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Mastering Enterprise Security in Microsoft Power Platform

Kaushal Shah — Fri, 07 Aug 2026 19:00:00 GMT

Citizen development was supposed to free up IT teams, not give them a new category of risk to manage. Yet that is precisely what has happened in many organizations running Microsoft Power Platform at scale. Business users build apps, automate workflows, and connect data sources at a pace that traditional governance models were never designed to keep up with. Each new app or flow is a small decision about data access, and when hundreds of these decisions are made independently across departments, the result is a security posture nobody fully understands.

The instinct to lock everything down defeats the purpose of low-code platforms in the first place. The real objective is to enable rapid development while keeping data, connections, and environments under deliberate control. Microsoft has built a substantial set of security and governance capabilities directly into Power Platform for exactly this reason, but they only work when an organization actually configures and enforces them. Left on default settings, the platform favors flexibility over restriction, and that gap is where most enterprise security gaps quietly form.

Microsoft Foundry Tool Search: Your Agent Pays a Tax on Every Tool It Never Calls

Jubin Soni, FBCS — Fri, 07 Aug 2026 18:00:00 GMT

Most agents are billed for tools they don't use. Not once — on every single turn.

The mechanics are simple enough that it's easy to miss. When you give a model a set of tools, the full JSON schema for every tool goes into the request. Names, descriptions, parameter types, enum values, nested objects, the lot. The model reads all of it, picks one, and calls it. Next turn, the whole catalog goes over the wire again, because the API is stateless and the tool list is part of the request.

Database Bottlenecks Nobody Talks About: Optimizing SQL Queries Beyond Indexing

Muhammad Awais — Fri, 07 Aug 2026 17:00:05 GMT

Every performance guide starts the same way. "Add an index." And yes, indexes matter. But I've spent years fixing production databases, and here's the truth: indexing is the easy 20%. The hard 80% is everything nobody writes blog posts about.

I once spent three days chasing a query that had a perfect index. The index wasn't the problem. The problem was that the database's own statistics were lying to it.

How We Cut PyFlink Pipeline p99 Latency from 3-5 Seconds to ~500ms

Arjun Shah — Fri, 07 Aug 2026 16:00:05 GMT

The Problem: Our p99 Was 3-5 Seconds

Our PyFlink pipeline was missing its latency SLO by seconds. The pipeline itself was straightforward: consume events from Kafka, transform them, serialize them as Protobuf, and write the results to downstream systems. Yet under production load, p99 end-to-end latency was consistently in the 3-5 second range.

Profiling pointed us to an unexpected bottleneck: we were deserializing Protobuf messages in Python, even though the Flink runtime processing our stream was JVM-based. Every record that entered the Python path had to cross the JVM-to-Python process boundary, get parsed by a Python UDF, and then cross back. The business logic wasn't the problem. The doorway was.

Orchestration Meets MCP: Building Governed Agentic Workflows With Quarkus Flow and AGENTS.md

Daniel Oh — Fri, 07 Aug 2026 15:00:06 GMT

Building autonomous AI agents with large language models (LLMs) is easy when writing single-turn demo scripts. However, moving multi-agent loops into production introduces serious architectural challenges. Agents hallucinate, loop infinitely without reaching convergence, require human approval for high-risk operations, and need standard tool-calling integrations alongside clear operational governance.

Historically, Java developers faced a tough choice: either rely on heavyweight, external workflow clusters (like Temporal or Camunda) that add operational overhead, or hand-craft fragile while loops and custom state machines inside their services.

A Zero-Trust Implementation Framework for Cloud Migrations: Lessons From Enterprise Deployments

Srinivasarao Thumala — Fri, 07 Aug 2026 13:00:00 GMT

Cloud migration projects almost always treat security as a downstream concern something to bolt on after workloads have already moved, once the “real” migration work is done. Across dozens of enterprise migrations spanning finance, healthcare, and manufacturing workloads, that ordering is consistently the source of the costliest rework: reopened firewall rules, retrofitted identity models, and access reviews that should have happened before a single virtual machine was provisioned.

The pattern holds regardless of which cloud provider is on the receiving end. What follows is a framework provider-agnostic by design for embedding zero-trust principles into the migration process itself, rather than applying them after the fact.

Building Internal Developer Platforms as Products: A Practical Guide for IDP Architects

Josephine Eskaline Joyce — Fri, 07 Aug 2026 12:00:00 GMT

Why Most Platforms Fail to Become Products

Many companies are heavily investing in internal developer platforms (IDPs) with the expectation that they will speed up delivery and governance, and increase developer productivity. Despite significant investment in Kubernetes, CI/CD, observability, security tooling, and cloud infrastructure, many platforms struggle to gain adoption.

The reason is simple: they are built and operated like infrastructure projects, not products.

How to Design a Distributed Job Scheduler

Ajit Singh — Thu, 06 Aug 2026 19:00:05 GMT

Almost every backend eventually needs to run code on a schedule. Send the invoice at midnight. Retry the failed payment in five minutes. Generate the weekly report every Monday at 7 AM. Clean up expired sessions every hour.

On one server, this is easy. You write a cron line and move on. The trouble starts when one server becomes ten. Now the same cron line lives on every box, so the invoice job fires ten times instead of once. Move the cron to a single “scheduler” box, and that box becomes a single point of failure. Every time you deploy new code, that process restarts, and if it crashes or the host dies, there is no second node to cover for it. Any job due during that downtime window silently never fires.

How RAG Cuts Hallucinations in Generative AI Chatbots

Paul Schloss — Thu, 06 Aug 2026 18:00:10 GMT

Retrieval-augmented generation (RAG) reduces hallucinations in generative AI chatbots by grounding each response in retrieved source data instead of relying only on what the model learned during training. Before the model writes a reply, the system fetches relevant passages from a trusted knowledge store and passes them in as context. The model then answers from that evidence, which shrinks the room it has to invent facts.

This article looks at why hallucinations happen at the token level, how a RAG pipeline counters them, and the engineering choices that decide whether grounding actually holds up in production.

HTTP QUERY Method Explained: RFC 10008, Ecosystem Adoption, and a Quarkus Implementation

Hüseyin Akdoğan — Thu, 06 Aug 2026 17:00:00 GMT

Then the search form grows, filters multiply, and nested criteria appear. Since using GET means placing the query inside the URI, a length limit problem emerges. Worse, placing sensitive query values in the URI increases the chance of exposure through access logs, browser history, proxies, and monitoring systems. Because the HTTP protocol does not forbid it, sending a body with GET may look like a way out, but building your design on behavior the standards leave undefined is not a recommended practice. Elasticsearch's GET-with-body search API is a well-known example, and Elastic's own documentation openly acknowledges the problem:

"As a result, some HTTP servers allow it, and some—especially caching proxies—don't. [...] However, because GET with a request body is not universally supported, the search API also accepts POST requests."

VL-JEPA: End of LLMs? Or the End of How We Think About Them?

Ram Ghadiyaram — Thu, 06 Aug 2026 16:00:00 GMT

For the past few years, large language models have felt unstoppable...

Every few months, a bigger model arrived. Longer context. Better fluency. Fewer hallucinations. More parameters. The message was implicit but clear. Scale is intelligence.

Building an AI Visibility Checker With Cloudflare Workers (Without a Backend)

Nena Jasar — Thu, 06 Aug 2026 15:00:00 GMT

I am not a developer. I want to say that upfront, because it changes everything about how you should read this.

I run nenawow.com, a site that reviews AI tools and SEO software. Three years ago I had no SEO background and no coding background either. Last month I shipped six working tools that check AI visibility signals across any website, and they run without a database, without a backend, and without a single line of code I wrote myself.

Build Your First Knowledge Graph From Unstructured Documents Using Python

Sriharsha Makineni — Thu, 06 Aug 2026 14:00:00 GMT

Many engineering teams currently face a knowledge challenge.

Information does exist; however, the information is distributed across various documentation formats such as design documents, runbooks, architectural notes, deployment guides, and incident reports. In general, a developer is aware of which services depend on each other (the Checkout Service depends upon the Payment API), the database or technology stack being used by the dependent services (the Payment API utilizes PostgreSQL), and who owns/operates the dependent service (Platform Team owns and operates the Payment API), however, these pieces of information typically reside in separate locations.

AI-Assisted Development Without Chaos

Miguel Garcia — Thu, 06 Aug 2026 13:00:05 GMT

AI Accelerates Development, But Governance Determines Whether It Reaches Production

Over the last two or three years, I’ve watched AI-assisted development move through distinct phases. First, general-purpose chat assistants like ChatGPT as a thinking partner. Then AI completions embedded in the IDE. Then, toward the end of 2025, the frontier agentic coding models arrived, and agentic development took a real leap in quality: harnesses, orchestrated agents, raw velocity.

What came next is the part I care about most. Code volume exploded, and so did the number of problems riding along with it. I remember one of the strongest engineers on my team telling me, early this year, that he’d stopped reviewing lines of code. He was reviewing how the AI had reasoned. There was simply too much code to check any other way.

Orchestrating Trusted Environments: Securing Untrusted Code Execution With Docker and GKE Agent Sandbox

Anuj Ashok Potdar — Thu, 06 Aug 2026 12:00:07 GMT

Building agentic AI systems fundamentally changes how we handle application security. We are no longer just securing our own code. We are securing our infrastructure against code written dynamically by an LLM and executed on the fly. When building a multi-tenant AI platform, allowing an agent to run arbitrary scripts is a massive escape vector waiting to happen.

Google recently made the GKE Agent Sandbox generally available on their custom Arm-based Axion N4A instances. This gives us a highly efficient, hardware-optimized path to run untrusted code safely. Under the hood, this relies on gVisor to intercept application kernel calls and run them in a heavily restricted user-space kernel.

Docker Containers Don’t Know Your Model Is Still Loading

Pruthvi Raj Seknametla — Wed, 05 Aug 2026 19:00:06 GMT

It was a Friday at 4:50 pm, the worst possible time for anything to go sideways when marketing flipped on a new AI summarization feature for the whole user base instead of the 5% rollout we'd agreed on. Traffic to our LLM service doubled in about four minutes. The autoscaler did exactly what it was told: it spun up three new replicas. What it didn't account for is that each replica needed almost three minutes just to pull a 14GB checkpoint and warm up CUDA kernels before it could answer a single request. The load balancer, seeing new pods report as running, immediately started routing traffic to them. For three minutes, a chunk of our users got 504s while perfectly healthy-looking pods sat there loading a model into memory.

Nobody on the infra side had touched Docker that day. The incident wasn't a Docker bug. We assumed that container orchestration designed for web services would function the same way for processes that take minutes to become useful, rather than those that operate in milliseconds.

Practical QA Workflow Showing How Teams Integrate LLM Testing into Real CI/CD Pipelines

Minkle Kalra — Wed, 05 Aug 2026 18:00:05 GMT

Generative artificial intelligence introduces unprecedented unpredictability into software development pipelines. Traditional software returns predictable outputs for exact inputs. Large language models generate varied responses for the exact same prompt.

QA teams face a massive challenge scaling quality checks for these probabilistic systems. Manual validation falls short during fast deployment cycles. Implementing LLM testing in CI/CD has become mandatory for any modern engineering team.

TensorFlow vs PyTorch: The Real Difference Isn’t Accuracy

Rakshath Naik — Wed, 05 Aug 2026 17:00:04 GMT

A few days ago, I set out to build a simple image classification model using convolutional neural networks (CNNs). The task itself wasn’t particularly complex, but choosing the right framework proved more challenging than expected. I found myself choosing between TensorFlow and PyTorch, two powerful frameworks for building high-performance CNNs.

To explore this, I implemented the same CNN in both frameworks under identical conditions and compared them across key aspects like learning curve, flexibility, debugging, and performance.

Securing Branch Networks With Firewalls, VPNs, IDS/IPS, and Identity-Based Access

Kamal chand Narra — Wed, 05 Aug 2026 16:00:00 GMT

Branch networks no longer behave like quiet extensions of a single headquarters LAN. They terminate local user traffic, break out directly to the internet for SaaS, maintain persistent connections back to core systems, and increasingly host devices that are operationally important even when central resources are unavailable. NIST notes that the enterprise network landscape has shifted because of cloud services, geographic dispersion, and changes in application design, while zero trust guidance emphasizes that network location is no longer the primary signal of trust.

In practice, that means a branch cannot be secured by treating the site-to-site tunnel as a blanket trust boundary. The branch edge has to make explicit policy decisions about which flows are allowed, which flows are encrypted, which flows are inspected, and which identities are entitled to touch which resources.

Why LLM Pipelines Fail in Production and How Temporal and Kafka Fix Them

Akhil Madineni — Wed, 05 Aug 2026 15:00:01 GMT

A production LLM pipeline is rarely just a prompt and a response. It typically combines retrieval, prompt rendering, model inference, output shaping, validation, persistence, and downstream actions. That broader shape is why many systems look stable in a demo and then become fragile under live traffic. The model call is only one component; the operational problem is the workflow around it. Provider APIs impose rate limits, structured outputs still need application-level checks, and external calls introduce failure ambiguity that ordinary request-response code does not handle well.

Where the Breakage Starts

Most production failures happen between steps, not inside the prompt. A request enters an API, context is loaded, a model call is sent, the response is parsed, a downstream action is triggered, and a record is written. If the provider generated output but the network dropped before the caller saw it, the system no longer has a clean answer to whether the operation should be retried or treated as complete. Kafka’s default delivery model is at least once, and Temporal’s documentation is explicit that activities may be retried and therefore should be idempotent. That combination makes duplicate side effects the default risk unless the pipeline is designed around durable state and idempotent writes.

The Retry Budget Pattern: How to Stop Retry Storms in API-Led and Microservice Systems

Manjeera Chanda — Wed, 05 Aug 2026 14:00:00 GMT

The Production Story

Several years ago, my team made a decision that felt obviously correct: If a downstream call fails, retry it. More retries, more resilience. We set three retries on every integration touching our order-fulfillment platform, shipped it on a Thursday, and went home feeling good about our reliability posture.

Six weeks later, retries were the single largest source of traffic in the platform.

Securing AI Agents at the API Layer: 5 Controls That Actually Matter

Priyanka Jayavel — Wed, 05 Aug 2026 13:00:00 GMT

Most API security programs were built for predictable consumers: mobile apps, backend services, partner integrations, and the occasional script. Each of those calls your APIs in fairly bounded ways. AI agents do not fit that model.

An agent does not just call an API. It decides which APIs to call, in what order, and often keeps going until it reaches a result. That autonomy is the point of using an agent, but it is also what makes it dangerous: a single misconfigured agent can generate thousands of requests in minutes, reach systems it was never meant to touch, or chain APIs together in a sequence no human ever designed.

Building an Async Validation API With AWS Bedrock Agents and Serverless Architecture

Rohit Nagpal — Wed, 05 Aug 2026 12:00:00 GMT

As a data engineer, I’ve noticed business teams submitting intake forms, compliance documents, and project proposals that a tech team then manually validates against a set of predefined business rules stored in a database that gets updated quarterly. The time it takes to validate a single form is typically in the hours, and by the time you’ve validated the form, the submitter has moved on to other work.

When I needed to validate project intake forms against 60+ business rules of financial, compliance, and other types of business rules and guidelines (some of them to be used in a deterministic way and others to be used in a more nuanced manner), I knew that a simple if-else logic-based manual review process would not scale.

Designing a Reliable Data Synchronization Layer: Idempotency, Ownership, and Observability

Mike Beentjes — Tue, 04 Aug 2026 19:00:01 GMT

In a lot of organizations, the real integration platform is a person. Someone exports orders from the ERP every morning and pastes them into the planning tool. Someone else re-types customer updates from the CRM into the invoicing system. It works until that person is on holiday or makes a typo in a price field or the volume doubles.

Replacing that manual work with a synchronization service sounds like a junior-level task: read from system A, write to system B, schedule it, done. In practice, sync services are where many integration projects quietly fail. They fail not because moving data is hard, but because the edge cases are partial failures, retries that duplicate records, two systems that both think they own a field, and errors that nobody notices for three weeks.

Agentic RAG: Basic RAG Plus MCP Tool Calls

Balaji Venkatasubramaniyar — Tue, 04 Aug 2026 18:00:00 GMT

That second question isn't a retrieval problem — it's a computation problem that depends on live, structured, user-specific data. This is exactly the gap agentic RAG closes: it keeps the semantic search RAG is good at, and bolts on tool calls (via MCP) so the agent can reach into live systems, fetch real numbers, and reason over them before answering.

A simple way to think about it:

I Built a Java Version Manager by Fixing Other Tools' Open Bugs

David Lerner — Tue, 04 Aug 2026 17:00:05 GMT

Every Java developer knows the ritual. A JAVA_HOME export in one profile file, a different one in another. sdk use java 21 in this terminal, but the other terminal is still on 8. The build passes in your shell and fails in the IDE because the IDE launched from the dock and never sourced your init line. A teammate's "works on my machine" that turns out to mean "works on my shell."

I got tired of it, so I built Jolta. It's Volta, but for Java. This article is partly about what it does, but mostly about how I built it, because the process is the part I'd recommend to anyone building in a crowded tool category: I mined my competitors' bug trackers and turned their backlogs into my test suite.

GraphRAG Retrieval Is Three Decisions: Granularity, Mechanism, and Paradigm

Lokesh Prakash Manohar — Tue, 04 Aug 2026 16:00:05 GMT

Traditional vector RAG retrieves by embedding the question and finding semantically similar chunks, often augmented with lexical search, filtering, or reranking. This approach works when the answer is explicitly described in one or more chunks. However, it breaks down when the answer depends on relationships between facts.

The question "Does my application depend on a compromised package?" illustrates this limitation. The vulnerable package may be several layers deep in the dependency tree, and no single chunk contains the answer. The answer emerges by following a chain of dependencies, but similarity search can struggle because the answer is distributed across multiple relationships rather than represented as a single semantic concept.

Performance Testing With JMeter Beyond the Basics: Distributed Load, Realistic Profiles, and Identifying Security Bottlenecks

Srivenkata Gantikota — Tue, 04 Aug 2026 15:00:08 GMT

Most JMeter test plans I’ve inherited share a common shape. Two hundred threads, one ramp-up, a flat plateau, and a results table that says “p95 was 480ms.” Somebody declares the system performant, the test plan goes into a Confluence page, and nobody runs it again until the next major release.

The problem is that the test doesn’t model anything. The traffic shape is wrong, the user behavior is wrong, the data volumes are wrong, and the security controls aren’t being exercised. The system passes the test and then fails in production at peak load because production traffic doesn’t look like the test.

Understanding Agentic SDLC: The Future of Software Engineering

Pavan Belagatti — Tue, 04 Aug 2026 14:00:09 GMT

Agentic SDLC, or agentic software development life cycle, is the next major shift in how I think about building and shipping software. It is not simply AI writing a few lines of code inside an IDE. It is an end-to-end approach where specialized AI agents participate across planning, implementation, testing, review, deployment, monitoring, documentation, and continuous improvement.

The core idea is straightforward: I set the goal, the priorities, guardrails, and approval points. Agents do the operational work, hand context to one another, and help move a feature through the pipeline with far less manual coordination. The result is not removing developers from engineering. It is allowing developers to spend more time on decisions that actually require judgment.

Engineering Production Agentic Systems: Part 3: The Topology

Ram Ravishankar — Tue, 04 Aug 2026 13:00:00 GMT

Human-in-the-Loop Topology for Production Agentic Systems — Loop Bounding, Approval Gates, and Termination Discipline

This is Part 3 of a three-part field manual on engineering production agentic systems. Part 1 took context engineering. Part 2 took guardrails. This part takes human-in-the-loop topology and closes the series. The conviction underneath all three: production agentic systems are won on these three architectural disciplines — not on model choice.

The Opening Claim

Almost every conversation I have with teams designing agentic systems eventually arrives at the same wrong question: “Should we have a human in the loop?” The answer is yes, always. That is not the design question. The design question is “At which joints, with what context, and with what default behavior on timeout?”