16 results for “agentic workflow”
Related topicsagentic workflowagentic workflows
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.
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.
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.
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.
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.
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.
Aug 07
GitLost: How a GitHub AI Agent Leaked Private Repos from One Public Issue
Security researchers discovered "GitLost," a vulnerability where a GitHub AI agent was tricked by a malicious prompt in a public issue into leaking data from private repositories, highlighting a fundamental security flaw in agentic AI systems.
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.
Jul 06
HITL That Works: Make Review Faster Than the Original Task
Most human-in-the-loop (HITL) review queues fail because they are slower and more tedious than the manual work they replace. This article details four principles for designing HITL systems that humans will actually use, focusing on making review faster than creation, surfacing model uncertainty, and turning the review process into a continuous evaluation set.
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.
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.
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.
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.
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.
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.