10 results for “agent chain”
Related topicsagent chain
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 12
Prompts Are Production Code: Version Them Like It
A 2025 industry survey found that 30-40% of AI development time is spent on prompt engineering. This article explains why treating prompts as versioned, deployable artifacts is the key to reclaiming that time, improving reliability, and shipping faster.
Dec 04
Your Retry Loop Is a Time Bomb: From a $50 API Call to a $5,000 Bill
A simple retry-on-failure wrapper seems harmless, but in AI agent systems, it can lead to silent cost overruns and cascading failures. This article explains why, and how to build resilient, cost-aware error handling using techniques like total budget caps and structured escalation ladders.
Feb 03
How to Log Your LLM: Unlock 99% Automation With Better Data
Traditional logging can't tell you if your LLM is getting worse. This article details the essential fields to log—from costs to verdicts—to debug silent failures, track quality, and automate up to 99% of your AI-driven workflows.
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.
Jun 30
Your AI Is Slow: A Latency Budgeting Guide for Sub-Second Agents
Stop thinking about AI latency as a single number. Treat it as a budget to be spent across retrieval, generation, and tool use, and learn how in-process controls can shave 50ms or more from every user interaction.
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.
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.
Jun 11
Stop Fine-Tuning Your LLM: Why Retrieval Is Cheaper, Faster & More Reliable
Developers often try to add knowledge to a large language model by fine-tuning it, a costly and often ineffective approach. A better strategy follows a clear hierarchy: start with the prompt, then use Retrieval-Augmented Generation (RAG) for facts, and only use fine-tuning to change the model's core behavior.