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How to Create an AI Agent: A Production Walkthrough The first agent I shipped to production failed at 3am on a Sunday. It looped on a tool call, burned through $40 in tokens before my budget alarm fired, and left a half-written draft in the database with no way to resume. That night taught me more about agent design than any framework tutorial. Since then I have built a pattern I trust enough to leave running unattended for weeks at BizFlowAI, where agents research, write, optimize and publish content without me touching them. This is that pattern, stripped down to what actually matters. Start with the job spec, not the framework Before you pick LangGraph, CrewAI, or roll your own, write the agent's job spec like you would for a junior engineer. One paragraph. What it owns, what it must never do, what "done" looks like, and which signals tell you it failed. Here is the spec for one of my production agents: The Topic Researcher owns generating a ranked list of 20 content topics per site per week. It reads from keyword_pool and search_console_perf , writes to topic_queue . It must never publish, never call paid APIs more than 8 times per run, and must finish in under 6 minutes. Done = 20 topics with score >= 0.6 and zero duplicates against the last 90 days. Failure signal = empty queue after a run, or any topic flagged by the dedupe check. If you cannot write this paragraph, do not build the agent. You will end up with a "do everything" prompt that hallucinates its way through ambiguous tasks. The job spec becomes your evaluation rubric later, so write it carefully. Rule of thumb I use : if the spec needs more than 5 tools or more than 3 decision branches, it is two agents, not one. Design the tools before you write the prompt Most agent failures I have debugged were not prompt failures. They were tool failures. The model called a tool with wrong arguments, the tool returned a 4MB JSON blob, or two tools had overlapping responsibilities and the model picked the wrong

The AI Implementation Process I Use With Every Client Most AI projects do not fail at the model. They fail in the six weeks before anyone writes a prompt, and in the six weeks after the demo lands in a Slack channel and nobody knows who owns it. I have run enough of these now (from one-off automations to multi-agent content systems running unattended) that the process has converged into something stable. This is the version I actually use. It has five phases: scoping, POC, integration, evaluation, operations. Each phase has an exit criterion. If we cannot meet the exit criterion, we do not move forward. That single rule has saved more projects than any clever architecture choice. Phase 1: Scoping (1 to 2 weeks, fixed price) Scoping ends with a written document that names the workflow being automated, the system of record it touches, the success metric in hours or dollars, the data we have access to, and the smallest possible first slice. No model is chosen yet. No code is written. If we cannot produce that document, the engagement stops here and the client keeps the document. The hardest part of scoping is resisting the urge to solve the interesting problem. Clients almost always describe the AI-shaped fantasy ("an agent that handles all support tickets") when the real opportunity is narrower and uglier ("triage tier-1 tickets that mention billing, route to the right queue, draft a reply for human approval"). The narrower version ships. The fantasy does not. I run scoping as three sessions: Workflow walkthrough. Someone who actually does the work shows me their screen for an hour. I record it. I take timestamps. The point is to find the moments where a human is doing pattern matching that an LLM can do, and to find the moments where they are doing judgment that an LLM should not do. Data audit. Where does the input live? Where does the output need to go? What is the auth story? If the data is locked inside a SaaS product with no API and no export, that is the projec

Every npm project is one malicious package away from a supply-chain breach. NPM Safety Guard catches threats that npm audit completely misses — from DPRK backdoors and typosquatted packages, to exposed API keys and AI credential theft hidden inside your node_modules. This video walks through all 23 detection layers, one by one, showing exactly what each layer catches and how it protects your project in real time. 🛡️ Intro NPM Safety Guard is the most comprehensive npm security scanner for developers. It ships as a VS Code extension (also works in Cursor and Windsurf) and a JetBrains plugin (WebStorm, IntelliJ IDEA, and all IntelliJ-based IDEs). It runs silently in the background and alerts you to supply-chain threats, malware, CVEs, and credential leaks — before they can cause damage. Layer 1 — Known Malicious Packages Checks every package in your package.json against a bundled database of documented supply-chain attacks, including DPRK/Lazarus Group backdoors, the infamous event-stream compromise, and dozens of other confirmed malicious packages. The database is also synced against a live remote feed so newly discovered threats are caught even before you update the extension. Layer 2 — CVE Vulnerabilities Queries the Google OSV.dev API for known CVEs across all your direct dependencies. No API key needed — it is completely free. Results are cached for 24 hours to minimize network calls. CVSS scores are mapped to severity levels (Critical, High, Medium, Low) so you always know exactly how serious each vulnerability is and which version fixes it. Layer 3 — Install Script Hooks Flags packages that declare preinstall, postinstall, install, or prepare npm scripts. These hooks run automatically during npm install — before any of your own code executes — making them the number one real-world vector for supply-chain malware delivery. Legitimate packages that genuinely need install scripts (like node-gyp and imagemin) are automatically whitelisted. Layer 4 — Deep Tarball AS

Originally published on bckinfo.com Redis with Docker Compose: Persistence, Security, and Production-Ready Configuration Table of Contents Why Redis Containers Lose Data RDB vs AOF: Choosing a Persistence Strategy Basic Setup with Docker Compose Full Persistence Configuration Securing Redis in Docker Setting Resource Limits Redis in a Multi-Service Stack Backing Up and Restoring a Redis Volume Common Issues and Quick Fixes Closing Notes Redis is one of the most common services to run in Docker — it's fast to spin up, lightweight, and perfect for caching, session storage, and queues. But that same simplicity hides a trap: by default, Redis in Docker stores everything in memory, and the moment a container is removed, all of that data disappears . This guide walks through setting up Redis with Docker Compose the right way — covering persistence, authentication, resource limits, and the health checks you need before putting it anywhere near production. Why Redis Containers Lose Data A Docker container is meant to be disposable. That's a feature for stateless services, but it's a liability for a database like Redis. If you start a plain Redis container without a mounted volume, here's what happens: The container writes its dataset only inside its own writable layer. docker compose down or docker rm removes that layer entirely. The next time the container starts, Redis initializes with an empty dataset. This single oversight accounts for a large share of "we lost our session data" incidents in small teams running Redis in containers for the first time. The fix is straightforward once you understand the two persistence mechanisms Redis offers. RDB vs AOF: Choosing a Persistence Strategy Redis supports two persistence models, and production setups typically combine both: RDB (Redis Database snapshots) Point-in-time snapshots of the dataset, saved at intervals you define. Fast to restore, but you can lose any writes that happened after the last snapshot. AOF (Append Only Fil

Introduction This article is not about introducing a new security tool. Nor is it an argument to replace Secret Scanners, SAST, or other existing security technologies. Instead, I want to propose an architectural concept for the AI era: How should security controls be positioned within a software development workflow where AI agents generate most of the artifacts? I call this concept the AI Security Gate . AI Is No Longer Just a Coding Assistant Generative AI has evolved far beyond code completion. Today's AI systems can already: Generate source code from requirements Write unit tests Refactor existing code Create pull requests Review code The next logical step is a development workflow where: AI implements, AI reviews, and AI iterates. In such a world, relying on humans as the final security checkpoint no longer scales. When AI-generated artifacts are reviewed by another AI, we need a security mechanism that operates independently of AI reasoning and executes every time without exception. What Is an AI Security Gate? I define an AI Security Gate as: A deterministic security control layer that validates AI-generated artifacts before they are accepted into a software development workflow. Two words in this definition are particularly important. Artifacts The scope is broader than source code. It includes any artifact produced by AI, such as: Source code Infrastructure as Code Dockerfiles Kubernetes manifests SQL scripts CI/CD workflows API specifications Deterministic An AI Reviewer performs reasoning. It may conclude: "This design is easier to maintain." An AI Security Gate does not reason. Instead, it verifies objective facts such as: An API key is embedded. A private key is committed. An organizational policy is violated. Its purpose is not to judge software quality. Its purpose is to enforce security rules consistently. Four Characteristics of an AI Security Gate I believe an AI Security Gate should satisfy four fundamental properties. 1. Deterministic Every exec

Denormalization has been the standard approach to analytical data modeling for good reason. Moving joins, lookups, and business rules out of query time and into ingestion gives you the fastest possible reads for a known access pattern. For most of the past decade, it was often the practical default for latency-sensitive analytics. Earlier columnar engines and distributed query processors could execute joins, but many workloads paid for them through higher latency, higher compute cost, spill-to-disk, or distributed coordination overhead. That constraint has loosened. Modern columnar databases with advanced join algorithms have reduced the cost of runtime joins enough that normalization is now a genuinely viable option for many analytical workloads. Denormalization still delivers faster reads, but normalization can bring operational benefits: simpler pipelines, flexible schemas, and cleaner governance. Engineers can now make the decision based on their actual workload characteristics, rather than being forced into one approach by engine limitations. This guide is a decision framework for making that choice in ClickHouse. It starts with why denormalization became the default, explains what has changed in join performance, then compares the tradeoffs on both sides so you can decide where to denormalize, where to join, and where to use ClickHouse primitives that bridge the gap. For a broader evaluation framework covering latency, concurrency, ingest throughput, SQL flexibility, and cost across real-time OLAP options, see our guide to choosing a database for real-time analytics in 2026 . For a deeper comparison of how ClickHouse executes star schema joins against Druid, Pinot, and cloud DWHs, see our star schema and fast joins guide . TL;DR Denormalization and normalization are both valid modeling strategies. The right choice depends on your workload. Denormalization's tradeoffs are primarily operational : pipeline complexity, write-path overhead, data freshness lag, back

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Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

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I want to talk about why philosophy is actually far more important than people think, especially when it comes to software engineering, systems design, and AI. When most people hear the word "philosophy," they roll their eyes. They think of abstract, circular arguments that don't matter in the real world. But true philosophy, good philosophy, is more like base mathematics. It is base physics. It is the raw understanding of the essence of a concept and how that translates into real-world action. If you don't understand the origin of a thing, you are left playing a game of perceptions. You will circle around a problem, coming up with endless rationalizations, but you will be completely unable to predict where it is going to go next. The origin of something is it fundamental nature. This origin is actually its bounding box. It dictates the absolute limits of its trajectory. Knowing this gives you predictive capability before you execute. It is the a priori knowledge that separates actual engineers from people who just copy-paste solutions. (When should and how should you copy paste, for example, 'it depends'.) The Gun Analogy and Inherent Limitations Imagine you are at a shooting range, and you point a gun downrange. As long as you point that gun in the general direction of the targets, it is not going to shoot directly behind you, or 90 degrees to the left. The inherent nature of the gun, and the velocity of the bullet, give it strict limitations. Because of those limitations, you can heavily rely on the fact that the bullet won't leave that bounding box. Therefore, shooting on a range is actually very safe. It only becomes unsafe when you turn the gun in a different direction. You have to understand that you cannot ask a tool to do more than its inherent nature allows. If you are firing an M16, it is not going to act like a guided missile and hit a target in another country hundreds of miles away. It does not have that capability. * Furthermore, a gun cannot read you

Last month, I had a problem with a popular mobile banking app in Southeast Asia. Nothing exotic. A transaction didn't go through, and my support ticket had been sitting untouched for two weeks. So I opened the app's chatbot. It greeted me warmly, asked how it could help, and then couldn't do a single useful thing. It couldn't look up my transaction. It couldn't check the status of my ticket. It couldn't tell me why my issue was unresolved. It could answer FAQ questions, and that was it. I called the hotline instead. Spent an hour navigating prompts, got bounced between menus, and every path ended the same way: "Please contact our chatbot or check your existing ticket." The system was built for deflection, not resolution. The ticket that nobody had touched for fourteen days. I gave up. And somewhere in that company's dashboard, my interaction counted as a successful AI chatbot deflection. The uncomfortable part: if you shipped a deflection-optimized bot this quarter, a customer somewhere is living this exact loop right now. Your dashboard is calling it a win. The Deflection Metric Everyone Loves (and Nobody Questions) Deflection rate measures the percentage of customer contacts handled without a human agent. It's cheap to track, easy to celebrate, and it maps directly to cost savings. Industry benchmarks citing McKinsey's 2026 service operations data put AI resolutions at $0.62 per ticket versus $7.40 for human agents. That's a 12x cost difference. Of course executives love this number. But deflection doesn't measure whether the customer's problem got solved. It measures whether the customer stopped asking. Those are very different things. This is Goodhart's Law applied to customer experience: when a measure becomes a target, it ceases to be a good measure. Deflection is cheap and easy to optimize. Resolution is hard and expensive to track. So companies optimize the proxy and stop looking at the goal. Gartner data, as reported by Forbes , confirms the gap: only 14% o

Over the past year, one concept has fundamentally changed how I think about AI applications. Not larger language models. Not better prompts. Not even AI agents. It's Model Context Protocol (MCP) . For a long time, most AI applications lived inside a closed environment. They could generate text, answer questions, or write code, but they couldn't easily interact with external systems. MCP changes that. It provides a standardized way for AI models to communicate with tools, databases, APIs, and applications. Instead of building custom integrations for every project, developers can expose capabilities through MCP servers. After experimenting with different workflows, these are five MCP servers that have had the biggest impact on how I build AI applications. 1. GitHub MCP Server If you're building software with AI, GitHub integration is one of the most valuable capabilities you can add. Imagine asking an AI assistant to: Read a repository Review pull requests Search issues Create commits Open new issues Inspect project structure Instead of manually copying files into ChatGPT, the AI can interact directly with your repository. For developers, this dramatically improves productivity. Typical workflow: Developer Request ↓ GitHub MCP Server ↓ Repository ↓ LLM ↓ Action or Response This is far more scalable than copying snippets of code into prompts. 2. Filesystem MCP Server Almost every AI workflow eventually needs access to local files. Examples include: Reading documentation Editing Markdown Creating reports Refactoring code Updating configuration files Without an MCP server, these tasks often require multiple manual steps. With a Filesystem MCP server, an AI application can safely interact with project directories. For example: Read: /docs/api.md Update: /src/routes.py Create: /reports/summary.md This makes AI assistants feel much more like development partners. 3. PostgreSQL MCP Server One limitation of traditional chatbots is that they don't know your data. Connecting an

We're upgrading Crawlberg to a new version: Crawlberg v1.0.0. It builds on the previous kreuzcrawl. It declares the public API frozen under the new project name. All technical features below shipped in v0.3.0 (2026-06-23); v1.0.0 is a stability declaration and rename, not a new feature release. The four production-facing changes most likely to require operational action: Package and env var rename - every artifact identifier has changed; see the migration table. SSRF defense is now on by default - internal crawl targets (localhost, RFC 1918, cloud metadata) will fail without CRAWLBERG_ALLOW_PRIVATE_NETWORK=1 . CrawlError::WafBlocked is now a struct variant - exhaustive match arms will not compile until updated. max_retries semantics changed - off-by-one fixed; max_retries=3 now produces exactly 3 retries. Precompiled binaries cover Linux (x86_64/aarch64), macOS (ARM64 and x86_64), and Windows x64. Homebrew bottles and Docker images on GHCR are also available. What Is Crawlberg? Crawlberg is a web crawling engine written primarily in Rust that exposes a single consistent API across 14 language runtimes. It handles HTTP transport, JavaScript rendering, robots.txt compliance, per-domain rate limiting, SSRF safety, and structured extraction. Extension points ( Frontier , RateLimiter , CrawlStore , EventEmitter , ContentFilter , WafClassifier , ProxyProvider ) are injectable traits; wire in your own frontier, storage backend, or proxy pool without forking the engine. A single scrape() call returns text, metadata, links, images, assets, JSON-LD, Open Graph tags, hreflang, favicons, headings, response headers, and clean HTML→Markdown. When a site requires JavaScript, the optional headless browser tier handles it transparently. v1.0.0 promotes v1.0.0-rc.2 and freezes the public API under the new project name. The features described in the sections below represent the platform that 1.0.0 declares stable; they shipped in v0.3.0. What v1.0.0 Declares Stable These capabilities

Windows Persistence Techniques (MITRE ATT&CK Mapped) – Complete Red Team Course Windows persistence is one of the most important topics for red teamers, malware analysts, DFIR professionals, and cybersecurity students. Understanding persistence techniques helps both attackers simulate real-world threats and defenders detect and respond to them. This article accompanies my full YouTube course, which covers Windows internals, persistence mechanisms, privilege escalation, post-exploitation concepts, and digital forensics in a controlled lab environment. 📺 Full Video What You'll Learn Windows Boot Process Windows Architecture Windows System Calls Windows Memory Management PEB & TEB Structures Windows Persistence Techniques Registry-Based Persistence DLL Hijacking Windows Services Scheduled Tasks Digital Forensics Registry Analysis Privilege Escalation Concepts Post-Exploitation Techniques MITRE ATT&CK Mapping Tools Covered Mimikatz AccessChk PowerUp PrivescCheck SharpUp RegRipper Registry Explorer Regshot SessionGopher LaZagne PSRecon Frogman Tool LogonTracer credump Course Structure Windows Internals Persistence Techniques Digital Forensics Privilege Escalation Post Exploitation MITRE ATT&CK Mapping GitHub Resources Windows Persistence Repository https://github.com/manikandantn68/window-persistence-Privilege-Escalation Frogman Tool https://github.com/manikandantn68/frogman-tool Intended Audience Cybersecurity Students SOC Analysts Blue Team Engineers Red Team Operators Malware Analysts Digital Forensics Investigators Penetration Testers Educational Disclaimer This course is intended solely for educational purposes and demonstrates techniques within an authorized lab environment. Always obtain proper permission before testing or assessing systems you do not own or administer.

The access you revoked in your last review is probably still live. I know how that sounds, but it is how most access recertification actually works. A tool generates a list, an owner clicks approve or revoke, the cycle gets marked complete, and then nothing touches the real resources. The review produces a record. The permissions stay exactly where they were. You attested to a state that was never made true. That gap bothered me for a long time, so I built something to close it and open-sourced it on AWS's aws-samples org. It is called VIGIL. The core idea A normal review answers one question: should this access still exist? The owner says no, a ticket gets filed, and maybe someone actions it next quarter. Between the decision and the change, the risk just sits there. I wanted the decision and the change to be the same step. So VIGIL does four things: It discovers resources by their owner tag and works out who actually has access to each one. It asks the owner to keep, trim, or remove that access. It applies that decision on the live resource. It records what happened in a way you can later prove. The part I care about most: scoped enforcement The lazy way to revoke someone's access to one bucket is to detach their policies. That nukes their access to everything, and it is how you cause an incident while trying to improve security. VIGIL never does that. If the access came from a bucket policy, it removes just that principal, or just the specific actions, from that bucket's policy. If the access came from the principal's own IAM policy, it adds a resource-scoped explicit Deny instead of touching shared policy, so nothing else the principal can do is affected. In practice it can remove only s3:PutObject for one principal on one bucket and leave everything else alone. If a change cannot be made safely and narrowly, it raises a ticket instead of guessing. I would rather it do nothing than do something broad. Making enforcement durable Enforcement is not a synchronous c