studies, and authoritative technical resources published in December 2025: