Agentic AI Tools & Resources for Senior Developers

A curated guide to tools, concepts, and best practices for AI-assisted coding

calendar Mar 31, 2026 by Matt Eland  · 8 min read
ai agents copilot claude productivity

This article was originally intended as an internal guide at Leading EDJE to help our consultants be more effective with agentic coding as they use AI responsibly to help their client organizations achieve good results. While we were developing it, we realized this piece would be helpful not just to folks at Leading EDJE but the community at large so we turned it into a blog post.

AI Disclaimer: Unlike our other pieces of content, some parts of this piece were generated using AI. Specifically, I used Claude Code in research mode to compile a master list of recent AI resources that would be fit for our consulting team. This list was summarized in bullet points (some of which are included in this article) and then I went through and reviewed the content article by article and filtered the list down to only the most relevant content for our team. While this is different from the standard approach we use on our blog, we feel the result is still helpful for the community, provided we disclose how it was collected.

Feel free to read this article from beginning to end or to skim ahead to the AI concept or tool that's most relevant to your interests.

What is Agentic AI Development?

We define agentic AI development as sort of the software engineer's equivalent of "vibe coding". It's taking an engineering mindset and set of responsibilities, concerns, and your array of technical knowledge and using it to direct AI tools in effective ways that drive your organization and its goals forward.

While vibe coding is about building as quickly as possible and not necessarily needing to understand what's even going on under the hood, agentic AI development takes your engineering mindset and applies it to interacting with AI agents in ways that deliberately give them the right context about your codebase to fulfill their requests - and have a proper attitude towards safeguarding production and your organization's quality against the limitations of AI.

In this context, the engineer taking advantage of agentic AI development tools can do a little more each day without needing to stop thinking about how the system should work, what the code is doing, or how to accurately deliver features and fixes in a sustainable manner.

Resources on Agentic AI Development Concepts

This section contains a list of helpful resources on general concepts of working with AI agents for coding tasks.

  • Agentic Coding vs Vibe Coding — A great overview of the philosophy of agentic coding (vs vibe coding) and workflows for working successfully with AI tools. Other parts of their site have very useful information as well.

  • MorphLLM and Faros.AI have good sets of benchmarks around selecting different models for different tasks. Although models will continue to be released, it is likely the same families of models will continue the same capability specializations over time.

  • Effective Context Engineering for AI Agents — Anthropic Engineering — The successor concept to "prompt engineering." Covers how to manage the full token budget across long-running agent sessions — memory layers, compaction strategies, and sub-agent architectures. This is a critically important concept in working with AI Agents and governs how effective these tools will be.

  • Model Context Protocol (MCP) Understanding Risks and Controls — Provides an overview of what an MCP server is, how it's useful, and the risks it poses.

  • Build an MCP Server — Official Tutorial — The canonical first MCP server (weather tools). Available in Python, TypeScript, Java, Kotlin, C#, and Ruby. Under one hour.

  • Writing Effective Tools for Agents — Anthropic Engineering — How to design tools that agents can actually use well. Namespacing, consolidation, eval-driven development. This is very valuable if you design tools for MCP servers or for direct consumption by libraries like Microsoft Agent Framework.

  • Building Effective AI Agents — Anthropic — The foundational mental model for the entire space. Defines five composable patterns: prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer. This is long, but great for understanding components of AI agent evaluation.

Tool-Based Guides

Pick the tool(s) your team is actually using and start here.

If you're not sure which tools to use, we recommend AI Coding Agents & IDEs: The Complete 2026 Comparison as it contains a good comparison of different agent-enabled IDEs.

GitHub Copilot

GitHub Copilot tends to be a default first choice by organizations. It's a solid development experience with defaults that err on the side of protecting your organization and its data (assuming you turn off data sharing in your Copilot settings on GitHub or your organization plan disables it automatically).

My personal experience with Copilot is that I can do a lot with it, but I have to either manually approve a lot of choices or switch to an AI-based approval process that's more aggressive than I'd like (including answering question prompts when the agent is unsure). Copilot's allowlist capabilities are less intelligent than other IDEs such as Cursor. This being said, Copilot is a great starting point and I'm able to be very productive when working with clients who use it.

Here are some helpful guides and resources for Copilot:

Claude Code

When I talk with devs in the community, a large number of them use or have used Claude Code as their default choice for agentic development, particularly on projects unrelated to their primary jobs. Claude features a CLI-based user interface, extensions to most popular IDEs for a more visual chat experience, and a standalone desktop application. Claude is a capable and powerful choice for development, but may require some extra digging to configure it versus some of its competitors. Still, it is capable and efficient at what it does.

Here are some helpful resources for working effectively with Claude:

Cursor

Cursor is currently my favorite of the agentic AI toolsets as it works very effectively with indexing your documents, is constantly evolving, and supports a rich set of ways of injecting context into your conversations and controlling the behavior of the AI agent. I've now run somewhere around ten workshops on Cursor as a consultant at Leading EDJE as part of our initiative to train our clients in emerging AI trends and it's always fun to see people's faces light up when Cursor finds indexed documents or code or when they create their first skill or agent.

Cursor's pricing model is a little different than many of its competitors as it runs off of a monthly limit versus a recycling weekly limit, but it's an extremely powerful IDE with a lot of customization options.

Here are some of our recommended resources for Cursor:

OpenAI Codex

While I've not had a chance to play with Codex yet, I have coworkers who swear by it and its efficiency with cost/token economy. It's definitely on my list to check out this year.

Check out some current resources on Codex here:

Conclusion

There are a lot of tools, concepts and resources out there and it can feel overwhelming at how quickly everything is changing, but this is our current survey of content that we feel at Leading EDJE is most valuable for our developers and developers at our client organizations who want to get up to speed quickly.

If you think we missed something important, we probably did! This stuff changes all the time and we'd love to hear from you on socials on new and emerging concepts and agentic toolsets built for developers.

Best of luck in this brave new world, and happy agentic coding.