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AI Agents

AI agent development tutorials, best practices, and production deployment guides for building reliable autonomous systems.

25 posts

AI Agents for Legacy Code Migration: How Effective Are They?

Real-world assessment of using AI agents for legacy code migration. What works, what fails, and the hybrid approach that delivers the best results.

Best Practices for Agentic Workflows in IDEs

Configure your IDE for maximum productivity with AI coding agents. Covers Cursor, VS Code extensions, workspace setup, and agent-friendly project structures.

Best Prompts for Code Refactoring with AI Agents

Copy-paste refactoring prompts that produce clean, maintainable code. Templates for extracting functions, simplifying logic, and improving code architecture.

Best Tools and Combos for AI Agents in 2026

Compare the top AI agent toolkits: Vercel AI SDK, LangChain, CrewAI, Composio, and more. Find the right stack for your agent project.

Best Way to Review and Merge Agent-Generated Code

Code review strategies specifically for AI-generated code. What to look for, common AI coding patterns, and how to maintain quality standards at scale.

Best Way to Use Claude and Cursor for Building Full Applications

A practical guide to building complete applications with AI coding agents. Project setup, agent configuration, and workflow patterns that actually work.

CLI Agents vs IDE-Integrated Agents: A Complete Comparison

Compare command-line AI agents (Claude Code, Aider) with IDE-integrated ones (Cursor, Copilot). Pros, cons, and when to use each approach.

Common Mistakes When Scaling AI Coding Agents

Avoid the pitfalls that teams encounter when moving from single-developer agent use to team-wide adoption. Covers governance, cost control, and quality assurance.

How Do AI Coding Agents Like Claude Code and Cursor Actually Work?

Understand the architecture behind AI coding agents. How they plan, execute, verify code, and manage context across multi-file projects.

How to Build Verification Tools and Scripts for AI Agents

Create custom verification layers that validate agent-generated code automatically. Covers test harnesses, assertion frameworks, and CI integration patterns.

How to Build Your First AI Coding Agent from Scratch

Step-by-step tutorial for building a functional AI coding agent. Covers tool use, planning loops, code execution, and verification — no frameworks required.

How to Debug Agent Loops and Infinite Reasoning

Fix the frustrating problem of AI agents stuck in reasoning loops. Diagnosis techniques, loop detection, and architectural patterns to prevent them.

How to Give AI Agents Tools Without Context Pollution

Manage tool definitions efficiently so agents stay focused. Techniques for lazy loading, tool selection, and preventing context window bloat from tool schemas.

How to Handle Long-Running Agent Tasks Without Failures

Keep AI agents reliable during extended operations. Covers checkpointing, state persistence, timeout handling, and graceful recovery from interruptions.

How to Integrate AI Agents with Existing Codebases

Onboard AI coding agents to legacy and existing projects. Covers codebase documentation, AGENTS.md files, architectural context, and incremental adoption.

How to Make AI Agents Self-Verify Their Code

Build verification loops that catch AI coding errors before they reach production. Includes test generation, linting integration, and assertion-based validation.

How to Manage Context in Multi-File Agent Edits

Keep AI agents coherent across multi-file changes. Techniques for context windowing, file prioritization, and maintaining consistency across large codebases.

How to Recover AI Agents from Sandbox and Network Failures

Build resilient AI agent systems that gracefully handle sandbox crashes, network timeouts, and API failures. Includes retry strategies and state recovery patterns.

How to Teach AI Agents Domain-Specific Knowledge

Inject specialized knowledge into AI coding agents without fine-tuning. Covers knowledge files, example-based learning, and domain-specific verification.

How to Use AI Agents for Test Generation and CI Integration

Automate test writing with AI agents. Covers unit tests, integration tests, edge case generation, and plugging agent-generated tests into your CI pipeline.

Plan Mode vs Direct Execution in Coding Agents: When to Use Each

Compare planning-first versus direct-execution agent strategies. Learn when each approach produces better code and how to configure your agent for each.

Tips for Using Sub-Agents and Multi-Agent Systems

Design effective multi-agent architectures for complex coding tasks. Covers orchestration patterns, communication protocols, and result aggregation strategies.

When Should I Use Human-in-the-Loop vs Full Autonomy for AI Agents?

Decision framework for choosing between supervised and autonomous AI agent operation. Risk assessment, trust calibration, and progressive autonomy strategies.

Why Do Coding Agents Fail on Complex Projects? Root Causes and Fixes

The top 10 reasons AI coding agents break down on real-world projects. Diagnosis and solutions for context overflow, hallucinated APIs, and planning failures.

Why Won't My Coding Agent Use LLMs Proactively in Code?

Debug the common issue of agents that write code without leveraging AI capabilities. Covers tool registration, prompt design, and confidence thresholds.