AWS Kiro Autonomous Agent: Multi-Day Coding Without Context Loss—What .NET & Azure Developers Need to Know

TL;DR AWS announced three new “frontier agents” including one designed to learn how you like to work and then operate on its own for days. Kiro maintains “persistent context across sessions,” meaning it won’t forget mid-task. For .NET and Azure developers, this raises important questions: Should you evaluate AWS agents for long-running automation, or stick with Azure’s Agent Service? We’ll break down the implications for your infrastructure and workflows.

The Core Story: Persistent Memory Changes the Game

AWS 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 headline feature is Kiro, a coding agent that learns your team’s patterns and can tackle complex, multi-step tasks independently.

What makes this different from existing AI coding assistants? 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. Kiro flips this script.

How Kiro Works: Spec-Driven Development & Team Learning

To make reliable code, the AI must follow a company’s software-coding specifications. Kiro does that through a concept called “spec-driven development.” As Kiro codes, it has the human instruct, confirm, or correct its assumptions, thereby creating specifications. The Kiro autonomous agent watches how the team works in various tools by scanning existing code, among other training means. And then, AWS says, it can work independently. AWS CEO Matt Garman promised when introducing the new product during his keynote at AWS re:Invent on Tuesday: “You simply assign a complex task from the backlog and it independently figures out how to get that work done. It actually learns how you like to work, and it continues to deepen its understanding of your code and your products and the standards that your team follows over time.” —

The Catch: Context Windows & Hallucinations Still Matter

Before you declare the era of hands-off coding here, a reality check: It’s not totally clear that the biggest hurdle to agentic adoption is the context window (aka the ability to work continuously without stalling out). LLMs still have hallucination and accuracy issues that turn developers into “babysitters.” Amazon’s agents aren’t the first to claim long work windows. For instance OpenAI said last month that GPT‑5.1-Codex-Max, its agentic coding model, is designed for long runs, too, up to 24 hours. So persistence alone doesn’t guarantee quality output.

Implications for .NET & Azure Engineers

1. Competitive Pressure on Azure

Azure AI Foundry Agent Service is now generally available, helping more companies like JM Family, Fujitsu, and YoungWilliams automate some of the most complex business processes. But Kiro’s multi-day autonomy and spec-driven approach represent a meaningful leap. If you’re already on Azure, you’ll want to evaluate whether Kiro’s capabilities justify a hybrid or multi-cloud approach.

2. Agent Governance & Safety

One upgrade is the introduction of Policy in AgentCore. This feature allows users to set boundaries for agent interactions using natural language. These boundaries integrate with AgentCore Gateway, which connects AI agents with outside tools, to automatically check each agent’s action and stop those that violate written controls. AWS is addressing the trust problem head-on—crucial for enterprises. .NET developers using Azure should check whether Azure’s governance tooling matches this level of granularity.

3. .NET Integration Path (or Lack Thereof)

Kiro is AWS-native and runs on AWS infrastructure. The technical implementation provides support for both Python and .NET environments. Developers can install the Python version through pip package manager or integrate .NET support through NuGet packages. That’s referring to the Microsoft Agent Framework, not Kiro. Kiro itself has no direct .NET SDK yet—you’d invoke it via AWS APIs or SDKs.

For .NET developers, this means:

Use AWS SDKs (C# bindings exist) to orchestrate Kiro from your .NET apps.
Or, stick with Microsoft Agent Framework, Microsoft.Extensions.AI, and first-class Model Context Protocol (MCP) support. These systems aim to standardize patterns for building agentic workflows, integrating external tools, enabling telemetry, and working across multiple AI providers. —

Practical Takeaway: Evaluation Checklist

If you’re considering Kiro for your team:

Does your workload fit the AWS ecosystem? Kiro is optimized for AWS infrastructure. If you’re cloud-agnostic or Azure-first, friction will increase.
Can you live with the learning curve? Spec-driven development is powerful but requires upfront investment in defining specs and letting the agent learn.
Do you need multi-day autonomy? For most CRUD and incremental features, probably not. For large refactors or cross-service migrations, yes.
How do you handle hallucinations? Kiro is not a “set and forget” tool. Plan for human review cycles.

The Bigger Picture

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. 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.” This is AWS signaling that agentic AI is moving from “cool demo” to “production infrastructure.” For .NET and Azure developers, that means the competitive landscape is heating up. Your choice of cloud and tooling now directly affects which agents you can deploy and how easily you can integrate them.