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llm Archives – TechEmpower

Evals for Agentic Loop Applications – TechEmpower What if the Repository Replaced Your Wiki (and Agents Maintained it) – TechEmpower Agentic Coding in Practice Product meets Engineering in the AI Era Building Reliable Autonomous Agentic AI Announcing TechEmpower’s AI Developer Bootcamp Real-time Monitoring of LLM-Based Applications
AI Coding Assistants Update
Tony Karrer · 2025-09-16 · via llm Archives – TechEmpower

The conversation around AI coding assistants keeps speeding up, and we are hearing the following questions from technology leaders:

  • Which flavor do we bet on—fully-agentic tools (Claude Code, Devin) or IDE plug-ins (Cursor, JetBrains AI Assistant, Copilot)?
  • How do we evaluate these tools?
  • How do we effectively roll out these tools?

At the top level, I think about:

  • Agentic engines are happy running end-to-end loops: edit files, run tests, open pull requests. They’re great for plumbing work, bulk migrations, and onboarding new engineers to a massive repo.
  • IDE assistants excel at tight feedback loops: completions, inline explanations, commit-message suggestions. They feel safer because they rarely touch the filesystem.

Here’s a pretty good roundup:

The Best AI Coding Tools, Workflows & LLMs for June 2025.

Most teams I work with end up running a hybrid—agents for the heavy lifting, IDE helpers for day-to-day quick work items.

Whichever path you take, the practices you use matter the most.

Some examples to get you started:

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