Amazon's new AI can code for days without human help. What does that mean for software engineers?

Amazon Web Services on Tuesday announced a new class of artificial intelligence systems called "frontier agents" that can work autonomously for hours or even days without human intervention, representing one of the most ambitious attempts yet to automate the full software development lifecycle.

The announcement, made during AWS CEO Matt Garman's keynote address at the company's annual re:Invent conference, introduces three specialized AI agents designed to act as virtual team members: Kiro autonomous agent for software development, AWS Security Agent for application security, and AWS DevOps Agent for IT operations.

The move signals Amazon's intent to leap ahead in the intensifying competition to build AI systems capable of performing complex, multi-step tasks that currently require teams of skilled engineers.

"We see frontier agents as a completely new class of agents," said Deepak Singh, vice president of developer agents and experiences at Amazon, in an interview ahead of the announcement. "They're fundamentally designed to work for hours and days. You're not giving them a problem that you want finished in the next five minutes. You're giving them complex challenges that they may have to think about, try different solutions, and get to the right conclusion — and they should do that without intervention."

Why Amazon believes its new agents leave existing AI coding tools behind

The frontier agents differ from existing AI coding assistants like GitHub Copilot or Amazon's own CodeWhisperer in several fundamental ways.

Current AI coding tools, while powerful, require engineers to drive every interaction. Developers must write prompts, provide context, and manually coordinate work across different code repositories. When switching between tasks, the AI loses context and must start fresh.

The new frontier agents, by contrast, maintain persistent memory across sessions and continuously learn from an organization's codebase, documentation, and team communications. They can independently determine which code repositories require changes, work on multiple files simultaneously, and coordinate complex transformations spanning dozens of microservices.

"With a current agent, you would go microservice by microservice, making changes one at a time, and each change would be a different session with no shared context," Singh explained. "With a frontier agent, you say, 'I need to solve this broad problem.' You point it to the right application, and it decides which repos need changes."

The agents exhibit three defining characteristics that AWS believes set them apart: autonomy in decision-making, the ability to scale by spawning multiple agents to work on different aspects of a problem simultaneously, and the capacity to operate independently for extended periods.

"A frontier agent can decide to spin up 10 versions of itself, all working on different parts of the problem at once," Singh said.

How each of the three frontier agents tackles a different phase of development

Kiro autonomous agent serves as a virtual developer that maintains context across coding sessions and learns from an organization's pull requests, code reviews, and technical discussions. Teams can connect it to GitHub, Jira, Slack, and internal documentation systems. The agent then acts like a teammate, accepting task assignments and working independently until it either completes the work or requires human guidance.

AWS Security Agent embeds security expertise throughout the development process, automatically reviewing design documents and scanning pull requests against organizational security requirements. Perhaps most significantly, it transforms penetration testing from a weeks-long manual process into an on-demand capability that completes in hours.

SmugMug, a photo hosting platform, has already deployed the security agent. "AWS Security Agent helped catch a business logic bug that no existing tools would have caught, exposing information improperly," said Andres Ruiz, staff software engineer at the company. "To any other tool, this would have been invisible. But the ability for Security Agent to contextualize the information, parse the API response, and find the unexpected information there represents a leap forward in automated security testing."

AWS DevOps Agent functions as an always-on operations team member, responding instantly to incidents and using its accumulated knowledge to identify root causes. It connects to observability tools including Amazon CloudWatch, Datadog, Dynatrace, New Relic, and Splunk, along with runbooks and deployment pipelines.

Commonwealth Bank of Australia tested the DevOps agent by replicating a complex network and identity management issue that typically requires hours for experienced engineers to diagnose. The agent identified the root cause in under 15 minutes.

"AWS DevOps Agent thinks and acts like a seasoned DevOps engineer, helping our engineers build a banking infrastructure that's faster, more resilient, and designed to deliver better experiences for our customers," said Jason Sandry, head of cloud services at Commonwealth Bank.

Amazon makes its case against Google and Microsoft in the AI coding wars

The announcement arrives amid a fierce battle among technology giants to dominate the emerging market for AI-powered development tools. Google has made significant noise in recent weeks with its own AI coding capabilities, while Microsoft continues to advance GitHub Copilot and its broader AI development toolkit.

Singh argued that AWS holds distinct advantages rooted in the company's 20-year history operating cloud infrastructure and Amazon's own massive software engineering organization.

"AWS has been the cloud of choice for 20 years, so we have two decades of knowledge building and running it, and working with customers who've been building and running applications on it," Singh said. "The learnings from operating AWS, the knowledge our customers have, the experience we've built using these tools ourselves every day to build real-world applications—all of that is embodied in these frontier agents."

He drew a distinction between tools suitable for prototypes versus production systems. "There's a lot of things out there that you can use to build your prototype or your toy application. But if you want to build production applications, there's a lot of knowledge that we bring in as AWS that apply here."

The safeguards Amazon built to keep autonomous agents from going rogue

The prospect of AI systems operating autonomously for days raises immediate questions about what happens when they go off track. Singh described multiple safeguards built into the system.

All learnings accumulated by the agents are logged and visible, allowing engineers to understand what knowledge influences the agent's decisions. Teams can even remove specific learnings if they discover the agent has absorbed incorrect information from team communications.

"You can go in and even redact that from its knowledge like, 'No, we don't want you to ever use this knowledge,'" Singh said. "You can look at the knowledge like it's almost—it's like looking at your neurons inside your brain. You can disconnect some."

Engineers can also monitor agent activity in real-time and intervene when necessary, either redirecting the agent or taking over entirely. Most critically, the agents never commit code directly to production systems. That responsibility remains with human engineers.

"These agents are never going to check the code into production. That is still the human's responsibility," Singh emphasized. "You are still, as an engineer, responsible for the code you're checking in, whether it's generated by you or by an agent working autonomously."

What frontier agents mean for the future of software engineering jobs

The announcement inevitably raises concerns about the impact on software engineering jobs. Singh pushed back against the notion that frontier agents will replace developers, framing them instead as tools that amplify human capabilities.

"Software engineering is craft. What's changing is not, 'Hey, agents are doing all the work.' The craft of software engineering is changing—how you use agents, how do you set up your code base, how do you set up your prompts, how do you set up your rules, how do you set up your knowledge bases so that agents can be effective," he said.

Singh noted that senior engineers who had drifted away from hands-on coding are now writing more code than ever. "It's actually easier for them to become software engineers," he said.

He pointed to an internal example where a team completed a project in 78 days that would have taken 18 months using traditional practices. "Because they were able to use AI. And the thing that made it work was not just the fact that they were using AI, but how they organized and set up their practices of how they built that software were maximized around that."

How Amazon plans to make AI-generated code more trustworthy over time

Singh outlined several areas where frontier agents will evolve over the coming years. Multi-agent architectures, where systems of specialized agents coordinate to solve complex problems, represent a major frontier. So does the integration of formal verification techniques to increase confidence in AI-generated code.

AWS recently introduced property-based testing in Kiro, which uses automated reasoning to extract testable properties from specifications and generate thousands of test scenarios automatically.

"If you have a shopping cart application, every way an order can be canceled, and how it might be canceled, and the way refunds are handled in Germany versus the US—if you're writing a unit test, maybe two, Germany and US, but now, because you have this property-based testing approach, your agent can create a scenario for every country you operate in and test all of them automatically for you," Singh explained.

Building trust in autonomous systems remains the central challenge. "Right now you still require tons of human guardrails at every step to make sure that the right thing happens. And as we get better at these techniques, you will use less and less, and you'll be able to trust the agents a lot more," he said.

Amazon's bigger bet on autonomous AI stretches far beyond writing code

The frontier agents announcement arrived alongside a cascade of other news at re:Invent 2025. AWS kicked off the conference with major announcements on agentic AI capabilities, customer service innovations, and multicloud networking.

Amazon expanded its Nova portfolio with four new models delivering industry-leading price-performance across reasoning, multimodal processing, conversational AI, code generation, and agentic tasks. Nova Forge pioneers "open training," giving organizations access to pre-trained model checkpoints and the ability to blend proprietary data with Amazon Nova-curated datasets.

AWS also added 18 new open weight models to Amazon Bedrock, reinforcing its commitment to offering a broad selection of fully managed models from leading AI providers. The launch includes new models from Mistral AI, Google's Gemma 3, MiniMax's M2, NVIDIA's Nemotron, and OpenAI's GPT OSS Safeguard.

On the infrastructure side, Amazon EC2 Trn3 UltraServers, powered by AWS's first 3nm AI chip, pack up to 144 Trainium3 chips into a single integrated system, delivering up to 4.4x more compute performance and 4x greater energy efficiency than the previous generation. AWS AI Factories provides enterprises and government organizations with dedicated AWS AI infrastructure deployed in their own data centers, combining NVIDIA GPUs, Trainium chips, AWS networking, and AI services like Amazon Bedrock and SageMaker AI.

All three frontier agents launched in preview on Tuesday. Pricing will be announced when the services reach general availability.

Singh made clear the company sees applications far beyond coding. "These are the first frontier agents we are releasing, and they're in the software development lifecycle," he said. "The problems and use cases for frontier agents—these agents that are long running, capable of autonomy, thinking, always learning and improving—can be applied to many, many domains."

Amazon, after all, operates satellite networks, runs robotics warehouses, and manages one of the world's largest e-commerce platforms. If autonomous agents can learn to write code on their own, the company is betting they can eventually learn to do just about anything else.