2025-12-30 13:42:43
Building AI demos has never been easier. With notebooks, Streamlit or Gradio, you can create something impressive in minutes.
But once AI is supposed to live inside a real system that serves requests, integrates with data sources, handles errors and evolves over time, most of these approaches start to fall apart. This article series focus on exactly that gap:
How to turn AI PoCs into production-ready backend services.
A production-ready AI backend needs to do more than generate text.
It must provide:
A demo optimizes for speed and visibility. A backend service optimizes for reliability, structure and integration. That distinction becomes critical as soon as AI is not the product itself, but a capability inside a larger system.
This is where backend frameworks, and especially FastAPI, start to matter.
Its core concepts map extremely well to the architectural challenges of AI systems. Instead of treating AI as a special case, FastAPI allows it to be handled like any other backend component with clear boundaries and responsibilities.
Large language models are probabilistic. Backend systems are not.
Using Pydantic as a contract layer makes AI outputs machine-consumable:
This is essential if AI is supposed to interact reliably with existing systems. Without strict contracts, small deviations in model output quickly lead to runtime errors, brittle integrations and difficult debugging.
Retrieval pipelines, vector stores, agent tools or memory components are often treated as something “special” in AI projects. FastAPI’s dependency injection model removes that distinction.
RAG components, agents and tools can be injected exactly like:
This leads to a clean separation of concerns:
The result is an architecture where AI components are replaceable, testable and composable, rather than being hard wired into endpoint logic.
Production AI systems often require:
FastAPI provides these primitives out of the box, which makes it easier to move from experimentation to real backend services without changing the entire stack.
Not as a draft, but as a visual concept:
@app.post("/chat", response_model=ChatResponse)
asyncdefchat(
prompt: ChatRequest,
llm=Depends(get_llm),
):
result = llm.invoke(prompt.message)
return ChatResponse(answer=result)
This is intentionally simple, but it already enforces:
That is the difference between “AI as a demo” and “AI in a production backend”.
Building production-ready AI backends is not about better prompts or bigger models.
It is about:
Good architecture turns AI into a reliable and powerful backend capability. It allows you to design systems in which AI creates real and sustainable value instead of merely generating text.
2025-12-30 13:38:00
This blog continues from my previous Ansible basics blog. If you haven't gone through it yet, I recommed reading it first, where I explained Ansible fundamentals inventory, playbooks, and how to write your first playbook.
Ansible Fundamentals
Variables - The basic inputs of Ansible
Variables are values that can change and are used to make playbooks flexible and reusable.
Instead of hard coding information inside a playbook, we store it in variables. Variables are important because one playbook works for multiple servers and changes are easy and safe
Think of it like: When you build a house, you first decide:
These details can change from one house to another.
Similarly, in asnible:
Server name can change
Port number can change
Software version can change
These changing values are stored in variables.
How to Define Variables in Ansible
1) Inside a Playbook
---
- name: Example of Ansible Variables
hosts: all
vars:
app_name: "MyApp"
app_port: 8080
tasks:
- name: Print application details
debug:
msg: "Deploying {{ app_name }} on port {{ app_port }}"
Here, vars: defines variables directly in the playbook. You can reference them using {{ variable_name }}.
2) In Inventory File
[webservers]
server1 ansible_host=192.168.1.10 app_port=8080
server2 ansible_host=192.168.1.11 app_port=9090
3) Using vars_files
You can keep variables in a separate file for better organization
# vars.yml
app_name: "MyApp"
app_port: 8080
4) Passing Extra Variables at Runtime
ansible-playbook site.yml --extra-vars "app_name=MyApp app_port=9090"
Ansible Facts
Ansible facts are pieces of information about the target system that Ansible automatically gathers when running a playbook. These include details like:
IP Addresses
Operating system type and version
Hostname
CPU, memory, and disk information
Facts are collected by the setup module and stored in a dictionary called ansible_facts.
How to Gather Facts
By default, Ansible gathers facts at the start of a playbook run. This is controlled by the gather_facts parameter in the playbook:
- name: Example Playbook
hosts: all
gather_facts: yes
tasks:
- name: Print OS family
debug:
msg: "OS Family is {{ ansible_facts['os_family'] }}"
If you set gather_facts: no, Ansible will skip collecting facts, which can speed up execution when facts are not needed.
Ansible roles
Roles in Ansible are a way to organize playbooks into reusable components. Instead of writing large, monolithic playbooks, roles allow you to break them down into smaller, modular pieces.
Benefits of using roles:
Reusability: Use the same code across multiple projects.
Maintainability: Easier to update and manage.
Scalability: Ideal for large environment.
Standardization: Encourages best practices and consistent structure.
Structure of a role
roles/
└── myrole/
├── tasks/ # Main list of tasks to execute
├── handlers/ # Handlers triggered by tasks
├── templates/ # Jinja2 templates for configuration files
├── files/ # Static files to copy
├── vars/ # Variables with higher precedence
├── defaults/ # Default variables (lowest precedence)
└── meta/ # Role metadata (dependencies, author info)
Ansible Galaxy
Ansible Galaxy is a community hub and repository for Ansible content like roles, collections, and plugins. It’s essentially a marketplace where you can download pre-built automation content or share your own. This saves time because you don’t have to write everything from scratch.
1) Install a Role from Galaxy
ansible-galaxy role install geerlingguy.nginx
2) Use Installed Roles in Playbooks
---
- hosts: webservers
roles:
- geerlingguy.nginx
3) Create your own role
ansible-galaxy init roles/webserver
This creates the folder we need:
roles/
└── webserver/
├── defaults/
│ └── main.yml
├── files/
├── handlers/
│ └── main.yml
├── meta/
│ └── main.yml
├── tasks/
│ └── main.yml
├── templates/
├── vars/
│ └── main.yml
└── README.md
Converting a Simple Playbook into a Role
Original Playbook:
- hosts: webservers
tasks:
- name: Install Apache
yum:
name: httpd
state: present
- name: Start Apache
service:
name: httpd
state: started
Converted into Role:
# roles/webserver/tasks/main.yml
- name: Install Apache
yum:
name: httpd
state: present
- name: Start Apache
service:
name: httpd
state: started
Playbook using role:
- hosts: webservers
roles:
- apache
2025-12-30 13:35:55
¿Actualmente haces FinOps y te sientes solo/a? Esa fue la sensación que tuve durante algunos meses, cuando me enfrenté al reto de tomar un rol de nueva creación con una metodología muy nueva. Sin embargo, la comunidad de la FinOps Foundation ha representado para mí una alternativa de conectar y acercarme con otros/as profesionales que implementan la metodología en sus compañías y proyectos y gracias a eso, me animé a formar parte de este gran equipo.
2025 ha sido un año muy FinOpsero, y qué emoción escuchar que con más frecuencia hay más personas interesadas en aprender sobre esta nueva metodología.
Al menos en LATAM se ha detectado que un 70% de empresas que operan en la región están interesadas en ampliar su inversión en la nube, y esto también, les ha planteado tener y una gestión efectiva de los costos. En espera de los resultados del "State of FinOps" , estoy segura que encontraremos que las tendencias de la metodología tendrán un fuerte componente de cambio constante, con el objetivo de adaptarse a las tendencias del sector tecnológico, después del cambuo de enfoque hacia Cloud +, el ritmo ha sido más acelerado y la comunidad de la FinOps Foundation ha surgido como un mecanismo de impulso de la adopción de esta metodología
¿Te interesa unirte? Acá te cuento un poco sobre cómo hacerlo en América Latina:
El elemento inicial es integrarte a nuestra comunidad en slack, en él encontrarás, canales de comunicación por región, información por oportunidades laborales, anuncios de la comunidad, entre otros
Das click en "Join community" y tendrás acceso a un formulario, debes ingresar información básica sobre ti, tu contacto, detalles profesionales e información específica sobre tus certificaciones de FinOps, estos datos son importantes porque será más fácil identificarte dentro de la comunidad
Un poco de detalle sobre tus intereses y la aceptación del código de conducta, este último es de los más importantes, ya que la comunidad de FinOps es un espacio, abierto, flexible y diverso, pero sobre todo un espacio seguro para todas y todos quienes estamos interesados/as en FinOps.
Una vez que das click en "Submit" recibirás un correo de parte de nuestro director de comunidad Rodolfo Silva, en la que se confirma tu acceso a la comunidad y detalles adicionales sobre tus primeros pasos en la comunidad
¡Aquí inicia todo! Preséntate, únete al grupo de tu país o región, y conecta con las personas, siempre estaremos agradecidos/as y disponibles para ayudar, y colaborar ¡Y no es todoooo! En América Latina, tenemos 2 embajadores "FinOps Ambassadors" que son profesionales usuarios finales apasionados por la comunidad de la Fundación FinOps, reconocidos/as por su experiencia, sus contribuciones y dispuestos/as a ayudar a otros a aprender y tener éxito, en LATAM, tenemos a Guido Fiamenco de Argentina y a Diego Alejandro Gómez Baena de Colombia, puedes escribirles siempre que quieras resolver alguna duda o tengas interés en proponer e integrarte a la comunidad ¡Son muy coool!
Y también, estamos los Meetup Organizers, que somos miembros de la comunidad que dirigen grupos locales o virtuales, fomentando el intercambio de conocimientos sobre FinOps, la creación de redes y el debate sobre las mejores prácticas, puedes acercarte a nosotros/as si te interesa unirte a nuestros canales, unirte a las charlas, dar una charla, o incluso, proponer nuevas ideas. Estos grupos están disponibles en Perú, Colombia, Argentina, Ecuador, Chile, y México (queremos llegar a más países), y estamos disponibles para escuchar y conectar ¡Acércate!
Creo que esta es una introducción general para que puedas integrarte, así que no dudes en hacerlo
Mejores costos, son mejores tecnologías ¡Arriba la comunidad de la FinOps Foundation LATAM!
2025-12-30 13:31:15
Part 4 of the Zero-Trust AI Agent Security Series
As AI agents operate at machine speed with thousands of requests per second, traditional rate limiting approaches fall short. A compromised agent can stay within frequency limits while executing sophisticated attacks through behavioral manipulation, resource exhaustion, or coordinated activities. This is where behavioral throttling becomes critical for AI agent security.
The Problem with Traditional Rate Limiting
Standard rate limiting applies uniform thresholds: 100 requests per minute for everyone. But AI agents aren't uniform. A monitoring agent legitimately generates 500 telemetry messages per minute, while a decision-making agent should execute only 5 critical approvals per hour.
More importantly, sophisticated attacks operate within rate limits through:
Distributed coordination: 50 compromised agents each staying below individual limits while achieving 10,000 aggregate requests
Behavioral drift: Gradually modifying request patterns over weeks to normalize unauthorized access
Resource exhaustion: Submitting computationally expensive queries that consume 100x normal resources while staying within frequency limits
Sliding Windows: The Foundation
The first improvement moves from fixed windows to sliding windows. Fixed windows create exploitable edge cases where attackers send maximum requests at window boundaries, effectively doubling throughput in brief periods.
Fixed Window Vulnerability:
Window 1: [_________________100 requests at 59.8s]
Window 2: [100 requests at 60.2s_________________]
Result: 200 requests in 0.4 seconds = Attack Success
Sliding Window Protection:
Any 60-second span from 0.2s to 60.2s contains 200 requests
Result: Limit exceeded, second burst blocked
Sliding windows continuously track requests over rolling time periods, ensuring consistent enforcement regardless of timing.
Behavioral Throttling: Beyond Frequency
While rate limiting constrains request frequency, behavioral throttling addresses sophisticated abuse through pattern analysis:
Temporal Pattern Analysis
Agents shifting from distributed patterns to synchronized bursts
Coordinated timing between multiple agents indicating orchestrated activity
Deviation from established operational rhythms
Semantic Drift Detection
Messages structurally valid but semantically inconsistent with agent purpose
Gradual shifts in request types indicating scope expansion
Context switching patterns inconsistent with operational models
Resource Consumption Profiling
CPU or memory consumption patterns inconsistent with declared functions
Network bandwidth usage exceeding operational requirements
Processing duration anomalies indicating hidden computational workloads
Progressive Throttling Implementation
Behavioral throttling applies graduated constraints based on anomaly severity rather than binary blocking:
Level 1 (Minor Anomalies): 25% rate reduction, enhanced logging
Level 2 (Moderate Anomalies): 50% rate reduction, supervisor notification
Level 3 (Significant Anomalies): 75% rate reduction, manual approval required
Level 4 (Severe Anomalies): Near-complete throttling, emergency response
Trust levels influence response severity. High-trust agents with established behavioral baselines receive more lenient treatment, while low-trust agents face immediate restrictions for minor anomalies.
Distributed Architecture Considerations
AI agent rate limiting requires distributed enforcement that maintains consistency across multiple entry points. Implementation leverages:
Redis clusters with sharding for sub-millisecond rate limit lookups
Consistent hashing ensuring agent requests route to same counter nodes
Real-time analysis pipelines using Kafka and Apache Flink for behavioral scoring
Hot-reloadable policies allowing dynamic threshold adjustment
Real-World Impact: Financial Trading Case Study
A cryptocurrency trading platform implemented behavioral throttling for 200 AI agents processing millions of market data points. Results:
15 security incidents prevented in the first year, including 8 resource exhaustion attacks
40% reduction in false trading signals while maintaining sub-2ms latency
$50 million in potential losses prevented through behavioral anomaly detection
Trust-based adaptation during market volatility improved operational resilience
Key Takeaways for Practitioners
Move beyond simple frequency limits to behavioral pattern analysis
Implement sliding windows to eliminate timing attack vulnerabilities
Apply graduated responses based on trust levels and anomaly severity
Design for distribution with consistent hashing and failover capabilities
Monitor behavioral baselines to detect gradual drift and scope expansion
Behavioral throttling transforms rate limiting from a blunt instrument into a nuanced security control that adapts to AI agent behavior while maintaining operational performance. As AI agents become more sophisticated, our security controls must evolve to match their capabilities.
This article is part of an ongoing series on zero-trust architecture for AI-to-AI multi-agent systems. The complete framework addresses identity verification, authorization, temporal controls, rate limiting, logging, consensus mechanisms, and more.
About the Author: John R. Black III is a security practitioner with over two decades of experience in telecommunications and information technology, specializing in zero-trust architectures for AI agent systems.
2025-12-30 13:28:54
Hey Cloud Gatekeepers! 👋
Welcome to Day 19 of the #100DaysOfCloud Challenge: Attach IAM Policy to User! We are finishing the loop on our Identity and Access Management tasks with KodeKloud Engineer.
Over the last few days, we’ve built users and we’ve written custom policies. But right now, iamuser_jim has a "key" but no permissions to use it. Today, we are going to fix that by Attaching his specific policy to his account.
Our mission: Attach the existing policy iampolicy_jim to the user iamuser_jim.
In AWS, an IAM Policy is just a static document sitting in a library until it is associated with a "Principal" (a User, Group, or Role).
We will use the IAM Dashboard to finalize this security link.
iamuser_jim.
Inside Jim's user summary page, look for the "Permissions" tab.
Click the "Add permissions" button on the right and select "Add permissions" from the dropdown.
iampolicy_jim.
iampolicy_jim.
Success! iamuser_jim now has the specific powers defined in his custom policy. 🎉
iampolicy_jim to iamuser_jim. It’s easy to misclick when names are similar!Jim is now fully equipped to help the Nautilus team with their cloud migration! You've successfully managed the "Who" (User), the "What" (Policy), and the "How" (Attachment).
How are you finding the 100 Days of Cloud Challenge? 🛡️
2025-12-30 13:26:50
As of December 23, 2025, the role of a Microsoft Azure AI Engineer goes far beyond just writing code. It’s about building, deploying, and managing real AI solutions on Azure that actually work in production.
Azure AI engineers are involved in the full lifecycle of an AI solution. From understanding business requirements and designing the approach to development, deployment, integration, ongoing maintenance, and performance optimization, they play a hands-on role at every stage. Monitoring and fine-tuning models over time is just as important as building them.
The role is highly collaborative. Azure AI engineers work closely with solution architects to turn ideas into reality, and they regularly coordinate with data scientists, data engineers, IoT specialists, infrastructure teams, and fellow developers. Together, they create secure, end-to-end AI solutions and embed AI capabilities into larger applications and systems.
From a technical perspective, experience with Python or C# is essential. You’re expected to be comfortable working with REST APIs and SDKs to develop solutions for image and video processing, natural language processing, knowledge mining, and generative AI on Azure.
A strong understanding of the Azure AI ecosystem is also key, including how different AI services fit together and which data storage options make sense for different use cases. Just as importantly, Azure AI engineers are expected to apply responsible AI principles, ensuring solutions are ethical, secure, and trustworthy.