"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…
Latest News
⚑ Report a ProblemTech news from the best sources
All topics
- игры
AI
Gear
News
Tech
agents
ai
api
architecture
automation
beginners
career
database
devops
javascript
llm
machinelearning
mcp
opensource
performance
productivity
programming
python
react
security
showdev
testing
tutorial
typescript
webdev
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…