Awesome Agentic Engineering

A curated map of the best resources for learning agentic engineering and the AI-native SDLC.

Agentic engineering is the discipline of building and shipping software by directing AI coding agents across
the development lifecycle: the engineer sets the goal, delegates to agents, and reviews and verifies what
they produce. This list collects the highest-signal resources for learning it, from coding agents and
harnesses to loop and context engineering, spec-driven development, evals, and real production case studies.
It is maintained by Fatih Koc (https://fatihkoc.net), with a bias toward resources that hold up in real
engineering practice rather than hype. Contributions are welcome. It also lives as a browsable
site at https://awesomeagenticengineering.com.

Contents

• Start here
• Foundations
• Coding agents and tools
• Books
• Courses
• Podcasts
• Blogs and newsletters
• Essays and writing
• Videos and talks
• Case studies and in practice
• Communities
• How this list is maintained

Start here

A short on-ramp: read the framing, learn to direct one agent well, then see it running in production.

• Software Is Changing (Again) - Karpathy's keynote on the Software 3.0 shift and choosing autonomy levels when delegating to AI.
• Building Effective Agents - Anthropic's guide to the vocabulary: workflows vs agents, and the composable patterns.
• Claude Code Best Practices - How to actually direct a coding agent: workflows, context files, permissions, patterns.
• 12-Factor Agents - Twelve principles for reliable, production-grade LLM software.
• Designing agentic loops - Loop engineering: goals, tools, and safety constraints so an agent can iterate toward a solution.
• Spec-Driven Development with Coding Agents - A hands-on course on the plan, implement, verify workflow with agents.
• Claude Code - A capable coding agent to start practicing with today.

Foundations

A small set of the seminal work underneath agentic coding. Background, not the whole research pile.

• Software Is Changing (Again) - Keynote framing the Software 3.0 shift and how to choose autonomy levels when delegating to AI. By Andrej Karpathy.
• Building Effective Agents - Distinguishes workflows from agents and catalogues composable patterns (prompt chaining, routing, orchestrator-workers). By Anthropic.
• ReAct: Synergizing Reasoning and Acting in Language Models - The interleaved reason-and-act loop where a model alternates thought traces with tool actions, the primitive under most coding agents. Yao et al. (2022).
• SWE-bench: Can Language Models Resolve Real-World GitHub Issues? - The benchmark of 2,294 real GitHub issues that anchors how coding-agent progress is measured. Jimenez et al. (2023).
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering - Shows a purpose-built agent-computer interface for navigating repos, editing, and running tests sharply improves performance. Yang et al. (2024).

Coding agents and tools

• Claude Code - Anthropic's agentic coding tool that runs in the terminal, IDE, and CI, understands a codebase, and executes edits, tests, and Git workflows.
• OpenAI Codex CLI - Open-source lightweight coding agent that runs locally in the terminal with sandboxed execution. By OpenAI.
• Cursor - AI code editor and agent for building software across desktop, CLI, and mobile. By Anysphere.
• Aider - Open-source terminal pair-programming agent that edits code in a local Git repository using an LLM of choice.
• Cline - Open-source autonomous coding agent available as an IDE extension, CLI, and SDK.
• Devin - Autonomous AI software engineer for long-horizon tasks like migrations, bug fixes, and PRs. By Cognition.
• OpenHands - Open-source, self-hostable platform for running coding agents and automations.
• Model Context Protocol - Open standard for connecting coding agents to external tools, data, and workflows. By Anthropic.

Books

• AI-Assisted Programming - Using AI dev tools across the lifecycle: requirements, planning, design, coding, debugging, testing, deployment. By Tom Taulli (O'Reilly).
• Beyond Vibe Coding - Moving from rapid AI prototyping to disciplined AI-assisted engineering: prompting, review, and shipping production software. By Addy Osmani (O'Reilly).
• Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond - Practices for building production software using generative AI and autonomous coding agents. By Gene Kim and Steve Yegge.
• Vibe Engineering - An end-to-end process for AI-augmented development: agentic coding, validating AI-generated code, refactoring, and team collaboration. By Tomasz Lelek and Artur Skowronski (Manning).
• AI-Assisted Coding: The Practical Guide - AI pair programming with tools like Copilot, Ollama, and Aider, covering prompts, generation, testing, and local models. By Kofler, Oggl, and Springer (Rheinwerk).
• Agentic Coding with Claude Code - Directing Claude Code as a development platform: context engineering, memory files, slash commands, hooks, MCP, and subagents. By Eden Marco (Packt).

Courses

• Claude Code: A Highly Agentic Coding Assistant - Free short course on Claude Code best practices: codebase exploration, planning, testing, refactoring, and MCP. (DeepLearning.AI and Anthropic)
• Spec-Driven Development with Coding Agents - Writing project constitutions and feature specs to guide agents through a plan, implement, verify workflow. (DeepLearning.AI and JetBrains)
• Claude Code in Action - Free course on integrating Claude Code into a workflow: tools, context, plan mode, custom commands, hooks, SDK, and MCP. (Anthropic Academy)
• Claude Code: Software Engineering with Generative AI Agents - Orchestrating Claude Code across Git worktrees with CLAUDE.md context, reusable commands, subagents, and QA patterns. By Jules White (Vanderbilt, Coursera).
• Learn Cursor - Cursor's official tutorials on AI foundations, coding with the agent, and reviewing and testing agent-generated code.

Podcasts

• The AI Native Dev - AI-native software development: coding agents, spec- and context-driven development, and running the SDLC with agents. Hosted by Guy Podjarny and Simon Maple.
• Latent Space - Technical interviews for AI engineers with recurring deep dives on coding agents and harnesses (Claude Code, Cursor, Codex, Devin). Hosted by swyx and Alessio Fanelli.
• The Pragmatic Engineer Podcast - Engineering interviews including Boris Cherny on building Claude Code and Kent Beck on TDD with agents. Hosted by Gergely Orosz.
• AI & I - Builders walking through their workflows, including shipping features with Claude Code and other agentic tools. Hosted by Dan Shipper (Every).
• Agentic Coding Today - Short-form show tracking coding agents like Claude Code and Codex: workflows, patterns, and what works versus hype.

Blogs and newsletters

• Simon Willison's Weblog - Near-daily hands-on writing on coding agents (Claude Code, Codex), model releases, and agentic engineering patterns.
• Peter Steinberger - Field notes on agentic engineering workflows, Claude Code setups, and shipping code by directing agents.
• The Pragmatic Engineer - Engineering newsletter with recurring coverage of AI coding tools, adoption data, and how teams ship with agents. By Gergely Orosz.
• Anthropic Engineering Blog - Posts on Claude Code, harness design for long-running development, parallel agents, and coding-agent evals.
• Exploring Generative AI - Ongoing Thoughtworks memo series on harness engineering, context engineering, and spec-driven development. By Birgitta Boeckeler and colleagues.
• Geoffrey Huntley - Writing on building and running coding agents, loop-based methods (the ralph loop), and software-factory workflows.

Essays and writing

• Agentic Coding Recommendations - Field-tested advice on language choice, tool speed, logging, and codebase structure that make coding agents effective. By Armin Ronacher.
• Revenge of the Junior Developer - Six overlapping waves from completions to autonomous agents, agent clusters, and agent fleets. By Steve Yegge.
• My LLM Codegen Workflow - A spec, then plan, then execute workflow for building software with LLMs on greenfield and existing code. By Harper Reed.
• Ralph Wiggum as a Software Engineer - The Ralph technique: running a coding agent in a bash loop that repeatedly feeds one prompt, with progress persisted to files and git. By Geoffrey Huntley.
• Advanced Context Engineering for Coding Agents - Intentional compaction and a research, plan, implement workflow for coding agents in large legacy codebases. By Dex Horthy.
• Not All AI-Assisted Programming Is Vibe Coding - Distinguishes unreviewed vibe coding from professional AI-assisted engineering where you review, test, and can explain the code. By Simon Willison.

Videos and talks

• Claude Code and the Evolution of Agentic Coding - How coding agents differ from autocomplete and how agentic coding workflows are evolving. By Boris Cherny (Anthropic).
• No Vibes Allowed: Solving Hard Problems in Complex Codebases - The Research, Plan, Implement workflow and intentional context compaction for coding agents in large codebases. By Dex Horthy.
• Engineering Practices That Make Coding Agents Work - Concrete practices, testing, and verification patterns for reliable results from Claude Code and Codex. By Simon Willison.
• The New Code: Specifications as the Fundamental Unit of AI-Era Programming - Argues written specs, not code, become the primary artifact when directing AI. By Sean Grove (OpenAI).
• Agentic Coding: The Future of Software Development with Agents - Hands-on experience running Claude Code: tooling, permissions, and language choices for agentic coding. By Armin Ronacher.

Case studies and in practice

• How Claude Code Is Used in Practice - Analysis of roughly 400,000 real sessions: success rates, the human and agent split between planning and execution, and how outcomes vary by expertise. By Anthropic.
• AI-Driven Refactoring in Large-Scale Migrations - Building an Aider-based agent to migrate roughly one million lines of Ember.js to React, with throughput rising from about 50 to sometimes over 1,000 lines per engineer per day. By Stefano Amorelli (Qonto).
• WhatsCode: Large-Scale GenAI Deployment at WhatsApp - 25 months of agentic coding at WhatsApp: over 3,000 accepted code changes and privacy-verification coverage rising from 15 to 53 percent. Mao et al. (Meta).
• A Grounded Take on Agentic Coding for Production - Generating over 50,000 lines of mostly AI-written code in a month: task decomposition, domain knowledge, and verification through integration tests. By Ivan Velichko.
• Agentic Coding: Things That Didn't Work - An honest post-mortem of abandoned techniques (slash commands, hooks, subagent parallelization) and why. By Armin Ronacher.
• Old and New Apps, via Modern Coding Agents - A mathematician uses a coding agent to port decades-old Java applets to JavaScript, which surfaces bugs in the original code, then builds a new visualization tool he had abandoned years earlier. By Terence Tao.
• devopsstart - A DevOps learning site written and operated by an autonomous agentic content pipeline, run by this list's maintainer. By Fatih Koc.

Communities

• Cursor Community Forum - Official forum for the Cursor coding agent: agent workflows, model comparisons, and rules configuration.
• Latent Space Discord - AI engineering community with channels for agents, evals, and coding tools, plus a weekly paper club.
• Aider Discord - Community for the Aider CLI pair-programming agent where users share configurations, model choices, and workflows.
• r/ClaudeAI - Subreddit on Claude and Claude Code: agentic coding workflows, configurations, and results.
• r/ChatGPTCoding - Tool-agnostic subreddit for AI-assisted and agentic coding (Claude Code, Cursor, Aider, and more).

Contributing

See CONTRIBUTING.md. Open a pull request with resources that clear the bar, and the
maintainer will review and merge them.

How this list is maintained

This list is human-fronted, and partly maintained by the discipline it documents. Each week a curation agent
(the weekly-curator skill) drafts a pull request: it scans for
newly-published, in-scope resources and checks the existing links for rot. The maintainer reviews and merges
every change by hand, so nothing lands without a human. The agent does the legwork; the judgment stays human.
This list is deliberately bounded: each section holds only its best entries (a per-section cap, enforced in CI), so a new resource earns its place by beating one already here.