Give humans and agents complete context to understand, oversee, and evolve the world's largest, most complex codebases.
Cognition | Agent Trace: Capturing the Context Graph of Code
We’re excited to join in Cursor, Cloudflare, Vercel, git-ai, OpenCode and others in support of [Agent Trace](https://agent-trace.dev/). As described in the spec, Agent Trace is an open, vendor-neutral spec for recording AI contributions alongside human authorship in version-controlled codebases.
Cody - Sourcegraph docs
Documentation for Sourcegraph, the code intelligence platform.
The emerging skillset of wielding coding agents — Beyang Liu, Sourcegraph / Amp
The Evolutionary Ecology of Software: Constraints, Innovation, and the AI Disruption
This chapter investigates the evolutionary ecology of software, focusing on the symbiotic relationship between software and innovation. An interplay between constraints, tinkering, and frequency-dependent selection drives the complex evolutionary trajectories of these socio-technological systems. Our approach integrates agent-based modeling and case studies, drawing on complex network analysis and evolutionary theory to explore how software evolves under the competing forces of novelty generation and imitation. By examining the evolution of programming languages and their impact on developer practices, we illustrate how technological artifacts co-evolve with and shape societal norms, cultural dynamics, and human interactions. This ecological perspective also informs our analysis of the emerging role of AI-driven development tools in software evolution. While large language models (LLMs) provide unprecedented access to information, their widespread adoption introduces new evolutionary pressures that may contribute to cultural stagnation, much like the decline of diversity in past software ecosystems. Understanding the evolutionary pressures introduced by AI-mediated software production is critical for anticipating broader patterns of cultural change, technological adaptation, and the future of software innovation.
Running an AI-native engineering org | Claude
How the Claude Code engineering team’s processes and structure changed once agentic coding became the default way of working.
The Definitive Guide to Understand Anything: Turning Code and Knowledge Into Graphs That Teach
You just joined a new team. The codebase is 200,000 lines of code. The original authors left a year ago. The documentation is a wiki last…
When Science Goes Agentic
In a couple of years, we will inspect AI-generated source code about as often as we inspect the assembly output of a compiler. Which is to say, far less often—outside of high-stakes and adversarial settings. The trajectory is clear: vibe coding is not a fad but a transition, a stepping stone. Debugging AI-generated code will shrink dramatically for a lot of everyday software—not because the code will be flawless, but because the feedback loops between generation, testing, and correction will tighten until human inspection becomes the bottleneck rather than the safeguard. In this respect, requiring the co-generation, with code, of mechanically verifiable formal attestations can also improve the process.
Code Galaxies Visualization
Code galaxies visualization allows you to explore huge graphs of software package managers (npm, go, ruby gems, composer, etc.)
AI Coding Agents: Adoption Trends - The JetBrains Blog
How many developers use AI coding agents (Claude Code, Codex, Cursor, JetBrains Junie, and others)? Evidence from the Developer Ecosystem Survey 2026.
Diving into Claude Code's Source Code Leak
Engineer’s Codex is a publication about real-world software engineering.
Git AI - Track AI Code all the way to production
Cross-agent observability from prompt to production. Track AI-generated code from Cursor, Claude Code, GitHub Copilot, Gemini, and more through the entire SDLC.
Agent READMEs: An Empirical Study of Context Files for Agentic Coding
Agentic coding tools receive goals written in natural language, break them down into specific tasks, and write or execute code with minimal human intervention. Central to this process are agent context files (e.g., AGENTS.md and CLAUDE.md) that provide persistent, project-level instructions. In this paper, we conduct the first large-scale empirical study of 2,303 agent context files from 1,925 repositories to characterize their structure, maintenance, and content. We find that these files are not static documentation but complex, difficultto-read artifacts that evolve like configuration code through frequent, small additions. Our content analysis of 16 instruction types shows that developers prioritize functional context, such as test procedures (75.9%), implementation details (70.8%), and architecture (68.1%). We also identify a significant gap: non-functional requirements such as security (14.8%) and performance (14.5%) are rarely specified. These findings indicate that while developers use context files to make agents functional, they provide few guardrails to ensure that agent-written code is secure or performant, highlighting the need for improved tools and practices.
AI vs human code gen report: AI code creates 1.7x more issues
We analyzed 470 open-source GitHub pull requests, using CodeRabbit’s structured issue taxonomy and found that AI generated code creates 1.7x more issues.
In software, the code documents the app. In AI, the traces do.
Discover how traces for documentation replace code as the source of truth for AI agents. Learn to debug, test, and optimize agent behavior effectively.
Code Worth Writing - Ray Myers | SSW 2026