ndrstnd: understand what your coding agent actually did

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.

Sourcegraph — Code Understanding, Oversight and Evolution

Give humans and agents complete context to understand, oversee, and evolve the world's largest, most complex codebases.

Agents Done Right: A Framework Vision for 2026

Agents choke on context, loop on failures, and dump walls of code for review. It's time to rethink the architecture.

Claude Code, Codex and Agentic Coding #8

When I started this series, everyone was going crazy for coding agents.

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.

Prime Agent: A self-improving RLM agent

Prime Agent is our open-source, self-improving coding harness built around two abstractions: the Recursive Language Model (RLM) and the Continual Harness. With Opus 5, it achieves 95.5% on ARC-AGI-3, surpassing the reported human expert baseline.

Agentic Engineering Patterns - Simon Willison's Weblog

Patterns for getting the best results out of coding agents like Claude Code and OpenAI Codex. See my introduction for more on this project.

Agent Trace

A standard format for tracking AI-generated code.

The Anatomy of an Agent Harness

Learn how agent harnesses transform AI models into autonomous work engines. Explore core components: filesystems, sandboxes, and memory.

The 80% Problem in Agentic Coding

Managing comprehension debt when leaning on AI to code

Does Code Cleanliness Affect Coding Agents? A Controlled Minimal-Pair Study

As autonomous coding agents see rapid adoption, their evaluation has primarily focused on task completion rates holding the target codebase fixed. This leaves a critical question unanswered: does the structural and stylistic quality, or ``cleanliness'' of the underlying code affect an agent's ability to navigate and modify it? To isolate the effect of code cleanliness from agent capability, we introduce an evaluation protocol built around minimal pairs: repositories that match on architecture, dependencies, and external behaviour, but differ on static-analysis rule violations and cognitive complexity. The pairs are constructed in both directions, by agent pipelines that either degrade a clean repository or clean a messy one. We author 33 tasks across six such pairs, evaluated through hidden tests at the application's public surface. Across 660 trials with Claude Code, code cleanliness does not change the agent's pass rate. However, it substantially alters the agent's operational footprint: agents working on cleaner code use 7 to 8% fewer tokens and reduce file revisitations by 34%. Our findings suggest that traditional maintainability principles remain highly relevant in the era of AI-driven development, shaping the computational cost and navigational efficiency of coding agents. Code cleanliness joins model choice, harness, and prompting as a factor that materially affects agent behaviours.

Dii familiares

Musings on agents and AI and a little signposting

How coding agents work - Agentic Engineering Patterns

How coding agents work - Agentic Engineering Patterns

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.

Deep-Research Agents Can Be Poisoned via User-Generated Content

Deep-research agents, i.e., systems that rely on multi-agent pipelines to iteratively retrieve, synthesize, and cite Web content in order to produce structured reports, are rapidly replacing...