![[AWS] Strategies to make KAA work like a member of the project team [Kiro]](https://media2.dev.to/dynamic/image/width=1200,height=627,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fh59hn8ajn7zj08moovf0.png)
[AWS] Strategies to make KAA work like a member of the project team [Kiro]
This article is a machine translation of the contents of the following URL, which I wrote in Japanese: https://qiita.com/Nana_777/items/f9813fc7bec6c47826e2 Introduction In the previous article, we introduced the basic functions of Kiro Autonomous Agent (KAA). It's a frontier agent that automatically analyzes the repository, implements the task, and creates a pull request when a task is assigned via GitHub Issue. This article will explore how to integrate Kiro Autonomous Agent into your development workflow as a "member of your team." Specifically, we will build a series of flows using GitHub Actions and AWS APIs to automatically retrieve and implement issues from Backlog, and notify Slack of completion reports. This time, we explored various ways to utilize the basic functions of the Kiro
Fetching article from DEV Community…
DEV Community
https://dev.to/aws-builders/aws-strategies-to-make-kaa-work-like-a-member-of-the-project-team-kiro-19bmSign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
updateapplicationfeature
You Have 50 AI Agents Running. Can You Name Them All?
Last Tuesday at 2am, an agent burned through $400 in OpenAI credits. Nobody noticed until the invoice arrived. It was a research agent. One of about 40 running across three clouds. Someone had deployed it with a retry loop that never backed off. It hit rate limits, waited, retried, hit limits again -- for 11 hours straight. The team lead asked a simple question: "How many agents do we have running right now, and what are they doing?" Nobody could answer. The Spreadsheet Phase Every team goes through this. You start with one agent. Then five. Then someone on another team builds three more. The ML team deploys a batch of data processors. The support team launches a customer-facing bot. Pretty soon you have 30-50 agents. And the "monitoring" looks like this: AWS agents: check CloudWatch (mayb
Your AI Agent Stopped Responding 2 Hours Ago. Nobody Noticed.
Your agent is deployed. Pod is running. Container passes liveness probes. Grafana shows a flat green line. Everything looks fine. Except the agent stopped processing work 2 hours ago. It's alive - the process is there - but it's stuck. Deadlocked on a thread. Blocked on a full queue. Spinning in a retry loop that will never succeed. Silently swallowing exceptions in a while True . Nobody knows until a customer reports it. Or until someone opens a dashboard at 5 PM and wonders why the task queue has been growing all afternoon. Why Container Health Checks Don't Work for Agents Kubernetes liveness probes check one thing: is the process responding to HTTP? If your agent serves a /healthz endpoint, the probe passes. The agent is "healthy." But responding to /healthz and processing work are two
Series Week 20 / 52 — Differentiating Patching differences between Exadata On Prem and OCI Databases (DB Systems and ExaCS)
%0A%20%20%20%20%20%20%20%20.libutton%20{ %0A%20%20%20%20%20%20%20%20%20%20display:%20flex; %0A%20%20%20%20%20%20%20%20%20%20flex-direction:%20column; %0A%20%20%20%20%20%20%20%20%20%20justify-content:%20center; %0A%20%20%20%20%20%20%20%20%20%20padding:%207px; %0A%20%20%20%20%20%20%20%20%20%20text-align:%20center; %0A%20%20%20%20%20%20%20%20%20%20outline:%20none; %0A%20%20%20%20%20%20%20%20%20%20text-decoration:%20none%20!important; %0A%20%20%20%20%20%20%20%20%20%20color:%20#ffffff%20!important; %0A%20%20%20%20%20%20%20%20%20%20width:%20200px; %0A%20%20%20%20%20%20%20%20%20%20height:%2032px; %0A%20%20%20%20%20%20%20%20%20%20border-radius:%2016px; %0A%20%20%20%20%20%20%20%20%20%20background-color:%20#0A66C2; %0A%20%20%20%20%20%20%20%20%20%20font-family:%20"> { Abhilash Kumar Bhattaram : Follo
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products
Voice-to-Schema: Turning "Track My Invoices" Into a Real Table
We rebuilt our NLP pipeline three times before it actually worked. Here's what went wrong each time and what we learned about the gap between what people say and what they mean. The Problem Nobody Talks About When we started building VoiceTables , we had a simple hypothesis: let people describe what they need, and generate a structured table from that description. User says "I need to track my invoices," system creates an invoices table with sensible columns. Easy, right? Turns out spoken language and structured data are almost completely different things. The first version took about two weeks to build and maybe 20 minutes to realize it was broken. Attempt 1: Naive Prompt Engineering The first pipeline was embarrassingly simple. Take the transcript, send it to an LLM with a system prompt
Your AI Agent Stopped Responding 2 Hours Ago. Nobody Noticed.
Your agent is deployed. Pod is running. Container passes liveness probes. Grafana shows a flat green line. Everything looks fine. Except the agent stopped processing work 2 hours ago. It's alive - the process is there - but it's stuck. Deadlocked on a thread. Blocked on a full queue. Spinning in a retry loop that will never succeed. Silently swallowing exceptions in a while True . Nobody knows until a customer reports it. Or until someone opens a dashboard at 5 PM and wonders why the task queue has been growing all afternoon. Why Container Health Checks Don't Work for Agents Kubernetes liveness probes check one thing: is the process responding to HTTP? If your agent serves a /healthz endpoint, the probe passes. The agent is "healthy." But responding to /healthz and processing work are two
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!