A community-edited catalogue of programming languages designed for AI agents to author code, organised around three philosophical camps: syntactic, verification, and orchestration.
AI Agents: Key Concepts and How They Overcome LLM Limitations
An AI agent is an autonomous software entity that is often used to augment a large language model. Here's what developers need to know.
Agentic Engineering Management
To what extent AI is OK to use in software development might be debated, but in general, the idea is not a controversial one anymore. The debate rather moved on from code completion and simple PR summarizations to Agentic Engineering, where an execution loop allows an AI Agent to function
Microsoft offers devs a better way to control AI agent behavior | TechCrunch
The specification lets developer, compliance, and security teams define their own policies for agents to follow in portable policy files.
Writing effective tools for AI agents—using AI agents
Writing effective tools for AI agents—using AI agents
Three camps alike in dignity
🪧 A full catalogue of the field can now be found at agentlanguages.dev. In the last six months, programming languages designed for models rather than humans have started to appear. Work on most began entirely independently, initiated by people who had never heard of each other, but all roughly during the same window of time. […]
Agent Skills
AI coding agents take the shortest path to done, which usually means skipping the specs, tests, and reviews that make software reliable at scale. Agent Skill...
agentOS - Everything Agents Need to Run and Operate - Rivet
The complete platform for production AI agents. Stateful runtime, universal agent interface, and secure code execution. One SDK, one platform, deploy anywhere.
Trigger.dev | Build and deploy fully-managed AI agents and workflows.
Trigger.dev is the open source platform for building AI workflows in TypeScript. Long-running tasks with retries, queues, observability, and elastic scaling.
pguso/ai-agents-from-scratch
Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of function calling, memory, and ReAct patterns.
Models.dev — An open-source database of AI models
Models.dev is a comprehensive open-source database of AI model specifications, pricing, and features.
Agent Plugins
A portable package format for reusable components that extend AI agents.
How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows. While developers increasingly benefit from these coding agents, their impact on software quality remains insufficiently understood. In particular, how agentic contributions evolve across the software development lifecycle has not been thoroughly investigated. This study aims to characterize agentic pull requests (PR) in comparison to human generated PRs and to examine how their properties change across different stages of the development lifecycle. Using the AIDev dataset, we first analyze how differences in merge rates between agentic and human generated PRs vary over time. We then identify the types of development tasks where AI coding agents are predominantly applied and investigate how these task distributions evolve across development quarters. Finally, we compare a set of key characteristics of agentic and human generated PRs, focusing on their implications for software quality and their temporal dynamics. Overall, our findings provide an empirical and longitudinal perspective on the role of AI coding agents in software development, offering a more nuanced understanding of their benefits and limitations in real-world practices.
How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows. While developers increasingly benefit from these coding agents, their impact on software quality remains insufficiently understood. In particular, how agentic contributions evolve across the software development lifecycle has not been thoroughly investigated. This study aims to characterize agentic pull requests (PR) in comparison to human generated PRs and to examine how their properties change across different stages of the development lifecycle. Using the AIDev dataset, we first analyze how differences in merge rates between agentic and human generated PRs vary over time. We then identify the types of development tasks where AI coding agents are predominantly applied and investigate how these task distributions evolve across development quarters. Finally, we compare a set of key characteristics of agentic and human generated PRs, focusing on their implications for software quality and their temporal dynamics. Overall, our findings provide an empirical and longitudinal perspective on the role of AI coding agents in software development, offering a more nuanced understanding of their benefits and limitations in real-world practices.
SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
Large Language Model (LLM) agents have been widely adopted in modern software development workflows. SWE-bench [13] and related works [23, 24, 22, 25, 15] establish the task of issue resolution as a de-facto standard for assessing their capability and usefulness. In this setting, an agent is given an entire codebase, a task description (e.g., a bug report or feature request) in natural language and is instructed to produce a code patch that resolves the issue and passes the repository’s test suite. These benchmarks have been instrumental in demonstrating both the substantial potential and the persistent limitations of current models as SWE agents.