"My Agent Refused 96 Times": Building Self-Editing Agents with Hard Failure Modes
Originally published on tamiz.pro . In the early days of shipping LLM-based agents, we optimized for output volume. If the model could not find the…
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From Prototype to Production: Hard-Won Lessons Building Multi-Agent Systems That Actually Ship
Originally published on tamiz.pro . The Gap Between Demo and Day One Every team that builds a multi-agent system starts the same way: three agents c…
From Monolithic LLMs to Autonomous Rust Agents: Building the Next-Gen Developer Stack with uv, RAGFlow, and zeroclaw
Originally published on tamiz.pro . The era of throwing everything at a single LLM call is over. Prompt engineering and RAG pipelines hit diminishin…
From Goroutines to Agents: Lessons from 1M Concurrent Threads and the New Wave of AI Engineering
Originally published on tamiz.pro . In 2021, a team running a Go-based infrastructure service hit 1 million concurrent goroutines under load. The le…
Beyond the LLM: Why RAG Checklists, Agent Observability, and Lightweight Infrastructure Are the New Developer Stack
Originally published on tamiz.pro . The excitement around Large Language Models (LLMs) has largely focused on the capabilities of the models themsel…
What Happens After the Agent Replies: Archiving Prompt History for Reproducible AI Workflows
Originally published on tamiz.pro . When a Retrieval-Augmented Generation (RAG) agent or any LLM-backed service produces an answer, the real value r…
Why Your AI Agent Doesn't Have a Reasoning Problem—It Has a Memory Problem: A Practical Guide to Production-Grade Agent State
Originally published on tamiz.pro . You've spent weeks tuning your system prompt. You've tried chain-of-thought, ReAct, and tree-of-thought promptin…
From Hype to Hard Reality: What We're Learning About Shipping AI Agents in Production
Originally published on tamiz.pro . For the better part of two years, the narrative has been seductive: build a few function calls around a reasonin…
From Hype to Production: The Harsh Reality of Shipping AI Agents Beyond the Demo
Originally published on tamiz.pro . The demo works beautifully. The agent reads the inbox, writes a draft, calls the API, and updates the database —…
From Cherry Studio to Destroylist: Mapping the New Developer Stack for AI Agents, Privacy, and Lightweight Tooling
Originally published on tamiz.pro . The era of treating Large Language Models (LLMs) as simple text-completion endpoints is over. We are currently w…
Beyond the Agent Hype: Architecting Observability, Memory, and Guardrails for Production AI Systems
Originally published on tamiz.pro . The initial wave of the Generative AI boom was defined by the "Hello World" of agents: a simple script chaining…
Why Your AI Agent Fails in Production: Bridging the Memory, Testing, and Tooling Gaps
Originally published on tamiz.pro . You spent weeks building an agentic workflow that works flawlessly on your local machine. It handles edge cases,…
The Memory Bottleneck: Why AI Agents Fail and How to Fix Them with Self-Driving Tooling
Originally published on tamiz.pro . Large Language Models (LLMs) have revolutionized software development, but when we stack them into multi-step ag…
From SGLang to OpenLogi: The Developer Shift Toward Local-First AI Infrastructure
Originally published on tamiz.pro . The current wave of Large Language Model (LLM) adoption has created a bifurcation in the engineering landscape.…
The Solo Founder Simulation: Lessons from Letting an AI Agent Run a SaaS While I Audited Its Human-Like Mistakes
Originally published on tamiz.pro . I spent six weeks delegating the operational backbone of my SaaS to a multi-agent system. The goal was to test t…
Planning Over Execution: Lessons from 157 Agent Runs and the Rise of Orca-Style Agent Fleets
Originally published on tamiz.pro . The field of AI agents has moved rapidly from single-model executors to complex multi-agent orchestration. But a…
The AI Agent Reality Check: Why MCP Backdoors Fail in Production
Originally published on tamiz.pro . You built a prototype. The model connected to the server, fetched a tool, and the loop closed perfectly. But whe…
Why Your AI Agent Can't Execute Its Own Plan: Bridging the Gap Between Local LLM Intelligence and Real-World Software Reliability
Originally published on tamiz.pro . You prompt your agent to orchestrate a multi-step workflow. It generates a beautifully reasoned plan. Then it fa…
Building a Private Agentic OS with Local LLMs: Lessons from Eliza, Hister, and the Planning Problem
Originally published on tamiz.pro . Introduction We are witnessing a fundamental shift in software architecture: the transition from passive APIs to…
Why Your AI Agent Architecture Is Failing: Bridging Security Holes, Planning Failures, and Real-World Dev Workflows
Originally published on tamiz.pro . The promise of autonomous AI agents transforming software development is tantalizing. Imagine agents drafting co…
The Observability Crisis: Why OTel Alone Fails for AI and How to Build a Resilient Pipeline
Originally published on tamiz.pro . Observability in software engineering has long been the domain of metrics, traces, and logs. OpenTelemetry (OTel…
Beyond the Hype: A Developer’s Critical Audit of Real-World AI Agents from OmniRoute to Eliza
Originally published on tamiz.pro . The term "AI Agent" has become the default marketing suffix for nearly everything LLM-connected today. But for a…
The AI Assistant That Lied: Why Self-Correcting Agents Are the Only Path to Trustworthy Production LLMs
Originally published on tamiz.pro . Your production LLM assistant just told a customer their refund was processed. It wasn't. The customer never rec…
Why AI Agent Runtimes Need a 'Constitution': Lessons from Ironclaw and the Rise of Policy-First Autonomous Systems
Originally published on tamiz.pro . Introduction Autonomous AI agents are transitioning from research prototypes to production-critical systems. As…
Validating AI Memory: How to Benchmark Agent Memory Systems Without the Hype
Originally published on tamiz.pro . 1. Introduction: The Memory Hype Cycle AI agent memory has become the latest battleground for vendor differentia…
Beyond the Demo: Building Production-Ready AI Agents — A Guide to Benchmarking, Cost Optimization, and Tooling in 2026
Originally published on tamiz.pro . Most AI agents ship from a notebook, impress in a demo, and quietly fail in production. The gap isn't intelligen…
The 2026 Toolchain Revolution: From Postman to CLI, Code to Prompts — What Stays and What Goes
Originally published on tamiz.pro . The developer toolchain isn't evolving—it's being rewritten. By 2026, two seismic shifts are reshaping daily wor…
The Trust Problem in AI Agents: Building Architecture-First Gates Before Autonomous Code Runs Wild
Originally published on tamiz.pro . You've felt it. The rush of watching an AI agent scaffold a full CRUD service in minutes. The sinking dread when…
From Lab to Production: Securing Local LLMs and AI Agents with Self-Hosted Infrastructure and GitOps Guardrails
Originally published on tamiz.pro . You've fine-tuned your model. It runs on your GPU server. Prompt injections are a lab curiosity. But the moment…
Beyond the Prompt: Building Unhackable AI Agents — Lessons from GitHub's Top Security & Gateway Repos
Originally published on tamiz.pro . The AI agent is no longer a chatbot that reads and writes. It connects to APIs, executes code, accesses database…