#CrewAI tool retry
115 related articles
·3 min
Three Costly Lessons from Building AI Agents: Schema Validation, Circuit Breakers, and Retry Strategies in Practice
Three real-world lessons from building AI Agents: schema leniency over strict validation, consecutive-failure circuit breakers, and smart retry strategies to prevent double billing.
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·3 min
One Decorator to Solve the Duplicate Tool Execution Problem in AI Agents
The idempotent-tools Python library prevents duplicate tool execution in AI Agents with a single @idempotent decorator, supporting SQLite, Redis, LangGraph, and CrewAI.
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·3 min
A Guide to Optimizing LLM Tail Latency: Request Hedging and Other Low-Cost Fixes Explained
Learn how to fix LLM tail latency (P99/P99.9) using request hedging, dynamic timeouts, and scheduling optimizations — practical low-cost solutions for production LLM apps.
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·4 min
Software Engineering in the Agentic Era: The Development Paradigm Is Being Fundamentally Reshaped
An in-depth analysis of how AI agents are reshaping software engineering paradigms—from code completion to autonomous execution—covering agentic workflows, productivity shifts, reliability challenges, and the evolving role of engineers.
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·3 min
What to Do When Your AI Coding Assistant Suddenly Crashes: A Guide to Troubleshooting Agent Loop Anomalies
AI coding assistant outputting gibberish or stuck in loops? This guide explains common LLM anomaly causes including context overflow, repetition loops, and server errors, with systematic troubleshooting steps.
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·3 min
Multi-Agent Collaboration: The New Hard Requirement in AI Engineering Interviews
Deep analysis of how Multi-Agent collaboration and Skill mechanisms are becoming core evaluation criteria for AI engineering roles, covering architecture design, high-frequency interview questions, and practical advice.
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·5 min
What Is DeepSeek Harness? Dissecting the 7 Core Modules of Agent Architecture
Deep analysis of DeepSeek Harness: not just a product, but an Agent architecture paradigm. Dissecting 7 core modules including tool calling, memory systems, and sandbox environments.
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·4 min
A2A Protocol in Practice: A Complete Breakdown of Agent-to-Agent Communication
A hands-on breakdown of the A2A (Agent2Agent) protocol through a Travel Agent and Weather Agent collaboration demo, covering Agent Card, Task state machine, SSE streaming, and more.
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·3 min
Agentic AI Meets RAG, LLM, and RL: A Complete Guide to the AI Agent Technology Stack
Deep dive into how Agentic AI integrates with RAG, LLM, and RL. Explore the agent tech stack's architecture, deployment challenges, and future trends for building production-grade AI applications.
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·3 min
Deep Dive into Row-Bot's Multi-Agent Orchestration Architecture: Parent-Child Agent Collaboration and Concurrency Control
Deep analysis of Row-Bot's multi-agent orchestration: parent-child Agent collaboration, Git worktree concurrency safety, state persistence, and fault recovery design for production AI Agent systems.
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·3 min
Loop Engineering Explained: From Agent Loops to a New AI Development Paradigm
A deep dive into Loop Engineering, covering Agent Loop think-act mechanisms, the evolution from While loops to Graph structures, and the practical value of this emerging AI development methodology.
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·3 min
Inside an AI Agent: How Intelligent Agents Think, Decide, and Act
A deep dive into AI Agent internals: from the perceive-reason-act loop, tool calling, and context management to error handling—revealing how agents truly work and their engineering challenges.
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·3 min
AI Agent Development Learning Roadmap: A Complete Three-Phase Guide from Zero to Production
A complete three-phase AI Agent development roadmap: Python basics & LLM fundamentals, five core capabilities (planning, tool use, memory, reflection, context optimization) with LangChain/LangGraph, and hands-on RAG projects.
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·3 min
Meta Open-Sources Muse Glimmer: A 30-Billion Parameter Agent Model That Runs Locally on a Single GPU
Meta open-sources Muse Glimmer, a 30B parameter agent model compressed to under 20GB via 4-bit quantization. Runs on a single RTX 4090 with 128K context, 3x speedup via D-Flash speculative decoding, and MCP tool-calling score of 75.5.
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·3 min
Advanced AI Agent Development: A Complete Practical Guide to Three Major Frameworks
A deep dive into AI Agent development covering LangChain, LangGraph, and CrewAI frameworks, from single-agent to multi-agent collaboration systems.
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·2 min
Cohesor: A Neutral Cost Control Hub for Enterprise AI Agents, Cutting Bills by 60%-90%
Cohesor is a neutral enterprise AI Agent cost control platform that helps businesses cut 60%-90% of agent bills through 50% token compression, intelligent model routing, and per-user spend governance — with zero code changes.
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·4 min
Microsoft Agent Framework in Practice: Building Enterprise-Grade AI Agents with .NET
A deep dive into Microsoft Agent Framework for building enterprise AI agents with .NET, covering tool calling, multi-agent orchestration, Qdrant RAG, and A2A, MCP, AGUI protocols.
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·2 min
Aquifer Open Source: A Traffic Smoothing Solution for Peak Shaving Bursty GPU Inference Workloads
Aquifer is an open-source traffic smoothing runtime that uses durable queue buffering and backend backpressure to solve burst traffic challenges in GPU inference services, enabling peak shaving and cost reduction.
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·3 min
The Overlooked "Boring" Infrastructure Behind Production-Grade AI Agents
Exploring the core challenges of AI Agents moving from demo to production: idempotency, approval states, retries, action ledgers, audit tables, and other critical infrastructure design patterns.
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·3 min
ScrapeOps Proxy Tester: Choose the Right Proxy for Your Scraper with Real-World Data
ScrapeOps Proxy Tester benchmarks 20+ proxy configurations against your specific target URL, measuring success rate, latency, and cost to help scrapers and AI Agents choose the optimal proxy.
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