15 results for “agent architecture”
Related topicsagent architecture
Apr 16
ReAct's Loop: The Simple Pattern Behind Every Modern AI Agent
The ReAct framework, introduced in a 2022 paper, defined the thought-action-observation loop that underpins nearly all modern AI agents. This article breaks down the pattern, explains why it works, and covers the failure modes that developers still encounter today.
Jan 13
Codex-Max and the $100+ Mistake 24 Hours In
GPT-5.1-Codex-Max can code autonomously for over 24 hours, but this power introduces new risks. This article explores the economics of long-horizon tasks and how to architect systems that prevent costly, deep-rooted errors.
Nov 26
Claude Opus 4.5 at $5/Mtok: When to Upgrade Your Agent's Brain
Anthropic's Claude Opus 4.5, released November 24, 2025, changes the economics of using frontier models. This architectural review explores how its $5/$25 per million token price point forces a redesign of model routing logic, especially for complex agentic workflows.
Jul 29
2.8 Trillion Parameters, Free to Download: Inside Moonshot AI's Kimi K3
Kimi K3 packs 2.8 trillion parameters but activates only 104 billion per token — and Moonshot AI put the full weights on Hugging Face eleven days after announcing it.
Dec 09
Opus 4.5 vs Gemini 3 Pro: Which Model for Which Workload
Released six days apart in November 2025, Google's Gemini 3 Pro and Anthropic's Claude Opus 4.5 are good at different things. A comparison on the axes that actually decide a stack: coding and agentic work, context handling, cost per unit of work, and provider concentration risk.
Feb 24
Claude's 1M Tokens & 90% Recall: What It Solves, What It Doesn't
Anthropic's Claude Opus 4.6 offers a 1M token context window, but its real value lies in its claimed 90% recall. This article explores when this massive context replaces RAG and when it's an expensive distraction.
Jan 06
The $12 Agent Task: Why Production AI Needs a Harness, Not a Loop
Most AI agent demos are built on a simple loop that is too expensive and unreliable for production. Real-world systems use a "harness" architecture with structured workflows, checkpoints, and budgets to create agents that can survive contact with reality.
Apr 02
The 76% Mistake: Why AI Build vs. Buy Is a Layer-by-Layer Choice
The "build vs. buy" decision for LLM applications is no longer a single choice. This article presents a framework for analyzing the AI stack layer by layer—from the model to evaluation—to avoid common pitfalls and focus engineering effort where it creates the most value.
Aug 10
LLM System Design Architecture: The End-to-End Guide to Building Production AI
A comprehensive guide to end-to-end LLM system design architecture, covering the core patterns like RAG, vector databases, and agentic workflows required to ship production-grade AI applications.
Aug 25
Debate and Decompose: When a Second Agent Is Worth the Cost
Multi-agent systems add cost and complexity, but are justified for specific use cases like independent verification (debate) or breaking down tasks that exceed a single model's context window (decomposition). This article provides a framework for deciding when to add a second agent.
Apr 30
The Trillion-Parameter Tipping Point: Open-Weight AI Is Now a C-Suite Decision
As of April 2026, open-weight models with over a trillion parameters rival proprietary APIs in capability, forcing a new strategic decision for enterprises. This article breaks down the choice across four critical axes: data control, capability, cost, and the upgrade cycle.
Mar 26
Role Confusion: The 61% Attack Success Rate Exposing LLM's Core Flaw
Prompt injection isn't just "tricking an AI"; it's a classic authorization vulnerability. New research shows a 61% attack success rate by exploiting 'role confusion,' where models grant authority based on writing style, not source, demanding a shift to architectural security patterns.
Jan 19
The 100x Cost Gap: When to Replace Your LLM With a Simple Rule
A single powerful LLM is often the most expensive and least reliable way to answer simple questions. This article explains how to build a routing layer that uses deterministic rules for simple queries, saving money and improving reliability, while reserving expensive models for the complex tasks they were built for.
Jul 28
Kimi K3: a 2.8-trillion-parameter open model that bets on linear attention
Moonshot's new open-weight flagship isn't chasing a leaderboard — it's a bet that a cheap million-token context changes what we build. What actually changed under the hood, how to run it, and where it sits against the open-weight frontier.
Dec 23
The GenAI Divide: Why 95% of Enterprise AI Projects Delivered No Value
In 2025, enterprises spent $37 billion on generative AI, yet a stark "GenAI Divide" emerged. A landmark MIT study found 95% of projects failed to deliver any business value, revealing that success depends not on model quality, but on organizational strategy.